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:

cpp

#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

XPU Categories

{toctree}

:maxdepth: 1

streams
utilities