Individual skill
build-triton-ascend-layer-norm
Build a fused forward LayerNorm kernel for Triton-Ascend with row-wise mean and variance reductions, float32 accumulation, affine weight and bias, and masked feature tiles. Use when an agent needs LayerNorm over the last dimension on Ascend NPU or needs a reusable two-pass reduction pattern for normalization kernels.
skills/build-triton-ascend-layer-norm/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- 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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# Build Triton-Ascend LayerNorm
## Goal
Implement the forward equation y = ((x - mean) / sqrt(var + eps)) * weight + bias over the final tensor dimension.
## Workflow
1. Reshape the input logically to M by N, where N is the normalized dimension.
2. Allocate output with the original shape and allocate optional mean and reciprocal-standard-deviation buffers as float32 vectors of length M.
3. Launch one program per row.
4. Accumulate the mean in float32 across masked BLOCK_SIZE tiles.
5. Make a second pass for variance, compute rstd, then make a third pass to normalize and apply weight and bias.
