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Develop C++/CUDA extensions with PyTorch like Python scripts

Project description

CharonLoad

PyPI - Version PyPI - Python Version GitHub License Tests Lint Documentation

CharonLoad is a bridge between Python code and rapidly developed custom C++/CUDA extensions to make writing high-performance research code with PyTorch easy:

  • 🔥 PyTorch C++ API detection and linking
  • 🔨 Automatic just-in-time (JIT) compilation of the C++/CUDA part
  • 📦 Cached incremental builds and automatic clean builds
  • 🔗 Full power of CMake for handling C++ dependencies
  • ⌨️ Python stub file generation for syntax highlighting and auto-completion in VS Code
  • 🐛 Interactive mixed Python/C++ debugging support in VS Code via Python C++ Debugger extension

CharonLoad reduces the burden to start writing and experimenting with custom GPU kernels in PyTorch by getting complex boilerplate code and common pitfalls out of your way. Developing C++/CUDA code with CharonLoad feels similar to writing Python scripts and lets you follow the same familiar workflow.

Installation

CharonLoad requires Python >=3.8 and can be installed from PyPI:

pip install charonload

Quick Start

CharonLoad only requires minimal changes to existing projects. In particular, a small configuration of the C++/CUDA project is added in the Python part while the CMake and C++ part should adopt some predefined functions:

  • <your_project>/main.py

    import charonload
    
    VSCODE_STUBS_DIRECTORY = pathlib.Path(__file__).parent / "typings"
    
    charonload.module_config["my_cpp_cuda_ext"] = charonload.Config(
        project_directory=pathlib.Path(__file__).parent / "<my_cpp_cuda_ext>",
        build_directory="custom/build/directory",  # optional
        stubs_directory=VSCODE_STUBS_DIRECTORY,  # optional
    )
    
    import other_module
    
  • <your_project>/other_module.py

    import my_cpp_cuda_ext  # JIT compiles and loads the extension
    
    tensor_from_ext = my_cpp_cuda_ext.generate_tensor()
    
  • <your_project>/<my_cpp_cuda_ext>/CMakeLists.txt

    find_package(charonload)
    
    if(charonload_FOUND)
        charonload_add_torch_library(${TORCH_EXTENSION_NAME} MODULE)
    
        target_sources(${TORCH_EXTENSION_NAME} PRIVATE src/<my_bindings>.cpp)
        # Further properties, e.g. link against other 3rd-party libraries, etc.
        # ...
    endif()
    
  • <your_project>/<my_cpp_cuda_ext>/src/<my_bindings>.cpp

    #include <torch/python.h>
    
    torch::Tensor generate_tensor();  // Implemented somewhere in <my_cpp_cuda_ext>
    
    PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
    {
        m.def("generate_tensor", &generate_tensor, "Optional Python docstring");
    }
    

Contributing

If you would like to contribute to CharonLoad, you can find more information in the Contributing guide.

License

MIT

Contact

Patrick Stotko - stotko@cs.uni-bonn.de

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