# Neural Machine Translation

## Pre-trained models

Description | Dataset | Model | Test set(s)
---|---|---|---
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2)
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2)
Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2)
Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2)
Transformer <br> ([Edunov et al., 2018](https://arxiv.org/abs/1808.09381); WMT'18 winner) | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz) | See NOTE in the archive

## Example usage (torch.hub)

Interactive generation via PyTorch Hub:
```
>>> import torch
>>> en2de = torch.hub.load(
...   'pytorch/fairseq',
...   'transformer',
...   model_name_or_path='transformer.wmt16.en-de',
...   data_name_or_path='.',
...   tokenizer='moses',
...   aggressive_dash_splits=True,
...   bpe='subword_nmt',
... )
>>> print(en2de.models[0].__class__)
<class 'fairseq.models.transformer.TransformerModel'>
>>> print(en2de.generate('Hello world!'))
Hallo Welt!
```

Available models are listed in the ``hub_models()`` method in each model file, for example:
[transformer.py](https://github.com/pytorch/fairseq/blob/master/fairseq/models/transformer.py).

## 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)
```

## Preprocessing

These scripts provide an example of pre-processing data for the NMT task.

### prepare-iwslt14.sh

Provides an example of pre-processing for IWSLT'14 German to English translation task: ["Report on the 11th IWSLT evaluation campaign" by Cettolo et al.](http://workshop2014.iwslt.org/downloads/proceeding.pdf)

Example usage:
```
$ cd examples/translation/
$ bash prepare-iwslt14.sh
$ cd ../..

# Binarize the dataset:
$ 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

# Train the model (better for a single GPU setup):
$ mkdir -p checkpoints/fconv
$ CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \
  --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \
  --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
  --lr-scheduler fixed --force-anneal 200 \
  --arch fconv_iwslt_de_en --save-dir checkpoints/fconv

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

```

To train transformer model on IWSLT'14 German to English:
```
# Preparation steps are the same as for fconv model.

# Train the model (better for a single GPU setup):
$ mkdir -p checkpoints/transformer
$ CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \
  -a transformer_iwslt_de_en --optimizer adam --lr 0.0005 -s de -t en \
  --label-smoothing 0.1 --dropout 0.3 --max-tokens 4000 \
  --min-lr '1e-09' --lr-scheduler inverse_sqrt --weight-decay 0.0001 \
  --criterion label_smoothed_cross_entropy --max-update 50000 \
  --warmup-updates 4000 --warmup-init-lr '1e-07' \
  --adam-betas '(0.9, 0.98)' --save-dir checkpoints/transformer

# Average 10 latest checkpoints:
$ python scripts/average_checkpoints.py --inputs checkpoints/transformer \
   --num-epoch-checkpoints 10 --output checkpoints/transformer/model.pt

# Generate:
$ fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/transformer/model.pt \
  --batch-size 128 --beam 5 --remove-bpe

```

### prepare-wmt14en2de.sh

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)](https://arxiv.org/abs/1706.03762), but with 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)](https://arxiv.org/abs/1705.03122) paper, please use the `--icml17` option.

```
$ bash prepare-wmt14en2de.sh --icml17
```

Example usage:

```
$ cd examples/translation/
$ bash prepare-wmt14en2de.sh
$ 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

# Train the model:
# If it runs out of memory, try to set --max-tokens 1500 instead
$ mkdir -p checkpoints/fconv_wmt_en_de
$ fairseq-train data-bin/wmt17_en_de \
  --lr 0.5 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \
  --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
  --lr-scheduler fixed --force-anneal 50 \
  --arch fconv_wmt_en_de --save-dir checkpoints/fconv_wmt_en_de

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

```

### prepare-wmt14en2fr.sh

Provides an example of pre-processing for the WMT'14 English to French translation task.

Example usage:

```
$ 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

# Train the model:
# If it runs out of memory, try to set --max-tokens 1000 instead
$ mkdir -p checkpoints/fconv_wmt_en_fr
$ fairseq-train data-bin/wmt14_en_fr \
  --lr 0.5 --clip-norm 0.1 --dropout 0.1 --max-tokens 3000 \
  --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
  --lr-scheduler fixed --force-anneal 50 \
  --arch fconv_wmt_en_fr --save-dir checkpoints/fconv_wmt_en_fr

# Generate:
$ 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 interactive.py 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/valid.bpe.de-en \
  --joined-dictionary \
  --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/valid.bpe.fr-en \
  --joined-dictionary --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=no_c10d \
  --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 --min-lr '1e-09' \
  --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 --source-lang ${SRC} --target-lang en \
      --path checkpoints/multilingual_transformer/checkpoint_best.pt \
      --buffer 2000 --batch-size 128 \
      --beam 5 --remove-bpe=sentencepiece \
  > iwslt17.test.${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.
