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.

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

pip install charonload

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");
  }

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

MIT

Patrick Stotko - [email protected]