Add AMD GPU support via ROCm/HIP - #68
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Enable building FastGeodis on AMD GPUs with ROCm PyTorch. The existing CUDA kernels work on HIP through PyTorch's build-time hipify mechanism with no kernel-level changes needed. Changes: - setup.py: Detect ROCm PyTorch via ROCM_HOME when CUDA_HOME is not set, enabling the GPU build path on AMD systems - .gitignore: Exclude *.hip files generated during the hipify process The port is entirely automatic: PyTorch's CUDAExtension invokes hipify to translate the CUDA source at build time. All 4 kernels (2D/3D geodesic distance transforms, row/plane raster scans) use only standard CUDA runtime APIs and block-level synchronization that map 1:1 to HIP, with no warp-level intrinsics or textures. This work was developed with the assistance of Claude, an AI assistant by Anthropic. Test Plan: Built and tested on AMD MI250 (gfx90a) with ROCm 7.2 and PyTorch 2.13: ``` HIP_VISIBLE_DEVICES=0 PYTORCH_ROCM_ARCH=gfx90a pip install -e . --no-build-isolation python -m pytest tests/ -v ``` Result: 300 passed in 71.80s, covering 2D/3D geodesic distance transforms on both CPU and GPU paths.
c10.dll built with clang does not export the inherited constructor c10::ValueError(SourceLocation, string) because MSVC does not re-export inherited constructors from dllimport bases even for C10_API classes. MSVC-compiled extension .cpp files that include <torch/extension.h> trigger TORCH_CHECK_VALUE which generates a dllimport reference to that constructor, causing LNK2001. Fix: add a Windows-only /ALTERNATENAME linker directive in setup.py that redirects the missing ValueError(SourceLocation, string) dllimport thunk to Error(SourceLocation, string), which IS exported by c10.dll. ValueError IS-A Error with no additional data members; the constructors are semantically identical. Authored with Claude (claude-sonnet-4-6) as part of AMD ROCm porting. Test Plan: # Windows gfx1201 (RX 9070 XT, RDNA4, wave32) HIP_VISIBLE_DEVICES=0 PYTORCH_ROCM_ARCH=gfx1201 ROCM_HOME=<venv>/_rocm_sdk_devel DISTUTILS_USE_SDK=1 python.exe -m pip install -e . --no-build-isolation HIP_VISIBLE_DEVICES=0 python.exe -m pytest tests/ -v # Result: 300 passed
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FastGeodis currently builds its GPU extension only when
CUDA_HOMEis set, so on a ROCm PyTorch install the extension silently falls back to CPU. This adds AMD GPU support.The change is small because PyTorch does most of the work: its build-time hipify translates
fastgeodis_cuda.cuon the way through, so no kernel source changes were needed and the CUDA path is untouched.setup.pyalso checksROCM_HOMEfromtorch.utils.cpp_extension, soBUILD_CUDAis true on a ROCm install.CUDA_HOMEbehaviour is unchanged..gitignoreignores the*.hipfiles PyTorch generates during a ROCm build./ALTERNATENAMElink flag maps thec10::ValueError(SourceLocation, string)import toc10::Error(SourceLocation, string). A clang-builtc10.dlldoes not export the inherited constructor, so MSVC callers fail with LNK2001;ValueErrorderives fromErrorand adds no members, so the redirect is safe. It is guarded tosys.platform == "win32"and does not affect Linux or CUDA builds.Tested with
python -m pytest tests/(300 tests: 2D and 3D geodesic distance transforms, signed and unsigned, GPU and CPU):Both wavefront widths are covered: 64 on gfx90a and 32 on the RDNA parts. No CUDA hardware was available to re-run the NVIDIA path, but no CUDA-side source is modified.
This work was done with the assistance of an AI coding agent.