aoti-debug
Debug AOTInductor (AOTI) errors and crashes. Use when encountering AOTI segfaults, device mismatch errors, constant loading failures, or runtime errors from aot_compile, aot_load, aoti_compile_and_package, or aoti_load_package.
Install
With your agent
Install OMGSkills skill "pytorch/pytorch:aoti-debug" into this agent's normal skills directory: use the OMGSkills MCP get_skill tool, install only the pinned_install commit and path, verify SKILL.md against pinned_install.skill_md_sha, and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/pytorch/pytorch /tmp/pytorch && ln -s /tmp/pytorch/.agents/skills/aoti-debug ~/.claude/skills/aoti-debug
From README
AOTI Debugging Guide This skill helps diagnose and fix common AOTInductor issues. Error Pattern Routing Check the error message and route to the appropriate sub-guide: Triton Index Out of Bounds If the error matches this pattern: → Follow the guide in triton-index-out-of-bounds.md All Other Errors Continue with the sections below. --- First Step: Always Check Device and Shape Matching For ANY AOTI error (segfault, exception, crash, wrong output), ALWAYS check these first: Compile device == Load device: The model must be loaded on the same device type it was compiled on Input devices match: Runtime inputs must be on the same device as the compiled model Input shapes match: Runtime input shapes must match the shapes used during compilation (or satisfy dynamic shape constraints) If any of these don't match, you will get errors ranging from segfaults to exceptions to wrong outputs. Key Constraint: Device Type Matching AOTI requires compile and load to use the same device type.
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