[gemma4_31b][cuda] length-aware bf16 global attention + head_dim-agnostic prefill autotune

- Global (full-attention) bf16 layers: bound SDPA to a runtime kv_len scalar
  (CUDA-graph-safe) instead of the full max_seq_len KV buffer -> O(context)
  decode; restores decode scaling (was flat ~36.5 t/s at all depths ->
  46.5@512, 34.9@127K). (sdpa.py kv_len path + cuda_source_transformations.py
  _lenaware_attention_forward; global layers only, sliding + turbo untouched)
- Prefill global full-attention: replace fixed m32/m64 BLOCK_M selection with a
  head_dim-keyed autotuned _sdpa_fwd_kernel + register-budget prune
  (BLOCK_M*HEAD_DIM <= 4096*num_warps), fixing acc[64,512] fp32 register spill
  at head_dim=512. Prefill +24% @8K, +63% @32k, +117% @127k; head_dim-agnostic
  (no split-D needed for D<=512). (sdpa.py)
- Runner: add --tokens_file (pre-tokenized input) and --ignore_eos (fixed decode
  length) for benchmarking. (main.cpp)
- Validated: output bitwise-identical to prior kernel (cos=1.0, D=64/128/256/512),
  no decode regression; non-tq prefill now beats llama.cpp at all 5 cells and
  turbo TQ4 at 4/5. Op-level autotune profiling (A100) confirms the config set is
  near-optimal (in-set optimum at every regime; only <=1.3% marginal candidates).

Author: gasoonjia