An addernet CUDA version

Related tags

Deep LearningAdderNetCUDA
Overview

Training addernet accelerated by CUDA

Usage

cd adder_cuda
python setup.py install
cd ..
python main.py

Environment

pytorch 1.10.0 CUDA 11.3

benchmark

version training_time_per_batch/s
raw 1.61
torch.cdist 1.49
cuda_unoptimized 0.4508
this work 0.3158

The CUDA version of AdderNet has achieved a 5× speed increase over the original version. There seems to be some bugs in the Cuda_unoptimized version, causing the model to fail to converge. Its speed is still listed here for comparison. The experiment was run on RTX 2080Ti platform, and ResNet-20 based on CIFAR-10 was trained.

Time(%) Time Calls Avg Min Max Name
48.57 30.4752s 3920 7.7743ms 162.70us 12.271ms CONV_BACKWARD
34.85 21.8686s 19680 1.1112ms 5.3770us 11.827ms _ZN2at6native27unrolled_elementwise_kernel...
7.46 4.67901s 5920 790.37us 26.529us 1.5841ms CONV
2.24 1.40372s 3920 358.09us 31.298us 845.80us col2im_kernel
2.10 1.31882s 36862 35.777us 1.4720us 276.24us vectorized_elementwise_kernel
1.43 900.03ms 5920 152.03us 7.9040us 372.40us im2col_kernel

Here is the time distribution of training an epoch. If you are interested, you can continue to optimize the CUDA kernel.

Owner
LingXY
LingXY
GitHub Repository
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