2 Commits

Author	SHA1	Message	Date
Yu, Guangye	46e3f670b4	refactor code to share across different devices (#120602) ... # Motivation Refactor utils code to make it possible to share across CUDA, XPU, and other backends. # Solution Move `_dummy_type` and `_LazySeedTracker` to torch._utils; # Additional Context When upstreaming, refactor these code changes by isolating them into in an additional PR to minimize their impact on the CUDA code. Pull Request resolved: https://github.com/pytorch/pytorch/pull/120602 Approved by: https://github.com/albanD, https://github.com/jgong5, https://github.com/gujinghui, https://github.com/EikanWang	2024-02-28 09:42:58 +00:00
Yu, Guangye	a205e7bf56	[3/4] Intel GPU Runtime Upstreaming for Device (#116850) ... # Motivation According to [[1/4] Intel GPU Runtime Upstreaming for Device](https://github.com/pytorch/pytorch/pull/116019), As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), this third PR covers the changes under `libtorch_python`. # Design This PR primarily offers device-related APIs in python frontend, including - `torch.xpu.is_available` - `torch.xpu.device_count` - `torch.xpu.current_device` - `torch.xpu.set_device` - `torch.xpu.device` - `torch.xpu.device_of` - `torch.xpu.get_device_name` - `torch.xpu.get_device_capability` - `torch.xpu.get_device_properties` - ==================== - `torch.xpu._DeviceGuard` - `torch.xpu._is_compiled` - `torch.xpu._get_device` # Additional Context We will implement the support of lazy initialization in the next PR. Pull Request resolved: https://github.com/pytorch/pytorch/pull/116850 Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/gujinghui, https://github.com/malfet	2024-02-01 12:31:26 +00:00