apex-cuda-extension-removal

Name: apex-cuda-extension-removal
Availability: InStock
Author: Fudan-SMI-lab

Replace NVIDIA Apex imports and APIs with NPU-native PyTorch equivalents.

39|4|Updated May 18, 2026

Search Tags:#apex #amp #ddp #ascend #npu #layernorm #fused-optimizers

Ready to run

npx skills add https://github.com/Fudan-SMI-lab/SEAM --skill apex-cuda-extension-removal

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Please help me install this Agent Skill.
Skill: apex-cuda-extension-removal
Source: https://github.com/Fudan-SMI-lab/SEAM/tree/main/.memory/skills/apex-cuda-extension-removal
Command: npx skills add https://github.com/Fudan-SMI-lab/SEAM --skill apex-cuda-extension-removal

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill eliminates the need to manually refactor CUDA-specific Apex code when migrating to Ascend NPUs by guiding the removal and replacement of Apex APIs with native PyTorch equivalents.

Core Features & Use Cases

Apex removal guidance: Identify and remove Apex imports (apex, apex_C) across the codebase.
NPU-native replacements: Replace fused_layer_norm with PyTorch LayerNorm, AMP with native autocast/GradScaler, and Fused optimizers with standard PyTorch optimizers.
DDP migration: Swap Apex DistributedDataParallel with PyTorch DistributedDataParallel.
Verification: Validate functional equivalence with small test inputs and end-to-end checks.

Quick Start

Identify all Apex usages in your codebase and replace Apex imports and APIs with NPU-native PyTorch equivalents, then run a small test to verify outputs.

Frequently Asked Questions about apex-cuda-extension-removal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I replace NVIDIA Apex with native PyTorch for Ascend NPU compatibility?▼

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.

What is the native PyTorch equivalent for Apex fused_layer_norm when migrating to NPU?▼

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.

How do I migrate Apex AMP to PyTorch autocast and GradScaler on Ascend?▼

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.

Can I use standard PyTorch DistributedDataParallel instead of Apex DDP on NPU?▼

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.

How do I verify functional parity after removing Apex dependencies from my codebase?▼

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.