/OpenNMT-py

Open Source Neural Machine Translation in PyTorch

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OpenNMT-py: Open-Source Neural Machine Translation

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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.

Before raising an issue, make sure you read the requirements and the documentation examples.

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 in a separate branch. It will no longer receive extensive development from the core team but PRs may still be accepted.

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

Table of Contents

Setup

OpenNMT-py requires:

  • Python >= 3.6
  • PyTorch == 1.6.0

Install OpenNMT-py from pip:

pip install OpenNMT-py

or from the sources:

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

Note: if you encounter a MemoryError during installation, try to use pip with --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

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) and target (tgt) data containing one sentence per line with tokens separated by a space:

  • 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 is required here -- it represents the number of lines sampled from each corpus to build the vocab.
  • This configuration is the simplest possible, without any tokenization or other transforms. See other example configurations for more complex pipelines.

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 onmt_build_vocab;
  • 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 together with *.transforms.pt will be dumpped to -save_data with configurations specified in -config yaml file. We'll also generate transformed samples to simplify any potentially required visual inspection. The number of sample lines to dump per corpus is set with the -n_sample flag.

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 command line option.

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:

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",
}