apex-cuda-extension-removal

To achieve Ascend NPU compatibility, replace NVIDIA Apex imports and APIs with NPU-native PyTorch equivalents, swapping Apex AMP for native autocast, Fused optimizers for standard PyTorch optimizers, and Apex DDP for native DistributedDataParallel.

The native PyTorch equivalent for Apex fused_layer_norm when migrating to NPU is the standard PyTorch LayerNorm module, which removes CUDA dependencies while maintaining functional parity for your model.

Migrate Apex AMP to PyTorch autocast and GradScaler on Ascend by replacing Apex AMP imports with native PyTorch torch.cuda.amp equivalents, ensuring your mixed precision training runs on NPU without CUDA dependencies.

Yes, you can use standard PyTorch DistributedDataParallel instead of Apex DDP on NPU by swapping Apex DistributedDataParallel imports with native PyTorch equivalents to enable distributed training on Ascend.

Verify functional parity after removing Apex dependencies by running small test inputs and end-to-end checks across your codebase, ensuring there are no remaining Apex or apex_C references and outputs match expected behavior.