fla-ascend-performance

fla-ascend-performance is an MLOps & model serving skill for AI agents, published by fla-org in the flash-linear-attention repository on GitHub. Skillspace rates fla-ascend-performance D (62/100) and it passed the safety scan; the rating is based on static analysis only.

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, fusion/split, varlen, G_T_CONTIG gate loading, constexpr DMA-path split / TAIL_MODE, extract_slice, MTE OOB, int32 address overflow, tl.cast vs constexpr .to, make_block_ptr int32 offsets, correctness gates, tl.dot left-operand clobber). NPU kernels must not use num_warps/num_stages. Per-kernel catalog: references/cases.md (incl. causal_conv1d core-grid). Use when working on NPU profiling, kernel_details/op_statistic, aic_metrics, fla triton_ascend backends (ops or modules), g transpose stride-1, UB overflow, dual-path DCE, grid limits, int64 pointer math, tl.dot reuse, or Ascend performance.

Grade
D
Quality
62 / 100
Safety
Passed
GitHub stars
5,743

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubfla-org/flash-linear-attention

Our review

Architecture
78 / 100
Maturity
Structured: ships references or scripts
Security scan
No issues found
License
Not declared. Check the repository before reusing it.

Not checked yet

  • Category is a classifier prediction; no model has read this skill yet
  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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