Binary the dataset.
DATA_PATH=/path/to/data/file
DATA_BIN=/path/to/save/data-bin
SRC=
TGT=
python preprocess.py -s $SRC -t $TGT \
--trainpref $DATA_PATH/train \
--validpref $DATA_PATH/valid \
--destdir $DDATA_BIN \
--output-format binary \
DATA_BIN=
SAVE_FILE=
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6 python trian.py -data $DATA_BIN \
-s $SRC -t $TGT \
--lr 0.0005 --min-lr 1e-09 \
--weight-decay 0 --clip-norm 0.0 \
--dropout 0.3 \
--max-tokens 4500 \
--arch transformer \
--optimizer adam --adam-betas '(0.9, 0.98)' \
--warmup-updates 4000 \
--criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
--save-dir $SAVE_FILE
NOTICES:
- if
- A PyTorch installation
- For training new models, you'll also need an NVIDIA GPU and NCCL
- Python version 3.6
Currently fairseq requires PyTorch version >= 0.4.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.
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.
After PyTorch is installed, you can install fairseq with:
pip install -r requirements.txt
python setup.py build
python setup.py develop
The following tutorial is for machine translation.
For an example of how to use Fairseq for other tasks, such as language modeling, please see the examples/ directory.
Fairseq contains example pre-processing scripts for several translation datasets: IWSLT 2014 (German-English), WMT 2014 (English-French) and WMT 2014 (English-German). To pre-process and binarize the IWSLT dataset:
$ cd examples/translation/
$ bash prepare-iwslt14.sh
$ cd ../..
$ TEXT=examples/translation/iwslt14.tokenized.de-en
$ python preprocess.py --source-lang de --target-lang en \
--trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
--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.
Use python train.py to train a new model.
Here a few example settings that work well for the IWSLT 2014 dataset:
$ mkdir -p checkpoints/fconv
$ CUDA_VISIBLE_DEVICES=0 python train.py data-bin/iwslt14.tokenized.de-en \
--lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \
--arch fconv_iwslt_de_en --save-dir checkpoints/fconv
By default, python train.py will use all available GPUs on your machine.
Use the CUDA_VISIBLE_DEVICES environment variable to select specific GPUs and/or to change the number of GPU devices that will be used.
Also note that the batch size is specified in terms of the maximum number of tokens per batch (--max-tokens).
You may need to use a smaller value depending on the available GPU memory on your system.
Once your model is trained, you can generate translations using python generate.py (for binarized data) or python interactive.py (for raw text):
$ python generate.py data-bin/iwslt14.tokenized.de-en \
--path checkpoints/fconv/checkpoint_best.pt \
--batch-size 128 --beam 5
| [de] dictionary: 35475 types
| [en] dictionary: 24739 types
| data-bin/iwslt14.tokenized.de-en test 6750 examples
| model fconv
| loaded checkpoint trainings/fconv/checkpoint_best.pt
S-721 danke .
T-721 thank you .
...
To generate translations with only a CPU, use the --cpu flag.
BPE continuation markers can be removed with the --remove-bpe flag.
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:
$ curl https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf - -C data-bin
$ curl https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.newstest2014.tar.bz2 | tar xvjf - -C data-bin
$ python generate.py 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)
# Scoring with score.py:
$ grep ^H /tmp/gen.out | cut -f3- > /tmp/gen.out.sys
$ grep ^T /tmp/gen.out | cut -f2- > /tmp/gen.out.ref
$ python score.py --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)
The --update-freq option can be used to accumulate gradients from multiple mini-batches and delay updating,
creating a larger effective batch size.
Delayed updates can also improve training speed by reducing inter-GPU communication costs and by saving idle time caused by variance in workload across GPUs.
See Ott et al. (2018) for more details.
To train on a single GPU with an effective batch size that is equivalent to training on 8 GPUs:
CUDA_VISIBLE_DEVICES=0 python train.py --update-freq 8 (...)
Note: FP16 training requires a Volta GPU and CUDA 9.1 or greater
Recent GPUs enable efficient half precision floating point computation, e.g., using Nvidia Tensor Cores.
Fairseq supports FP16 training with the --fp16 flag:
python train.py --fp16 (...)
Distributed training in fairseq is implemented on top of torch.distributed. Training begins by launching one worker process per GPU. These workers discover each other via a unique host and port (required) that can be used to establish an initial connection. Additionally, each worker has a rank, that is a unique number from 0 to n-1 where n is the total number of GPUs.
If you run on a cluster managed by SLURM you can train a large English-French model on the WMT 2014 dataset on 16 nodes with 8 GPUs each (in total 128 GPUs) using this command:
$ DATA=... # path to the preprocessed dataset, must be visible from all nodes
$ PORT=9218 # any available TCP port that can be used by the trainer to establish initial connection
$ sbatch --job-name fairseq-py --gres gpu:8 --cpus-per-task 10 \
--nodes 16 --ntasks-per-node 8 \
--wrap 'srun --output train.log.node%t --error train.stderr.node%t.%j \
python train.py $DATA \
--distributed-world-size 128 \
--distributed-port $PORT \
--force-anneal 50 --lr-scheduler fixed --max-epoch 55 \
--arch fconv_wmt_en_fr --optimizer nag --lr 0.1,4 --max-tokens 3000 \
--clip-norm 0.1 --dropout 0.1 --criterion label_smoothed_cross_entropy \
--label-smoothing 0.1 --wd 0.0001'
Alternatively you can manually start one process per GPU:
$ DATA=... # path to the preprocessed dataset, must be visible from all nodes
$ HOST_PORT=master.devserver.com:9218 # one of the hosts used by the job
$ RANK=... # the rank of this process, from 0 to 127 in case of 128 GPUs
$ python train.py $DATA \
--distributed-world-size 128 \
--distributed-init-method 'tcp://$HOST_PORT' \
--distributed-rank $RANK \
--force-anneal 50 --lr-scheduler fixed --max-epoch 55 \
--arch fconv_wmt_en_fr --optimizer nag --lr 0.1,4 --max-tokens 3000 \
--clip-norm 0.1 --dropout 0.1 --criterion label_smoothed_cross_entropy \
--label-smoothing 0.1 --wd 0.0001
- Facebook page: https://www.facebook.com/groups/fairseq.users
- Google group: https://groups.google.com/forum/#!forum/fairseq-users
If you use the code in your paper, then please cite it as:
@inproceedings{gehring2017convs2s,
author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N},
title = "{Convolutional Sequence to Sequence Learning}",
booktitle = {Proc. of ICML},
year = 2017,
}
fairseq(-py) is BSD-licensed. The license applies to the pre-trained models as well. We also provide an additional patent grant.
This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross.