Description

In theory, Binarized Neural Networks boast of x32 compression and x10 acceleration on GPUs. However, all variables are stored and manipulated in the form of float-32 on PyTorch. In this case, if we want to implement 1-bit networks on PyTorch, we have to change the kernel.

This repository contains a wrapped CUDA kernel that supports both 1-bit storage and computation on PyTorch

How to use

check the environment

GCC:     >=5.0
PyTorch: <1.0
PyThon:  >=3.5
CUDA:    >=8.0

build the kernel

GPU

cd ./csrc/binop
make

CPU

cd ./csrc/binop_cpu
make

check the API and use

GPU

import torch
import binop
...

CPU

import torch
import binop_cpu

References

How to wrap a CUDA and use its functions in PyTorch:

Pytorch Custom CUDA kernel Tutorial

Examples

You can see the GPU example in test

You can see the CPU example in test