Individual skill
build-triton-ascend-moe-gather-scatter
Build Ascend-friendly MoE gather, scatter, and router-weight-gradient kernels with vector-core-sized grids, UB-aware row/column tiling, and CANN slice extensions. Use when an agent ports MegaBlocks-style unbinned token routing to Triton-Ascend, reorders top-k token assignments, combines weighted expert outputs, or computes per-route weight gradients.
skills/build-triton-ascend-moe-gather-scatter/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- Last checked
- Nov 4, 2026
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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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# Build Triton-Ascend MoE Gather and Scatter
## Goal
Implement the unbinned routing triplet over assignment IDs: gather token rows into routing order, scatter weighted rows back to tokens, and compute one router-weight gradient per assignment.
## Workflow
1. Define L = tokens * TOP_K and require indices to be a permutation of assignment IDs in [0, L).
2. Gather routed row i from token row indices[i] // TOP_K.
3. Scatter routed row i into assignment slot indices[i], optionally scale by weights[indices[i]], then reduce the TOP_K slots per token.
4. Compute wgrad[indices[i]] as the float32 dot product of routed row i and grads[indices[i] // TOP_K].
5. Split L approximately evenly across the target vector cores.
6. Process SUB_BLOCK_SIZE assignments and BLOCK_X feature columns at a time.