Fix CUDA Extension Compilation and CPU Fallback Logic#6
Fix CUDA Extension Compilation and CPU Fallback Logic#6Electroiscoding wants to merge 1 commit intomainfrom
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- Added `torch` and `numpy` to `pyproject.toml` build-system requirements to ensure PyTorch is available for building the `CUDAExtension` during `pip install`. - Updated `setup.py` to correctly catch `SystemExit` from failed `nvcc` compilation attempts, gracefully falling back to a CPU-only build instead of entirely failing the installation. - Verified CPU fallback functionality via benchmarks and test suite. Co-authored-by: Electroiscoding <103299713+Electroiscoding@users.noreply.github.com>
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This pull request resolves an issue where the
pip install xerv-crayoncommand would fail to compile the CUDA extension or crash the entire setup process.By adding
torchandnumpyto thebuild-systemrequirements inpyproject.toml, pip correctly bootstraps the environment necessary for building PyTorch C++ extensions.Furthermore,
setup.pyhas been updated with a robusttry...except SystemExitblock. This ensures that if the system has CUDA but lacks proper paths or unsupported drivers (or any other compilation failure), the installation gracefully degrades to the AVX2/AVX-512 CPU-only backend instead of halting entirely.PR created automatically by Jules for task 11700401904638269954 started by @Electroiscoding