KernelBench hard · RTX PRO 6000

Paged Attention GPT-5.6 Sol

manually audited: clean

Real Triton split-KV paged-attention decode. Main kernel maps one program to (batch, kv_head, split), loads whole GQA query group per program so each packed K|V cache line is fetched once, streams pages via block_table with a page-boundary broadcast trick (one table load per page, broadcast to its 16 token lanes), online softmax in exp2 domain, cache_modifier=".cg" on KV loads, then a per-query-head reduction kernel combines split partials via logsumexp. A second specialized kernel for the D=64 shape packs all four KV heads into one block-diagonally-masked MMA tile to fill the 16-row tensor core tile. No caching, no CUDA graphs, no input-identity paths: forward launches fresh kernels every call. No forbidden ops (no SDPA / flashinfer / vllm; flashinfer strings in the trace are only reads of problem.yaml and the deck's own sota.py). template_mutated=false; graded numbers physically plausible (703-1681 GB/s, all under H100 peak).

20260721_142512_codex_gpt-5.6-sol_03_paged_attention