Skip connections (residual connections) are essential components in transformer models that solve the vanishing gradient problem by creating direct paths for information flow. They enable training of deep networks by adding input directly to sublayer outputs, allowing models to learn residual functions rather than complete transformations. The post explains two architectural variants: post-norm (normalization after residual connection) and pre-norm (normalization before sublayer), with pre-norm being preferred for modern large models due to better training stability and faster convergence, despite post-norm potentially achieving slightly better performance when successfully trained.
Table of contents
OverviewWhy Skip Connections are Needed in TransformersImplementation of Skip Connections in Transformer ModelsPre-norm vs Post-norm Transformer ArchitecturesFurther ReadingsSummaryLearn Transformers and Attention!Sort: