Neutron: A pytorch based implementation of the Transformer and its variants.
This project is developed with python 3.10.
Try pip install -r requirements.txt after you clone the repository.
If you want to use BPE, to enable convertion to C libraries, to try the simple MT server and to support Chinese word segmentation supported by pynlpir in this implementation, you should also install those dependencies in requirements.opt.txt with pip install -r requirements.opt.txt.
We provide scripts to apply Byte-Pair Encoding (BPE) under scripts/bpe/.
Generate training data for train.py with bash scripts/mktrain.sh, configure variables in scripts/mktrain.sh for your usage (the other variables shall comply with those in scripts/bpe/mk.sh).
Most configurations are managed in cnfg/base.py. Configure advanced details with cnfg/hyp.py.
Just execute the following command to launch the training:
python train.py
bash scripts/mktest.sh, configure variables in scripts/mktest.sh for your usage (while keep the other settings consistent with those in scripts/mkbpe.sh and scripts/mktrain.sh).
You can convert python classes into C libraries with python mkcy.py build_ext --inplace, and codes will be checked before compiling, which can serve as a simple to way to find typo and bugs as well. This function is supported by Cython. These files can be removed by commands tools/clean/cython.py . and rm -fr build/. Loading modules from compiled C libraries may also accelerate, but not significantly.
You can rank your corpus with pre-trained model, per token perplexity will be given for each sequence pair. Use it with:
python rank.py rsf h5f models
where rsf is the result file, h5f is HDF5 formatted input of file of your corpus (genrated like training set with tools/mkiodata.py like in scripts/mktrain.sh), models is a (list of) model file(s) to make perplexity evaluation.
Foundamental models needed for the construction of transformer.
Implementation of label smoothing loss function required by the training of transformer.
Learning rate schedule model needed according to the paper.
Functions for basic features, for example, freeze / unfreeze parameters of models, padding list of tensors to same size on assigned dimension.
Provide an encapsulation for the whole translation procedure with which you can use the trained model in your application easier.
An example depends on Flask to provide simple Web service and REST API about how to use the translator, configure those variables before you use it.
Implementations of seq2seq models.
Multi-GPU parallelization implementation.
Supportive functions for data segmentation.
Scripts to support data processing (e.g. text to tensor), analyzing, model file handling, etc.
Settings: WMT 2014, English -> German, 32k joint BPE with 8 as vocabulary threshold for BPE. 2 nVidia GTX 1080 Ti GPU(s) for training, 1 for decoding.
Tokenized case-sensitive BLEU measured with multi-bleu.perl, Training speed and decoding speed are measured by the number of target tokens (<eos> counted and <pad> discounted) per second and the number of sentences per second:
| BLEU | Training Speed | Decoding Speed | |
|---|---|---|---|
| Attention is all you need | 27.3 | ||
| Neutron | 28.07 | 23213.65 | 150.15 |
Hongfei Xu is partially supported by the Education Department of Henan Province (Grant No. 232300421386) while maintaining this project.
Details of this project can be found here, and please cite it if you enjoy the implementation :)
@article{xu2019neutron,
author = {Xu, Hongfei and Liu, Qiuhui},
title = "{Neutron: An Implementation of the Transformer Translation Model and its Variants}",
journal = {arXiv preprint arXiv:1903.07402},
archivePrefix = "arXiv",
eprinttype = {arxiv},
eprint = {1903.07402},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language},
year = 2019,
month = "March",
url = {https://arxiv.org/abs/1903.07402},
pdf = {https://arxiv.org/pdf/1903.07402}
}