aoti-debug

Debug AOTInductor errors, segfaults, and CUDA illegal memory access issues.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/Billkst/Causal-TabDiff --skill aoti-debug
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aoti-debug
Source: https://github.com/Billkst/Causal-TabDiff/tree/main/.agents/skills/aoti-debug
Command: npx skills add https://github.com/Billkst/Causal-TabDiff --skill aoti-debug

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps diagnose and resolve errors and crashes encountered when using AOTInductor (AOTI), a compilation toolchain for PyTorch.

Core Features & Use Cases

  • Device and Shape Mismatch Debugging: Identifies and guides the user to fix common errors arising from incorrect device type or input shape configurations during compilation and loading.
  • CUDA Illegal Memory Access (IMA) Resolution: Provides a systematic approach to pinpoint and debug CUDA IMA errors, including the use of specific environment variables and debugging flags.
  • Use Case: When aot_load() fails with a segfault or runtime errors occur during model inference due to device mismatches, this skill provides the necessary steps to identify and correct the configuration.

Quick Start

Use the aoti-debug skill to help resolve a segfault encountered during aot_load().

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 during aot_load in PyTorch AOTInductor?

A segfault during aot_load often stems from device and shape mismatches between AOTInductor compilation and runtime inputs. You must systematically verify that runtime tensor configurations match the compiled model's expected inputs.

How do I debug a CUDA illegal memory access error in AOTInductor?

Debug a CUDA illegal memory access (IMA) by enabling specific debugging flags and environment variables. This approach systematically pinpoints the exact memory access violation occurring during AOTI model inference.

Why does my AOTInductor model crash at runtime with input device mismatches?

Runtime input device mismatches cause crashes because the loaded model expects tensors on a specific device. You must ensure the runtime device type exactly matches the device configuration used during the AOTI compilation phase.

What causes constant loading failures when using PyTorch AOTInductor?

Constant loading failures in PyTorch AOTInductor typically arise from incorrect compilation configurations. Diagnosing this requires verifying that all constant parameters are correctly aligned with the runtime environment during the aot_load process.

What are the limitations of debugging AOTInductor crashes without matching shapes?

Debugging AOTInductor crashes without matching input shapes is severely limited because shape mismatches directly cause runtime errors and segfaults. You must verify shape consistency between compilation and runtime to resolve these crashes.