aiter

AI code review for aiter PRs. Catches perf regressions, silent correctness bugs, dispatch gate holes, and AI-generated code patterns. Invoke with a PR number; works through fetch → semantic understanding → rule checklist → verdict. Add new rules here as patterns emerge from real reviews.

How to add/upload tuned config CSVs under aiter/configs (incl. model_configs/) without introducing duplicate shapes, and how to find & resolve duplicate-shape collisions. Use whenever adding a model's tuned config, merging/uploading config CSVs, editing anything under aiter/configs/**, or when a run hits "duplicate shape entries during merge".

Standard structure for aiter op_tests under op_tests/test_*.py — @benchmark + run_perftest candidate loop, a torch reference, a final markdown summary table, a __main__ guard so the module is importable, and faithful reproduction of the real model call (output buffer, layout, shapes). Use whenever writing, rewriting, or extending any aiter unit/perf test, or adding model-derived shapes (e.g. DeepSeek-V4) to an existing one.

Module-level JIT build-wall optimization for opus-based aiter modules. Use when an aiter JIT module's first-call build wall is a user-visible bottleneck or when adding a new module.

Compile-time optimization guidance for HIP/C++ kernels using opus.hpp. Use when writing or reviewing OPUS kernels, analyzing compile time, reducing template instantiation overhead, or optimizing hipcc build performance.