Individual skill
optimize-triton-ascend-int-vectors
Optimize integer vector kernels for Triton-Ascend by preferring int32 arithmetic when the value and index ranges permit it. Use when an agent ports an int64 GPU elementwise kernel to Ascend NPU, observes scalarized integer arithmetic, or designs vector addition, subtraction, or reduction over integer tensors.
skills/optimize-triton-ascend-int-vectors/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- Aug 15, 2026
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- 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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# Optimize Triton-Ascend Integer Vectors
## Goal
Keep integer elementwise work on efficient Ascend vector paths without changing numerical semantics.
## Workflow
1. Determine the minimum signed range required by inputs, offsets, intermediates, and outputs.
2. Use torch.int32 tensors and int32-compatible scalar arguments when every possible value fits.
3. Keep pointer arithmetic and tail masking separate from the arithmetic dtype decision.
4. Launch a masked one-dimensional kernel and compare it with an int32 PyTorch reference.
5. Benchmark int32 and int64 only after synchronizing the NPU around timed regions.
