gated linear layers

**Gated linear layers** is the **module pattern where a linear transform is modulated by a learned gate branch before output** - it provides fine-grained control over feature flow and supports richer nonlinear behavior than plain linear blocks. **What Is Gated linear layers?** - **Definition**: Two projection branches where one branch generates features and the other generates gate values. - **Combination Rule**: Output is produced by elementwise multiplication between feature activations and gate activations. - **Activation Options**: Gate branch can use sigmoid, GELU, Swish, or related nonlinear functions. - **Transformer Usage**: Common inside modern feed-forward blocks and specialized conditioning modules. **Why Gated linear layers Matters** - **Selective Pass-Through**: Gates suppress irrelevant features and amplify useful context signals. - **Expressive Capacity**: Multiplicative interactions improve function class compared with additive-only blocks. - **Training Stability**: Controlled feature scaling can improve optimization in deep stacks. - **Model Efficiency**: Better information filtering can raise quality at similar parameter counts. - **Design Flexibility**: Gate formulation can be adapted for dense and sparse architectures. **How It Is Used in Practice** - **Block Integration**: Replace standard activation MLP with gated modules in target model layers. - **Kernel Fusion**: Optimize projection, bias, activation, and gating multiply in efficient epilogues. - **Ablation Analysis**: Measure convergence speed and final accuracy against non-gated baselines. Gated linear layers are **a practical architecture upgrade for transformer feed-forward modeling** - they improve feature routing while preserving implementation simplicity.