dist-op-analysis
OfficialAnalyze and map distributed ops for HyperParallel.
Software Engineering#sharding#dtensor#distributed-ops#op-analysis#interface-signatures#layout-inference
Authormindspore-ai
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This SKILL provides read-only analysis for HyperParallel distributed operator development. Given a MindSpore mint or PyTorch op name, it explores framework source code to extract interface specifications, Primitive/ATen mappings and HyperParallel layout derivation logic to support dist-op-dev workflows. It is internal and not intended for direct user invocation.
Core Features & Use Cases
- Read actual source to extract full interface signatures, including parameter names, defaults and constraints for MindSpore mint and PyTorch ops.
- Trace distributed implementation details and sharding strategies via YAML configs, mapping to HyperParallel's layout derivation logic.
- Support workflow automation by feeding interface data, layout data, and expand logic into dist-op-dev pipelines for automated analysis.
Quick Start
Invoke the dist-op-dev workflow with a framework op name to generate its interface and layout analysis.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: dist-op-analysis Download link: https://github.com/mindspore-ai/hyper-parallel/archive/main.zip#dist-op-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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