code-migration

To migrate PyTorch code from CUDA to Ascend NPU, you map CUDA APIs to torch_npu equivalents for model initialization, data movement, and training loops. This process requires replacing specific device calls and applying structured migration patterns.

API mappings for replacing CUDA functions with torch_npu provide direct equivalents for device placement and execution. These mappings cover essential PyTorch operations, ensuring correct tensor movement and model initialization on Ascend NPU hardware.

PyTorch AMP usage is supported during Ascend NPU migration. The process provides specific migration patterns for training loops and AMP usage, allowing you to maintain mixed precision training workflows when transitioning from CUDA.

The best way to validate PyTorch model correctness after migrating to Ascend NPU is by testing typical workloads and iterating until parity is achieved. This involves applying best practices to verify performance and correctness on the new device.

You can use standard PyTorch training loops when migrating to Ascend NPU by applying specific migration patterns. These patterns guide necessary adjustments for data movement and AMP usage to ensure correct execution on the target hardware.