v1.2.0 — decode optimization cycle

A decode-focused optimization cycle on top of v1.1.0, for Gemma 4 E2B on
Jetson Orin Nano Super. No correctness change (greedy output unchanged) and
no change to the Qwen3 path — every optimization is env-gated (JLLM_*=0).

What's in it

• perf(rope): precomputed cos/sin table — no per-decode-step trig.
• perf(attn): warp-parallel Q·Kᵀ + block softmax in the decode attention kernel.
• perf(gemv): dp4a triple QKV GEMV (Q4_K+Q4_K+Q6_K), one shared q8_1 activation.
• perf(kernels): half2-vectorized vec_add / RMSNorm / GeGLU / SwiGLU / scale.
• perf(engine): Gemma 4 CUDA-graph support — inert for E2B (20 shared-KV
  layers → graph correctly skipped); benefits only a non-shared-KV variant.
• fix(engine): decode-graph skip messages now print once (were per-token).
• chore(bench): scripts/bench_v11x.sh decode profiler.

Measured (Orin Nano Super, gemma-4-E2B-it-Q4_K_M, MAXN_SUPER)

Same-conditions decode A/B (v1.1.0 vs v1.2.0 binaries, identical prompt, 3
interleaved runs each, ≤0.3 tok/s variance), at three context depths:

Context depth (prompt len)	v1.1.0	v1.2.0	Δ
shallow (~11 tok)	29.1	31.0	+6.5 %
~450 (421 tok)	22.1	24.4	+10.4 %
512 (511 tok)	21.1	23.8	~+12 %

The decode gain grows with context depth — the warp-parallel decode
attention does more work per token as the KV cache grows, so it saves more
there. The 512-depth row is the apples-to-apples comparison with v1.1.0's
"23.5 tok/s at parity" methodology (v1.1.0 reads 21.1 here on
jetson_clocks-unlocked MAXN_SUPER). The gain comes from the attention kernel,
the dp4a QKV triple, and the half2 kernels; the RoPE table and the Gemma 4
graph path contribute negligibly for E2B.

Full Changelog: v1.1.0...v1.2.0