Transformer gate architecture — SwiGLU, GeGLU, MoE gating, attention gates

Why architecture matters here

Gate failures come from wrong numerics + missing fusion. Architecture matters because gated designs dominate modern LLMs for a reason.

The architecture: every piece explained

The top strip is FFN forms. FFN input. GeLU FFN. Gated FFN (SwiGLU). Down-proj.

The middle row is other gates. MoE routing gate. Attention gates. Numerics. Kernel fusion.

The lower rows are practice. Empirical wins. Metrics. Ops — model choice + init + eval.

End-to-end flow

End-to-end: SwiGLU FFN block: input x; up-proj to gate + value; SiLU(gate) ⊙ value; down-proj. Fused kernel executes; fp32 accumulate for gate sigmoid. Loss curve smooth; quality beats GeLU baseline.