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aoti-debug

Diagnose and fix AOTInductor errors with structured device and shape validation.

1|Updated Apr 5, 2026
Search Tags:#debugging#pytorch#cuda#segfault#aoti#runtime-error#aotinductor
One-click install
npx skills add https://github.com/steleman/pytorch-cuda-2.11.0 --skill aoti-debug-steleman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aoti-debug
Source: https://github.com/steleman/pytorch-cuda-2.11.0/tree/main/.claude/skills/aoti-debug
Command: npx skills add https://github.com/steleman/pytorch-cuda-2.11.0 --skill aoti-debug-steleman

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps diagnose and fix common AOTInductor issues, including segfaults, device mismatch errors, constant loading failures, or runtime errors from aot_compile, aot_load, or aoti_compile_and_package, by guiding structured checks and debugging workflows.

Core Features & Use Cases

  • Structured device/shape checks to ensure compile and load device alignment, input device matching, and dynamic shapes handling.
  • Common error pattern guidance covering device mismatch, segmentation faults, and constant loading failures with recommended remedies.
  • Deterministic CUDA IMA debugging guidance and environment flag recommendations to reproduce and triage issues.
  • Use cases: diagnosing AOTInductor crashes in CPU/GPU pipelines, stabilizing model loading, and ensuring correct device contexts for aot workflows.

Quick Start

Follow the step-by-step checks to verify device, shapes, and inputs, then run the AOTInductor debugging workflow to identify and fix the issue.

Frequently Asked Questions about aoti-debug

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

FAQPage Schema
How do I fix a segfault when loading an AOTInductor compiled model?▼

To fix an AOTInductor segfault, run structured device and shape validation across aoti_load_package, verify compile and load device alignment, and apply recommended environment flags for deterministic CUDA debugging.

Why does my PyTorch AOTInductor model throw a device mismatch error at runtime?▼

AOTInductor device mismatch errors occur when compile and load device contexts differ. Run structured device checks to ensure compile and load device alignment and verify input device matching across aoti_load_package.

How do I debug constant loading failures in aoti_load_package?▼

Debug constant loading failures in aoti_load_package by following the structured troubleshooting workflow that checks error patterns, validates device contexts, and applies recommended remedies for CPU and CUDA contexts.

Can I use AOTInductor debugging steps for both CPU and CUDA pipelines?▼

Yes, AOTInductor debugging steps apply to both CUDA and CPU contexts, covering segfaults, device mismatches, and runtime exceptions across aot_compile, aot_load, and aoti_compile_and_package workflows.

What environment flags do I need to reproduce AOTInductor runtime errors deterministically?▼

To reproduce AOTInductor runtime errors deterministically, apply recommended environment flags that enable deterministic CUDA IMA debugging, then run structured device and shape validation to triage the issue.

How do I handle dynamic shapes when compiling with aoti_compile_and_package?▼

Handle dynamic shapes with aoti_compile_and_package by running structured shape validation checks that ensure compile and load shape alignment and verify input shape handling across the AOTInductor workflow.

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