[ExecuTorch][WebGPU] Add Qwen3 K16 attention and fp16 KV cache

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Preserve Linear and Q4 embedding params across dynamic resize #21483
• [ExecuTorch][WebGPU] Generate logical and arithmetic binary shader variants #21451
• [ExecuTorch][WebGPU] Generate extrema and unary shader variants #21450
• [ExecuTorch][WebGPU] Generate typed to_copy shader variants #21449
• [ExecuTorch][WebGPU] Make WGSL generation rollback-safe #21448
• -> [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

Qwen3's attention geometry differs from Llama's, and its KV cache is produced in
fp32 on the host but must be consumed in fp16 on the device. This adds guarded
Qwen3 K16 streaming online-softmax attention schedules — a Q16 schedule that is
the automatic default whenever the exact geometry and capability guards pass,
plus a Q32 candidate that is opt-in through the sdpa_query_tile runtime spec
(BackendOption) for future autotuning — together with the exact fp32-host to
fp16-device KV-cache boundary conversion. Selection requires the exact Qwen3
geometry, fp16 KV storage, adapter limits, a valid workgroup count, and an exact
2:1 byte ratio; the established Llama, materialized, and FlashDecoding routes
remain fallbacks. This builds on the HuggingFace rotate-half
RoPE operator. No Vulkan analogue (WebGPU-specific online-softmax attention;
Vulkan has only a materialized attention). It also makes the long generated WGSL
provenance and constant declarations format-stable and covers them with a
generator regression test.

Key changes:

• runtime/ops/sdpa/streaming_attention_qwen3_k16_causal_bound.wgsl and
  streaming_attention_qwen3_q32_k16_causal_bound.wgsl (+ generated headers): the
  Q16 and Q32 online-softmax Qwen3 kernels.
• Sdpa.cpp, WebGPUGraph.{cpp,h}: exact Qwen3 geometry and limit guards, Q16
  default route selection, and the fp32-host to fp16-device KV-cache conversion.
• WebGPUBackend.cpp: read the optional sdpa_query_tile runtime spec and thread
  it into graph build so the Q32 tile can be requested without a rebuild.
• scripts/gen_wgsl_headers.py (+ test_wgsl_codegen.py): format-stable generated
  headers with a regression test.
  @exported-using-ghexport

Differential Revision: D113171744

Differential Revision: D113171744

[pytorch-bot]

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✅ You can merge normally! (1 Unrelated Failure)

As of commit 22989c7 with merge base f4a21b8 ():

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• pull / test-qnn-testsuite-linux / test-backend-linux (qnn, models) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

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[psiddh]

There are linter errors that needs to befixed