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flash-linear-attention
flash-linear-attention contient 7 skills collectées depuis fla-org, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
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, warp specialization). Covers when a port is worth it, an incremental porting sequence that keeps numerical parity at every step, a Triton-to-Gluon API mapping, compile-time / autotune / smem-budget management for heavily unrolled kernels, and a pitfall checklist (proxy fences, mbarrier semantics, layout costs, bitwise-cancellation traps, NaN-poisoned OOB handling). Use when a Triton kernel is register-bound, when `num_stages` pipelining underperforms, or when Hopper/Blackwell features (TMA, TMEM, tcgen05) are needed.
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 kernel-optimization frameworks (KDA, the MLSys FlashInfer contest workflow, AKO4ALL/AKO4X), and anchors all of it on FLA's frozen pytest (forward AND backward, under NaN poisoning) as the immutable correctness gate. Use when iterating on `fla/ops/**` performance over multiple rounds.
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 detailed Nsight Compute collection and diagnosis.
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.
FLA KDA kernel workflow and public technical notes. Use when modifying or reviewing fla/ops/kda/**, KDA gate modes, chunk intra/inter kernels, safe_gate behavior, KDA backends, or KDA-specific 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.