Individual skill
tune-triton-ascend-kernels
Add standard or advanced autotuning to Triton-Ascend kernels, define shape keys and candidate meta-parameters, and structure kernels so automatic split and tiling analysis can recognize their axes. Use when an agent needs to benchmark block-size or multibuffer choices, use configs=[] for generated vector-kernel candidates, or diagnose why Triton-Ascend autotune cannot infer a tunable axis.
skills/tune-triton-ascend-kernels/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- Instructions only
- Last checked
- Nov 4, 2026
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- Repository last updated
- Aug 15, 2026
- License
- Apache-2.0
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- ai Repository topic
- ascend Repository topic
- ascend-npu Repository topic
- npu Repository topic
- triton Repository topic
- triton-ascend Repository topic
- claude-code Repository topic
- codex Repository topic
- opencode Repository topic
- engineering Derived category
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# Tune Triton-Ascend Kernels
## Goal
Select a correct kernel configuration for each relevant input shape without hardcoding one block size for all workloads.
## Workflow
1. Establish a correct untuned kernel and reference test.
2. Choose standard autotune when candidate configurations are known.
3. Choose advanced autotune when Triton-Ascend should infer vector split or tiling candidates.
4. Put only values that can change the winner in key.
5. Derive the launch grid from meta-parameters and omit tunable meta-parameters at launch.
6. Validate the tuned result, then benchmark representative shapes.