[ExecuTorch][WebGPU] Add Llama K16 online causal attention

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache #21136
• [ExecuTorch][WebGPU] Add HuggingFace rotate-half RoPE #21135
• [ExecuTorch][WebGPU] Repair dynamic SDPA routing, add attestation #21134
• [ExecuTorch][WebGPU] Select emdawnwebgpu for Emscripten builds #21133
• -> [ExecuTorch][WebGPU] Add Llama K16 online causal attention #21132
• [ExecuTorch][WebGPU] Replace broad QKV fusion with BK64 kernel #21131
• [ExecuTorch][WebGPU] Route exact prefill shapes to the BK64 GEMM #21130
• [ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain #21129
• [ExecuTorch][WebGPU] Coordinate dynamic dispatch routes #21128
• [ExecuTorch][WebGPU] Add certified scoped output suppression #21127

Materializing the full attention-weight matrix for Llama prefill is memory- and
bandwidth-heavy and does not scale to longer sequences. This adds a single-pass
online-softmax K16 causal-attention kernel for the exact Llama Hq32/Hkv8/G4/D64
geometry with fp16 KV, guarded by explicit adapter limits, so accepted prefill
shapes through S512 never materialize attention weights. S1 keeps the existing
FlashDecoding path, and unsupported geometry, storage, or capabilities fall back
to the materialized implementation. No Vulkan analogue (WebGPU-specific): the
Vulkan backend has only a materialized attention (compute the weights, then a
separate multi-pass softmax), with no online-softmax kernel.

Key changes:

• runtime/ops/sdpa/streaming_attention_k16_causal_bound.wgsl (+ generated
  header): the online-softmax single-pass causal kernel.
• Sdpa.cpp: exact Llama geometry and capability guards, S512 transition
  handling, and fallback to the materialized route.
  @exported-using-ghexport

Differential Revision: D113171738

Differential Revision: D113171738

[pytorch-bot]

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