Contract-first design and coverage discipline for FLA kernel and numerical changes. Use before implementation to define supported cells, numerical budgets, dispatch semantics, compatibility, tests, and benchmarks.
Checklist and workflow for preparing an MR/PR in the FLA repo. Covers CONTRIBUTING.md compliance, test plan, benchmark evidence, and PR body structure.
Guidelines for Ascend NPU kernel / Triton-Ascend backend performance work in the FLA repo. Covers profiling with torch_npu, PipeUtilization/MemoryUB CSV analysis, Cube/Vector/MTE/UB bottleneck diagnosis, and kernel optimization (UB tiling, grid splits,…
Disciplined, reproducible loop for making an FLA kernel faster (Triton, Gluon, TileLang, CuTe) without ever breaking or gaming correctness. Synthesizes the task-contract / three-phase / iteration-protocol / silent-bug-catalog discipline of agent…
Workflow for porting an existing Triton kernel in `fla/ops/**` to Gluon (`triton.experimental.gluon`) to gain explicit control over tensor layouts, shared memory, async data movement (cp.async / TMA), MMA (WGMMA / tcgen05), and scheduling (persistent kernels,…
Guidelines for NVIDIA GPU kernel / Triton / Gluon / TileLang / CUDA backend performance work in the FLA repo. Covers profiling workflow, hardware baselines, and MR-ready performance evidence requirements. Uses an installed ncu-report-skill when a task needs…
Guidelines for kernel correctness testing and coverage in fla/ops/** and related modules, including common Triton grid/addressing pitfalls. Helps decide what tests to add or run before an MR.
Workflow for FLA backend dispatch decorators and backend implementations. Use when touching fla.ops.backends, @dispatch-decorated functions, BaseBackend subclasses, backend verifier methods, backend env vars, or backend tests.