XPU Support#
PyTorch provides XPU support for Intel GPU-accelerated tensor operations. The XPU API allows you to manage Intel GPU devices, streams for asynchronous execution, and synchronization.
When to use XPU APIs:
When running on Intel GPUs (Data Center GPU Max, Arc, etc.)
When implementing custom XPU kernels or operations
When managing asynchronous execution with XPU streams
When writing device-portable code alongside CUDA
Basic usage:
#include <torch/torch.h>
// Check if XPU is available
if (torch::xpu::is_available()) {
// Create tensor on XPU
auto tensor = torch::randn({2, 3}, torch::device(torch::kXPU));
// Move model to XPU
model->to(torch::kXPU);
}
Header Files#
torch/xpu.h- High-level XPU utilities (device count, availability, seeding)c10/xpu/XPUStream.h- XPU stream management
