architecture-analysis

Analyze PyTorch model architectures for Ascend NPU migration requirements.

7|Updated Jan 29, 2026

Search Tags:#architecture #code analysis #PyTorch #distributed training #Ascend NPU #CUDA migration #NPU compatibility

Ready to run

npx skills add https://github.com/FeRhodium/ascend-migration --skill architecture-analysis-ferhodium

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: architecture-analysis
Source: https://github.com/FeRhodium/ascend-migration/tree/main/skills/architecture-analysis
Command: npx skills add https://github.com/FeRhodium/ascend-migration --skill architecture-analysis-ferhodium

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Architecture Analysis helps identify CUDA patterns, model structure, and distributed training setups that complicate or block migration to Ascend NPU. It provides a clear view of migration requirements and potential blockers early in the process.

Core Features & Use Cases

CUDA pattern detection: finds device placements, CUDA APIs, and custom kernels that need to be migrated.
Model structure assessment: evaluates layers, attention blocks, normalization, and activation usage for Ascend compatibility.
Distributed training analysis: inspects DDP/FSDP usage, process groups, and data parallel patterns to map to NPU-friendly equivalents.
Migration readiness planning: surfaces required API changes, data movement strategies, and potential performance implications.

Quick Start

Analyze a PyTorch project to generate a migration plan for Ascend NPU readiness. Example: analyze /path/to/repo for Ascend NPU compatibility.

Frequently Asked Questions about architecture-analysis

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

FAQPage Schema

How do I analyze PyTorch model architecture for Ascend NPU compatibility?▼

To analyze PyTorch model architecture for Ascend NPU compatibility, the skill evaluates layers, attention blocks, normalization, and activation usage to detect CUDA patterns and determine migration readiness. It produces a detailed migration plan outlining required API changes and data movement strategies.

What CUDA references do I need to map to NPU equivalents for migration?▼

Mapping CUDA references to NPU equivalents requires enumerating device placements, CUDA APIs, and custom kernels. The skill identifies these specific CUDA patterns in your PyTorch codebase and outlines the necessary API changes to translate them into NPU-compatible operations.

Can I assess distributed training patterns like DDP and FSDP for Ascend NPU migration?▼

Assessing distributed training patterns like DDP and FSDP for Ascend NPU migration is fully supported. The skill inspects process groups and data parallel patterns to map them to NPU-friendly equivalents, ensuring your distributed setup is compatible with the target hardware.

Does this analysis cover custom CUDA kernels and operations?▼

The analysis explicitly covers custom CUDA kernels and operations. It scans your codebase for these custom implementations, evaluates their compatibility with Ascend NPU architecture, and includes them in the generated migration requirements and plan.

What is included in the Ascend NPU migration plan generated from the codebase?▼

The Ascend NPU migration plan includes required API changes, data movement strategies, and potential performance implications. It surfaces specific migration requirements and blockers derived from analyzing your model structure and distributed training setups.

When do I need to evaluate PyTorch model structure for Ascend NPU readiness?▼

Evaluating PyTorch model structure for Ascend NPU readiness is needed when migrating codebases with CUDA usage. The analysis identifies layers, attention blocks, and normalization usage that complicate or block migration, providing a clear view of requirements early in the process.