[gemma4_31b][cuda] length-aware bf16 global attention + head_dim-agno…

[Gasoonjia]

…stic 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_MHEAD_DIM <= 4096num_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).

[pytorch-bot]

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❌ 3 New Failures, 1 Pending, 3 Unrelated Failures, 3 Unclassified Failures

As of commit ce442fe with merge base 1b726b2 ():

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• pull / unittest / linux / linux-job (gh)
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• pull / unittest-editable / linux / linux-job (gh)
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  /__w/executorch/executorch/pytorch/executorch/backends/apple/coreml/runtime/inmemoryfs/inmemory_filesystem.cpp:722:48: error: ‘inmemoryfs::InMemoryFileSystem::InMemoryNode::Kind’ has not been declared
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👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / test-llama-runner-qnn-linux (fp32, qnn_16a16w, qnn) / linux-job (gh) (trunk failure)
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