Summary:
Fuse each gemma4_31b MLP's gate_proj|up_proj into a single
[2*intermediate, hidden] coalesced-int4 matmul, applied by default in the CUDA
export. This issues one activation-quant + one W4A8 matvec per layer instead of
two, cutting per-token launch + activation-quant overhead in the launch-bound
decode path. Only Q4_K (CudaCoalescedInt4Tensor) gate/up pairs are fused; any
other quant type (e.g. Q6_K) is left as two matmuls (guarded, still correct).

Builds on the already-landed kv_len-bounded tq4_sdpa kernel + gemma4_31b
call-site (kv_len + mask_is_causal), which recovered 128k decode from ~2.8 to
~43 tok/s. With both, ET gemma4_31b 128k+TurboQuant decode beats llama.cpp at
every measured context (cuda_graph ON):

  ctx    ET      llama
  512    44.80   42.77
  2K     43.20   41.97
  8K     42.23   41.23
  32K    41.64   40.27
  127K   38.41   35.97

TurboQuant KV compression kept; prefill restored (6-8x) with no regression;
output quality preserved.

Test Plan:
- Fusion numerics: fused vs unfused MLP through the real W4A8 int4_plain_mm
  kernel = bit-exact (max_abs_diff 0.0, cos 1.000000) for decode (T=1) and
  prefill (T=4).
- Export + run: fused module exported via CudaPartitioner and executed through
  executor_runner (RC=0, cos 0.999915 vs eager). Full 31B export logs
  "Fused gate+up on 60 MLP layers".
- Decode A/B (gemma4_31b 128k+TQ, cuda_graph ON, 5x median): table above; beats
  llama.cpp at 512 -> 127K. nsys: tq4_sdpa 91.7% -> 2.9% of decode.