Neural Network CUDA Example
Several simple examples for neural network toolkits (PyTorch, TensorFlow, etc.) calling custom CUDA operators.
We provide several ways to compile the CUDA kernels and their cpp wrappers, including jit, setuptools and cmake.
We also provide several python codes to call the CUDA kernels, including kernel time statistics and model training.
For more accurate time statistics, you'd best use nvprof or nsys to run the code.
Environments
- NVIDIA Driver: 418.116.00
- CUDA: 11.0
- Python: 3.7.3
- PyTorch: 1.7.0+cu110
- TensorFlow: 2.4.1
- CMake: 3.16.3
- Ninja: 1.10.0
- GCC: 8.3.0
Cannot ensure successful running in other environments.
Code structure
├── include
│ └── add2.h # header file of add2 cuda kernel
├── kernel
│ └── add2_kernel.cu # add2 cuda kernel
├── pytorch
│ ├── add2_ops.cpp # torch wrapper of add2 cuda kernel
│ ├── time.py # time comparison of cuda kernel and torch
│ ├── train.py # training using custom cuda kernel
│ ├── setup.py
│ └── CMakeLists.txt
├── tensorflow
│ ├── add2_ops.cpp # tensorflow wrapper of add2 cuda kernel
│ ├── time.py # time comparison of cuda kernel and tensorflow
│ ├── train.py # training using custom cuda kernel
│ └── CMakeLists.txt
├── LICENSE
└── README.md
PyTorch
Compile cpp and cuda
JIT
Directly run the python code.
Setuptools
python3 pytorch/setup.py install
CMake
mkdir build
cd build
cmake ../pytorch
make
Run python
Compare kernel running time
python3 pytorch/time.py --compiler jit
python3 pytorch/time.py --compiler setup
python3 pytorch/time.py --compiler cmake
Train model
python3 pytorch/train.py --compiler jit
python3 pytorch/train.py --compiler setup
python3 pytorch/train.py --compiler cmake
TensorFlow
Compile cpp and cuda
CMake
mkdir build
cd build
cmake ../tensorflow
make
Run python
Compare kernel running time
python3 tensorflow/time.py --compiler cmake
Train model
python3 tensorflow/train.py --compiler cmake