HAWQ is an advanced quantization library written for PyTorch. HAWQ enables low-precision and mixed-precision uniform quantization, with direct hardware implementation through TVM.

• PyTorch version >= 1.4.0
• Python version >= 3.6
• For training new models, you'll also need an NVIDIA GPU and NCCL
• To install HAWQ and develop locally:

git clone https://github.com/zhendong/HAWQ

An example to run uniform 8-bit quantization for resnet50 on ImageNet.

export CUDA_VISIBLE_DEVICES=0; 
python quant_train.py -a resnet50 --epochs 90 --lr 0.0001 --batch-size 128 --data /path/to/imagenet/ --pretrained --save-path /path/to/checkpoints/ --act-range-momentum=0.99 --wd 1e-4 --data-percentage 0.0001 --fold-BN 1 --fix-BN 1 --checkpoint-iter -1 --quant-scheme uniform8

• HAWQ-V3: Dyadic Neural Network Quantization
• HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks (NeurIPS 2020)
• HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision (ICCV 2019)

HAWQ is released under the Apache 2.0 license.