Individual skill
build-triton-ascend-padded-moe-routing
Build padded MoE gather, scatter, and router-weight-gradient kernels for Triton-Ascend using real cumulative bins, padded cumulative bins, UB slice assembly, and vector-core-aware tiling. Use when an agent ports MegaBlocks padded routing, aligns each expert's token segment to a fixed multiple, restores weighted top-k outputs, or maps gradients through padded expert storage.
skills/build-triton-ascend-padded-moe-routing/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- Last checked
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- 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 Padded MoE Routing
## Goal
Store every expert's real assignments in a separately padded segment, preserve zero-filled gaps, and implement inverse scatter and per-assignment weight gradients.
## Workflow
1. Sort assignments by expert to obtain aligned indices and nondecreasing bin_ids.
2. Build bins from real cumulative counts.
3. Round each expert count to the required padding multiple and build padded_bins from cumulative padded counts.
4. Map sorted position i to offset_in_expert = i - bins[e-1] and padded row = padded_bins[e-1] + offset_in_expert.
5. Gather token indices[i] // TOP_K into that padded row.
6. Scatter real rows back through unique assignment IDs and compute wgrad with the same mapping.