OpenNMT-py: Open-Source Neural Machine Translation

OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework. It is designed to be research friendly to try out new ideas in translation, summary, morphology, and many other domains. Some companies have proven the code to be production ready.

We love contributions! Please look at issues marked with the contributions welcome tag.

Unless there is a bug, please use the forum or Gitter to ask questions.

Announcement - OpenNMT-py 2.0

We're happy to announce the upcoming release v2.0 of OpenNMT-py.

The major idea behind this release is the -- almost -- complete makeover of the data loading pipeline. A new 'dynamic' paradigm is introduced, allowing to apply on the fly transforms to the data.

This has a few advantages, amongst which:

• remove or drastically reduce the preprocessing required to train a model;
• increase the possibilities of data augmentation and manipulation through on-the fly transforms.

These transforms can be specific tokenization methods, filters, noising, or any custom transform users may want to implement. Custom transform implementation is quite straightforward thanks to the existing base class and example implementations.

You can check out how to use this new data loading pipeline in the updated docs.

All the readily available transforms are described here.

Performance

Given sufficient CPU resources according to GPU computing power, most of the transforms should not slow the training down. (Note: for now, one producer process per GPU is spawned -- meaning you would ideally need 2N CPU threads for N GPUs).

Breaking changes

For now, the new data loading paradigm does not support Audio, Video and Image inputs.

A few features are also dropped, at least for now:

• audio, image and video inputs;
• source word features.

For any user that still need these features, the previous codebase will be retained as

legacy

Feel free to check it out and let us know what you think of the new paradigm!

Table of Contents

• Setup
• Features
• Quickstart
• Pretrained embeddings
• Pretrained models
• Acknowledgements
• Citation

Setup

OpenNMT-py requires:

• Python >= 3.6
• PyTorch == 1.6.0

Install

OpenNMT-py

pip

bash
pip install OpenNMT-py

or from the sources:

bash
git clone https://github.com/OpenNMT/OpenNMT-py.git
cd OpenNMT-py
pip install -e .

Note: if you encounter a

MemoryError

pip

--no-cache-dir

(Optional) Some advanced features (e.g. working pretrained models or specific transforms) require extra packages, you can install them with:

pip install -r requirements.opt.txt

Features

• :warning: New in OpenNMT-py 2.0: On the fly data processing

• Encoder-decoder models with multiple RNN cells (LSTM, GRU) and attention types (Luong, Bahdanau)

• Transformer models

• Copy and Coverage Attention

• Pretrained Embeddings

• Source word features

• TensorBoard logging

• Multi-GPU training

• Data preprocessing

• Inference (translation) with batching and beam search

• Inference time loss functions

• Conv2Conv convolution model

• SRU "RNNs faster than CNN" paper

• Mixed-precision training with APEX, optimized on Tensor Cores

• Model export to CTranslate2, a fast and efficient inference engine

Quickstart

Full Documentation

Step 1: Prepare the data

To get started, we propose to download a toy English-German dataset for machine translation containing 10k tokenized sentences:

wget https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz
tar xf toy-ende.tar.gz
cd toy-ende

The data consists of parallel source (

src

tgt

• src-train.txt

• tgt-train.txt

• src-val.txt

• tgt-val.txt

Validation files are used to evaluate the convergence of the training. It usually contains no more than 5k sentences.

$ head -n 2 toy-ende/src-train.txt
It is not acceptable that , with the help of the national bureaucracies , Parliament 's legislative prerogative should be made null and void by means of implementing provisions whose content , purpose and extent are not laid down in advance .
Federal Master Trainer and Senior Instructor of the Italian Federation of Aerobic Fitness , Group Fitness , Postural Gym , Stretching and Pilates; from 2004 , he has been collaborating with Antiche Terme as personal Trainer and Instructor of Stretching , Pilates and Postural Gym .

