- Double-buffered KV (STAGES=2) for D<=128: next tile cp.async overlaps current tile MMA compute, hiding global load latency
- Q loaded directly from global into mma A-operand registers, removing sQ staging and prologue syncwarp
- Predicated cp.async unifies full and partial tile paths, eliminating scalar fallback branch
- STAGES=1 fallback for D=256 (double-buffer would exceed smem budget)
- Applied to both contiguous and paged decode MMA kernels
- ~1.27x average speedup on L20 (sm_89), zero precision loss
- Add attn_paged_decode wrapper in ops.py with gather fallback
- Register kernel in loader.py and export from __init__.py
- Extract test_utils.cuh shared by all attention unit tests
- Rename attn_paged_vs_contiguous.cu to attn_paged_decode_test.cu
- Refactor decode/prefill tests to use common bf16 helpers and cpu ref
- Fix k_cache dim check in attn_paged_decode.cu
- PagedAttentionParams merged into attn_common.h
- Scalar variant: warp-per-query-head split-KV, resolves page table per-position for the K/V shared-memory tile load
- MMA variant (sm_80+): tensor-core head-packing with cp.async, single page-table lookup per tile (BC=32 fits within page_size>=32)
- Standalone test: 14 cases across head_dim 32/64/128/256, GQA, multi-batch, both paths verified against CPU reference
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