1. C++/CUDA Extensions in PyTorch
2. Installation
3. To build
4. To test
5. To benchmark Python vs. C++ vs. CUDA
6. How to Contribute
   • 1. Fork the Repository
   • 2. Clone Your Fork
7. Authors An example of writing a C++/CUDA extension for PyTorch. See here for the accompanying tutorial. This repo demonstrates how to write an example extension_cpp.ops.mymuladd custom op that has both custom CPU and CUDA kernels.

The examples in this repo work with PyTorch 2.4+.

To use the following extension you must have the following:

• PyTorch 2.4+: Must have a compatible version installed.
• CUDA Toolkit: Required for building and running the CUDA kernels if using GPU acceleration.
• GCC or Clang: Necessary for compiling the C++ extension.
• Python 3.7+: Check you are using a compatible version of Python.

You can install the required packages using:

pip install torch
pip install -r requirements.txt  # If any additional requirements are specified

pip install .

python test/test_extension.py

python test/benchmark.py

Contributions are always welcome, to contribute remember to do the following:

• Click the "Fork" button at the top of this repository to create your own copy.

git clone https://github.com/<your-username>/extension-cpp.git
cd extension-cpp

Peter Goldsborough, Richard Zou