We need to build a YAML configuration file to specify the data that will be used:

# toy_en_de.yaml

Where the samples will be written
save_data: toy-ende/run/example
Where the vocab(s) will be written
src_vocab: toy-ende/run/example.vocab.src
tgt_vocab: toy-ende/run/example.vocab.tgt
Prevent overwriting existing files in the folder
overwrite: False
Corpus opts:
data:
    corpus_1:
        path_src: toy-ende/src-train.txt
        path_tgt: toy-ende/tgt-train.txt
    valid:
        path_src: toy-ende/src-val.txt
        path_tgt: toy-ende/tgt-val.txt
...

From this configuration, we can build the vocab(s) that will be necessary to train the model:

onmt_build_vocab -config toy_en_de.yaml -n_sample 10000

Notes: -

-n_sample

Step 2: Train the model

To train a model, we need to add the following to the YAML configuration file: - the vocabulary path(s) that will be used: can be that generated by onmtbuildvocab; - training specific parameters.

# toy_en_de.yaml

...
Vocabulary files that were just created
src_vocab: toy-ende/run/example.vocab.src
tgt_vocab: toy-ende/run/example.vocab.tgt
Train on a single GPU
world_size: 1
gpu_ranks: [0]
Where to save the checkpoints
save_model: toy-ende/run/model
save_checkpoint_steps: 500
train_steps: 1000
valid_steps: 500

Then you can simply run:

onmt_train -config toy_en_de.yaml

This configuration will run the default model, which consists of a 2-layer LSTM with 500 hidden units on both the encoder and decoder. It will run on a single GPU (

world_size 1

gpu_ranks [0]

Before the training process actually starts, the

*.vocab.pt

*.transforms.pt

-save_data

-config

-n_sample

For more advanded models and parameters, see other example configurations or the FAQ.

Step 3: Translate

onmt_translate -model toy-ende/run/model_step_1000.pt -src toy-ende/src-test.txt -output toy-ende/pred_1000.txt -gpu 0 -verbose

Now you have a model which you can use to predict on new data. We do this by running beam search. This will output predictions into

toy-ende/pred_1000.txt

Note:

The predictions are going to be quite terrible, as the demo dataset is small. Try running on some larger datasets! For example you can download millions of parallel sentences for translation or summarization.

(Optional) Step 4: Release

When you are satisfied with your trained model, you can release it for inference. The release process will remove training-only parameters from the checkpoint:

onmt_release_model -model toy-ende/run/model_step_1000.pt -output toy-ende/run/model_step_1000_release.pt

The release script can also export checkpoints to CTranslate2, a fast inference engine for Transformer models. See the

-format

Pretrained embeddings (e.g. GloVe)

Please see the FAQ: How to use GloVe pre-trained embeddings in OpenNMT-py

Pretrained models

Several pretrained models can be downloaded and used with

onmt_translate

http://opennmt.net/Models-py/

Acknowledgements

OpenNMT-py is run as a collaborative open-source project. The original code was written by Adam Lerer (NYC) to reproduce OpenNMT-Lua using PyTorch.

Major contributors are: * Sasha Rush (Cambridge, MA) * Vincent Nguyen (Ubiqus) * Ben Peters (Lisbon) * Sebastian Gehrmann (Harvard NLP) * Yuntian Deng (Harvard NLP) * Guillaume Klein (Systran) * Paul Tardy (Ubiqus / Lium) * François Hernandez (Ubiqus) * Linxiao Zeng (Ubiqus) * Jianyu Zhan (Shanghai) * Dylan Flaute (University of Dayton) * ... and more!

OpenNMT-py is part of the OpenNMT project.

Citation

If you are using OpenNMT-py for academic work, please cite the initial system demonstration paper published in ACL 2017:

@inproceedings{klein-etal-2017-opennmt,
    title = "{O}pen{NMT}: Open-Source Toolkit for Neural Machine Translation",
    author = "Klein, Guillaume  and
      Kim, Yoon  and
      Deng, Yuntian  and
      Senellart, Jean  and
      Rush, Alexander",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4012",
    pages = "67--72",
}