Individual skill
validate-triton-ascend-accuracy
Validate Triton-Ascend kernel outputs against PyTorch references with dtype-aware tolerances, exact integer checks, bfloat16 promotion, and boolean handling. Use when an agent writes or reviews NPU kernel tests, needs a reusable accuracy-comparison helper, or must choose an initial comparison policy for float16, bfloat16, float32, integer, or boolean outputs.
skills/validate-triton-ascend-accuracy/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- Observed behavior
- Instructions only
- Last checked
- Nov 4, 2026
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Repository context
- Repository stars (not skill ratings)
- 189
- Repository last updated
- Aug 15, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- 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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# Validate Triton-Ascend Accuracy
## Goal
Make correctness checks explicit and consistent across Triton-Ascend kernel tests.
## Workflow
1. Compute the PyTorch reference from the same inputs before interpreting performance.
2. Assert identical output shape and dtype.
3. Compare floating-point tensors with dtype-aware tolerances.
4. Compare integer and boolean tensors exactly.
5. Add edge shapes, tail masks, zeros, extreme values, and NaNs when the operator semantics allow them.
