FourierTransformer

This is the official Pytorch implementation of paper Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

Install

• Pytorch version >= 1.13.0
• Fairseq version >= 0.12.3

git clone https://github.com/LUMIA-Group/FourierTransformer.git
cd FourierTransformer
pip install -e .

For faster training, install NVIDIA's apex library following fairseq.

Experiments

# Download files for preprocessing

wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt'

Further Pretraining on Pile

1. Download the Pile dataset from here.
2. Preprocess the Pile:

   # BPE
   for SPLIT in train val test; do \
    python -m examples.roberta.multiprocessing_bpe_encoder \
        --encoder-json encoder.json \
        --vocab-bpe vocab.bpe \
        --inputs pile/${SPLIT}.raw.en \
        --outputs pile/${SPLIT}.bpe.en \
        --keep-empty \
        --workers 120; \
   done
   
   # Binarize
   fairseq-preprocess \
    --only-source \
    --source-lang "en" \
    --srcdict dict.txt \
    --trainpref pile/train.bpe \
    --validpref pile/val.bpe \
    --testpref pile/test.bpe \
    --destdir pile-bin \
    --workers 60
   
   rename files in pile-bin by removing ".en".
   

3. Script to further pretrain Fourier-Bart (in our paper, we randomly sliced 10G data from the Pile to conduct further pretraining).

CNN-Dailymail

1. Download, Preprocess and Binarize: Follow this script.

2. Fine-tuning Fourier Transformer on CNN-DM summarization task:

   cd Summarization
   sh submits/cnn-dm.sh

3. Evaluate:
   For calculating rouge, install files2rouge from here.

   sh submits/eval-cnn-dm.sh
   

ELI5

1. Download, Preprocess and Binarize: Follow this script.

2. Fine-tuning Fourier Transformer on ELI5 QA task:

   cd Summarization
   sh submits/eli5.sh

3. Evaluate:

   sh submits/eval_eli5.sh
   

LRA

As mentioned in our paper, the code for LRA is build from this repository. Please follow the scripts there to prepare the datasets.

To run LRA experiments,

cd LRA/code
sh run_tasks.sh

Feel free to play with different settings by modifying lra_config.py