architecture-analysis

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.

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.

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.

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.

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.

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.