Individual skill
build-triton-ascend-fused-attention
Build and adapt a FlashAttention-v2-style fused forward attention kernel for Triton-Ascend using tiled Q/K/V block pointers, online softmax, causal staging, and float32 accumulation. Use when an agent needs forward scaled dot-product attention in BNSD layout on Ascend NPU, must support causal or non-causal masking, or must handle large head dimensions without overflowing on-chip memory.
skills/build-triton-ascend-fused-attention/SKILL.md
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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 Fused Attention
## Goal
Compute softmax(QKᵀ * scale)V without materializing the full attention matrix. Support BNSD tensors shaped Z by H by N_CTX by HEAD_DIM.
## Workflow
1. Validate matching Q, K, and V shapes, compatible strides, supported dtype, and HEAD_DIM in {16, 32, 64, 128, 256}.
2. Choose BLOCK_M for query rows and BLOCK_N for key/value rows. Require N_CTX to be divisible by both in the compact implementation.
3. Map each task to a batch, head, and query-block tuple and create block pointers for Q, K, V, and output.
4. Load one Q block and stream K/V blocks through an online-softmax inner loop.
5. For causal attention, process preceding blocks and the diagonal block as separate stages; apply the triangular mask only to the diagonal stage.
6. Normalize the accumulated output by the running denominator and store it in the output dtype.