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https://github.com/opennmt/opennmt-tf
Neural machine translation and sequence learning using TensorFlow
https://github.com/opennmt/opennmt-tf
deep-learning machine-translation natural-language-processing neural-machine-translation opennmt python tensorflow
Last synced: 29 days ago
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Neural machine translation and sequence learning using TensorFlow
- Host: GitHub
- URL: https://github.com/opennmt/opennmt-tf
- Owner: OpenNMT
- License: mit
- Created: 2017-11-02T10:53:22.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2023-10-14T18:03:53.000Z (about 1 year ago)
- Last Synced: 2024-10-15T11:03:17.049Z (29 days ago)
- Topics: deep-learning, machine-translation, natural-language-processing, neural-machine-translation, opennmt, python, tensorflow
- Language: Python
- Homepage: https://opennmt.net/
- Size: 23.9 MB
- Stars: 1,454
- Watchers: 63
- Forks: 391
- Open Issues: 33
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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# OpenNMT-tf
OpenNMT-tf is a general purpose sequence learning toolkit using TensorFlow 2. While neural machine translation is the main target task, it has been designed to more generally support:
* sequence to sequence mapping
* sequence tagging
* sequence classification
* language modeling
The project is production-oriented and comes with [backward compatibility guarantees](https://github.com/OpenNMT/OpenNMT-tf/blob/master/CHANGELOG.md).
## Key features
### Modular model architecture
Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence to sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings:
```python
opennmt.models.SequenceToSequence(
source_inputter=opennmt.inputters.ParallelInputter(
[
opennmt.inputters.WordEmbedder(embedding_size=256),
opennmt.inputters.WordEmbedder(embedding_size=256),
],
reducer=opennmt.layers.ConcatReducer(axis=-1),
),
target_inputter=opennmt.inputters.WordEmbedder(embedding_size=512),
encoder=opennmt.encoders.SelfAttentionEncoder(num_layers=6),
decoder=opennmt.decoders.AttentionalRNNDecoder(
num_layers=4,
num_units=512,
attention_mechanism_class=tfa.seq2seq.LuongAttention,
),
share_embeddings=opennmt.models.EmbeddingsSharingLevel.TARGET,
)
```
The [`opennmt`](https://opennmt.net/OpenNMT-tf/package/overview.html) package exposes other building blocks that can be used to design:
* [multiple input features](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.ParallelInputter.html)
* [mixed embedding representation](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.MixedInputter.html)
* [multi-source context](https://opennmt.net/OpenNMT-tf/package/opennmt.inputters.ParallelInputter.html)
* [cascaded](https://opennmt.net/OpenNMT-tf/package/opennmt.encoders.SequentialEncoder.html) or [multi-column](https://opennmt.net/OpenNMT-tf/package/opennmt.encoders.ParallelEncoder.html) encoder
* [hybrid sequence to sequence models](https://opennmt.net/OpenNMT-tf/package/opennmt.models.SequenceToSequence.html)
Standard models such as the Transformer are defined in a [model catalog](https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/models/catalog.py) and can be used without additional configuration.
*Find more information about model configuration in the [documentation](https://opennmt.net/OpenNMT-tf/model.html).*
### Full TensorFlow 2 integration
OpenNMT-tf is fully integrated in the TensorFlow 2 ecosystem:
* Reusable layers extending [`tf.keras.layers.Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer)
* Multi-GPU training with [`tf.distribute`](https://www.tensorflow.org/api_docs/python/tf/distribute) and distributed training with [Horovod](https://github.com/horovod/horovod)
* Mixed precision training with [`tf.keras.mixed_precision`](https://www.tensorflow.org/guide/mixed_precision)
* Visualization with [TensorBoard](https://www.tensorflow.org/tensorboard)
* `tf.function` graph tracing that can be [exported to a SavedModel](https://opennmt.net/OpenNMT-tf/serving.html) and served with [TensorFlow Serving](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/serving/tensorflow_serving) or [Python](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/serving/python)
### Compatibility with CTranslate2
[CTranslate2](https://github.com/OpenNMT/CTranslate2) is an optimized inference engine for OpenNMT models featuring fast CPU and GPU execution, model quantization, parallel translations, dynamic memory usage, interactive decoding, and more! OpenNMT-tf can [automatically export](https://opennmt.net/OpenNMT-tf/serving.html#ctranslate2) models to be used in CTranslate2.
### Dynamic data pipeline
OpenNMT-tf does not require to compile the data before the training. Instead, it can directly read text files and preprocess the data when needed by the training. This allows [on-the-fly tokenization](https://opennmt.net/OpenNMT-tf/tokenization.html) and data augmentation by injecting random noise.
### Model fine-tuning
OpenNMT-tf supports model fine-tuning workflows:
* Model weights can be transferred to new word vocabularies, e.g. to inject domain terminology before fine-tuning on in-domain data
* [Contrastive learning](https://ai.google/research/pubs/pub48253/) to reduce word omission errors
### Source-target alignment
Sequence to sequence models can be trained with [guided alignment](https://arxiv.org/abs/1607.01628) and alignment information are returned as part of the translation API.
---
OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
* automatic evaluation during the training
* multiple decoding strategy: greedy search, beam search, random sampling
* N-best rescoring
* gradient accumulation
* scheduled sampling
* checkpoint averaging
* ... and more!
*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
## Usage
OpenNMT-tf requires:
* Python 3.7 or above
* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, 2.11, 2.12, or 2.13
We recommend installing it with `pip`:
```bash
pip install --upgrade pip
pip install OpenNMT-tf
```
*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
### Command line
OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
* the **model** type
* the **parameters** described in a YAML file
* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
that are passed to the main script:
```
onmt-main --model_type --config --auto_config
```
*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
### Library
OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
For example, the `Runner` class can be used to train and evaluate models with few lines of code:
```python
import opennmt
config = {
"model_dir": "/data/wmt-ende/checkpoints/",
"data": {
"source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"train_features_file": "/data/wmt-ende/train.en",
"train_labels_file": "/data/wmt-ende/train.de",
"eval_features_file": "/data/wmt-ende/valid.en",
"eval_labels_file": "/data/wmt-ende/valid.de",
}
}
model = opennmt.models.TransformerBase()
runner = opennmt.Runner(model, config, auto_config=True)
runner.train(num_devices=2, with_eval=True)
```
Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
```python
decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
initial_state = decoder.initial_state(
memory=memory, memory_sequence_length=memory_sequence_length
)
batch_size = tf.shape(memory)[0]
start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
decoding_result = decoder.dynamic_decode(
target_embedding,
start_ids=start_ids,
initial_state=initial_state,
decoding_strategy=opennmt.utils.BeamSearch(4),
)
```
More examples using OpenNMT-tf as a library can be found online:
* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
## Additional resources
* [Documentation](https://opennmt.net/OpenNMT-tf)
* [Forum](https://forum.opennmt.net)
* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)