Self-Attention in Transformers: Computation logic and implementation

Self-Attention in Transformers: Computation Logic and Implementation

Self-attention untangles the relationships between tokens in deep learning

Attention serves as a fundamental concept for transformer architecture and for Large Language Models, playing a pivotal role in capturing dependencies between different words in a sequence. It intervenes in several building blocks of the Transformer architecture, more specifically, the multi-head self-attention, cross-attention, and masked attention stages.

Attention-based stages in the Transformer architecture, based on Attention is All You Need, Wasnani et al.
arXiv:1706.03762 …

ai gpt llm self-attention transformers