ELF

ELF (Exponent-Less Float-point encoding) is a simple yet effective, near-lossless floating-point compression method, that transforms floating-point parameters within pre-trained models (PTMs) in such a way that the common exponent field of the transformed parameters can be completely eliminated to save storage space. ELF is embarrassingly parallel via data parallelism, achieving an extreme compression & decompression speed.

We also developed ELVES, a compression framework integrating ELF and several other data reduction methods. ELVES uses the most effective method to compress PTMs that exhibit different patterns.

Publication

Everything You Always Wanted to Know About Storage Compressibility of Pre-Trained ML Models but Were Afraid to Ask (VLDB'24 to appear).

The preprint of our VLDB'24 paper can be viewed at: https://arxiv.org/abs/2402.13429.

This branch contains the source code of ELF & ELVES.

ELF Build

Package Installation

sudo apt-get update
sudo apt-get upgrade -y
sudo apt-get install -y gcc g++ libeigen3-dev make python3-pip

Build ELF and DE

mkdir build
make

ELVES Compression Examples

Package Installation

pip3 install -r requirements.txt

Run the Example

cd ELVES
python3 main.py

ELF Usage

./build/elf_pthread -c -i ELVES/model_compressed/elves_compression/distilbert-base-cased/fl_weights/f32_65783040.bin -p f32 -o ELVES/model_compressed/elves_compression/distilbert-base-cased/exponential_dedup/f32/ -n 65783040

Parameter Explanation

Parameter	Explanation
-i	input binary file
-p	parameter type: f16 for floating 16, f32 for floating 32, f64 for floating 64
-n	parameter number
-c	compression
-d	decompression
-o	output folder specified