A multi-head self-attention module is a core computational block in transformer-based neural networks that enables a model to capture contextual relationships between all elements of an input sequence or feature map simultaneously. In this module, the input representations are linearly projected into multiple independent sets of queries, keys, and values, which are known as attention heads. Each head calculates self-attention in parallel using scaled dot-product attention, measuring the pairwise relevance between tokens to produce weighted context vectors across different representation subspaces. The outputs from all individual heads are then concatenated and linearly transformed into a unified output dimension, allowing the network to jointly encode diverse types of interactions, dependencies, and feature patterns across short and long distances.
