aqtp 0.0.2

AQT: Accurate Quantized Training

Accurate Quantized Training

This directory contains libraries for running and analyzing neural network quantization experiments in JAX and flax.

Summary about this work is presented at paper Pareto-Optimal Quantized ResNet Is Mostly 4-bit. Please cite the paper in your publications if you find the source code useful for your research.

Contributors: Shivani Agrawal, Lisa Wang, Jonathan Malmaud, Lukasz Lew, Pouya Dormiani, Phoenix Meadowlark, Oleg Rybakov.

AQT Quantization Library

Jax and Flax quantization libraries provides what you serve is what you train quantization for convolution and matmul. See this README.md.

Reporting Tool

After a training run has completed, the reporting tool in report_utils.py allows to generate a concise experiment report with aggregated metrics and metadata. See this README.md.