This repository is the official implementation of [1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit]

INSTALL

Tested with PyTorch 1.4.0 + CUDA 10.1.

Step 1: Install apex

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Step 2: Install this repo

# Make sure that your nvcc version is compatible with the cuda library version used by PyTorch
nvcc --version
cd pytorch_minimax
python setup.py install
cd ..

QAT

python imagenet.py --arch resnet18

FQT

python cifar10_imagenet.py

Reference

• XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
• https://github.com/jiecaoyu/XNOR-Net-PyTorch *[A Statistical Framework for Low-bitwidth Training of Deep Neural Networks(https://arxiv.org/abs/2010.14298)]
• https://github.com/cjf00000/StatQuant