In my recent work on large language models, I've been exploring the intricacies of attention mechanisms that power modern transformer architectures. The self-attention mechanism is arguably one of the most important innovations in deep learning, enabling models to process sequences of arbitrary length while maintaining computational efficiency.
This raises several important questions: How does multi-head attention actually work under the hood? What are the computational trade-offs between different attention variants? I'm particularly interested in both the theoretical foundations and practical implementation details that affect model performance.