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Hyperbolic Neural Networks++

This repository is the official pytorch implementation of Hyperbolic Neural Networks++, accepted by ICLR2021, aiming to reproduce the results in Section 4.3 out of three main experiments condcted in the paper.

Requirements

  • python: ^3.6
  • cuda: ^9
  • pytorch: 1.6
  • geoopt: 0.3

This repository is built upon the fairseq library, and inherits geoopt library for the computation on the Poincaré ball model. Please read the README at first to learn how to use fairseq.

1. Data Preparation

Basically, this repository can deal with any kind of fairseq-type datasets for translation tasks. The following shows one specific example to prepare WMT'17 English to German translation dataset.

Downloading Data

mkdir -p /PATH/TO/DATA/scripts
cp examples/translation/prepare-wmt14en2de.sh /PATH/TO/DATA/scripts/
cd /PATH/TO/DATA/scripts
bash prepare-wmt14en2de.sh

Then you can download the dataset in /PATH/TO/DATA/wmt17_en_de.

Preprocessing

cd bin/preprocess
bash preprocess_wmt17_en_de.sh /PATH/TO/DATA/wmt17_en_de

2. Training

To train a model, run:

cd bin/train
bash train_p_fconv_wmt17_en_de.sh N_GPUS GPU_IDS SCRATCH \
    /PATH/TO/DATA/wmt17_en_de/tokenized /PATH/TO/CHECKPOINT \
    DIM DROPOUT WARMUP_STEP MAX_STEP
  • N_GPUS : the number of GPUs you use to train a model, e.g., 4.
  • GPU_IDS: a comma separated GPU id list, e.g., 0,1,2,3.
  • SCRATCH: a specifier to determine whether to train from scratch -s or to resume a previous checkpoint -r.
  • DIM : dimensionality of feature vectors, e.g., 16.
  • DROPOUT: dropout probability, e.g., 0.0.
  • WARMUP_STEP: the number of warmup steps, e.g., 4000.
  • MAX_STEP: the number of trainig iterations, e.g., 100000.

3. Inference

To verify the performance of a trained model, run:

cd bin/generate
bash generate_p_fconv_wmt17_en_de.sh GPU_ID \
    /PATH/TO/DATA/wmt17_en_de/tokenized /PATH/TO/CHECKPOINT DIM

Then you can get translated sentences and the BLEU score for the test dataset.

Citation

@inproceedings{
shimizu2021hyperbolic,
title={Hyperbolic Neural Networks++},
author={Ryohei Shimizu and YUSUKE Mukuta and Tatsuya Harada},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=Ec85b0tUwbA}
}