[ExecuTorch][WebGPU] Fuse the SwiGLU elementwise chain

[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

The SwiGLU feed-forward activation lowered to separate gate and up projections
followed by a sigmoid and two elementwise multiplies, launching several
dispatches over the same tensors. This fuses the sigmoid and the two multiplies
into a single dynamically-resized 2D dispatch that computes
(gate * sigmoid(gate)) * up in one pass. Fusion applies only when the pattern
is exact and its scratch is planner-owned; graph outputs, extra consumers, and
separate inputs fall back to the unfused chain, and the landed broad-QKV
behavior is left untouched until its dedicated corrective diff. No Vulkan
analogue: the Vulkan backend has no fused SiLU/SwiGLU shader and composes it
from a sigmoid unary op plus an elementwise multiply at graph level.

Key changes:

• runtime/ops/mul/silu_mul_fused.wgsl (+ generated header): one-thread-per-
  element SiLU-gated multiply of gate and up into a single output.
• WebGPUGraph.cpp: detect the exact gate/up/sigmoid/mul pattern, own the gate
  scratch, size the 2D dispatch, and fall back conservatively.
  @exported-using-ghexport

Differential Revision: D113171742

Differential Revision: D113171742

[pytorch-bot]

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