v1.3.0 — split-K decode attention (beats llama.cpp at depth)

Split-K (flash-decoding) decode attention — fixes genie's decode-at-depth
collapse and pushes decode past llama.cpp at every context depth.

The decode-attention kernels launched grid=(n_heads) — only 8 blocks on the
8-SM Orin (ncu: ~11 % occupancy, 3 % throughput), and each per-head block
walked the whole KV serially, so decode throughput fell with depth. Split-K
splits the KV range across n_splits blocks per head, each computing an
online-softmax partial; a reduce kernel combines them per head. Default-on
(JLLM_SPLITK=0 reverts).

Decode-at-depth vs llama.cpp (same Orin, same gemma-4-E2B-it-Q4_K_M, MAXN_SUPER)

Context depth	v1.2.1 (no split-K)	v1.3.0	llama.cpp tg	v1.3.0 vs llama
~512	25.7	31.8	20.1	1.58×
~1024	19.7	30.5	20.8	1.47×
~2048	14.2	26.8	20.4	1.31×
~4096	8.6	21.0	19.1	1.10×

Without split-K genie was 2.2× behind llama.cpp at 4096 (8.6 vs 19.1); with
it genie leads at every depth. Shallow decode is unchanged (the single-block
kernel is kept below 64 tokens).

Details

• flash_decode_splitk_partial_kernel + flash_decode_reduce_kernel, wired into
  both the per-step and CUDA-graph decode paths (the graph bakes n_splits at
  capture; the kernel derives each split's KV range from *d_pos at replay).
• Handles FP16 + INT8 KV, Gemma's cache_head_dim sliding layers, and the
  sliding-window mask. Numerically exact vs the single-block kernel (standalone
  tests/test_flash_decode_splitk.cu: max abs diff 0.0, 3.2–7.3× at seq 512–4096).

Full Changelog: v1.2.1...v1.3.0