feat: Add Intel XPU (GPU) support#131
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- Add --accelerator CLI argument (auto, gpu, xpu, cpu, cuda) - Add XPUAccelerator, SingleXPUStrategy, and XPUMixedPrecision for PyTorch Lightning - Replace hardcoded 'cuda' with dynamic get_autocast_device_type() - Update tensor device handling across layers, losses, and modules - Add Intel GPU installation instructions to README
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feat: Add Intel XPU (GPU) Support
Hi @HannesStark ! 👋
First of all, I want to say thank you for creating and sharing BoltzGen. It's a fantastic tool for generative modeling of biomolecular structures, and the codebase is really well-structured and a pleasure to work with!
What this PR does
This PR adds full support for Intel XPU (GPU) devices, enabling BoltzGen to run on Intel GPUs (Arc, Data Center Max Series, etc.) via PyTorch's XPU backend while maintaining full CUDA compatibility.
Features & Changes
New CLI functionality:
--acceleratorargument with choices:auto,gpu,xpu,cpu,cudaPyTorch Lightning XPU Integration:
XPUAcceleratorclass for PyTorch LightningSingleXPUStrategyfor single-device XPU training/inferenceXPUMixedPrecisionplugin for proper mixed precision handling on XPU devicesModel & Module Updates:
'cuda'autocast device type with dynamicget_autocast_device_type()_get_device()helper that checks for XPU availability in addition to CUDADocumentation: