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dist-op-analysis

Extract interface signatures, Primitive/ATen mappings, and sharding strategies for MindSpore mint and PyTorch ops.

7|3|Updated Feb 27, 2026
Search Tags:#sharding#dtensor#distributed-ops#op-analysis#interface-signatures#layout-inference
One-click install
npx skills add https://github.com/mindspore-ai/hyper-parallel --skill dist-op-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dist-op-analysis
Source: https://github.com/mindspore-ai/hyper-parallel/tree/main/.claude/skills/dist-op-analysis
Command: npx skills add https://github.com/mindspore-ai/hyper-parallel --skill dist-op-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about dist-op-analysis

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

FAQPage Schema
How do I extract interface signatures and sharding strategies for distributed ops?▼

To extract interface signatures and sharding strategies for distributed ops, read the framework source code and HyperParallel YAML mapping files to compute infer_layout and get_expand_impl logic for MindSpore mint and PyTorch ops.

How does DTensor layout inference work for PyTorch distributed operators?▼

DTensor layout inference for PyTorch distributed operators works by tracing Primitive/ATen mappings and sharding strategies via YAML configs to map to HyperParallel's layout derivation logic and derive compatible layouts.

Can I analyze MindSpore mint ops for HyperParallel distributed development?▼

Yes, you can analyze MindSpore mint ops for HyperParallel distributed development by exploring the framework source to extract full interface specifications, parameter defaults, and layout derivation logic.

What do I need to compute infer_layout and get_expand_impl logic for a framework op?▼

To compute infer_layout and get_expand_impl logic for a framework op, you need access to the framework source code and the HyperParallel YAML mapping files to extract interface data and trace sharding strategies.

Why do I need YAML mapping files for distributed operator analysis?▼

You need YAML mapping files for distributed operator analysis because they contain the distributed implementation details and sharding strategies required to map framework ops to HyperParallel's layout derivation logic.

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