This repository contains the source code and dataset link mentioned in WWW 2022 accepted paper "TRACE:A Fast Transformer-based General-Purpose Lossless Compressor". TRACE is a deep-learning based lossless compressor which compresses byte streams. The model estimates probability of incoming bytes and arithmetic coder utilize this probability to encode. We want to focus at model sequencial representation ability so our method do not have pretraining stage, which mean the program start compression when model knows nothing, and adaptively adjust model parameters during compression. If you want higher compression ratio, do several epochs pretraining would help a lot.
The code of performer is from https://github.com/google-research/google-research/tree/master/performer/models/slim_performer
Nvidia-driver 455.38 CUDA 11.1 cudnn 7605 pytorch==1.7.0 numpy==1.18.5
git clone https://github.com/mynotwo/A-Fast-Transformer-based-General-Purpose-LosslessCompressor.git
cd ./A-Fast-Transformer-based-General-Purpose-LosslessCompressor
mkdir data
cd data
Then download dataset from https://drive.google.com/file/d/18qvfbeeOwD1Fejq9XtgAJwYoXjSV8UaC/view?usp=sharing and put it into \data.
Then do
tar zcxf compression_data.tar.gz
To compress data, e.g. file named 'dickens' which is already provided in this repository
python compressor.py --input_dir ./dickens --batch_size 512 --gpu_id 0 --prefix dickens --hidden_dim 256 --ffn_dim 4096 --seq_len 8 --learning_rate 1e-3 --vocab_dim 64
and the compressed file would be dickens_64_256_4096_bs512_random_seq32.compress.combined