Self Attention
Self-attention, also known as scaled dot-product attention, is a fundamental component of transformers and plays a crucial role in their success. Self-attention allows the model to weigh the importance of different elements (or "tokens") in the input sequence when making predictions. It captures dependencies and relationships between elements, enabling the model to understand the context and meaning of each token within the sequence.
To understand self-attention, let's break down the key components:
Query, Key, and Value: In self-attention, the input sequence is transformed into three vectors for each token: query, key, and value. These vectors are obtained by applying linear transformations to the input embedding of the token. The query vector represents the token being attended to, while the key and value vectors represent other tokens in the sequence.
Attention Scores: To compute the importance or relevance of each token to the query token, the dot product between the query vector and the key vector is calculated. This dot product operation measures the similarity or correlation between the query and each key in the sequence. The resulting scores indicate how much attention should be given to each token.
Attention Weights: The attention scores are then scaled by a factor of the square root of the dimension of the key vector to prevent them from becoming too large. Next, the scores are passed through a softmax function, which normalizes them into probabilities. These probabilities, known as attention weights, determine the contribution of each token's value to the final output.
Weighted Sum: Finally, the attention weights are applied to the value vectors. Each value is multiplied by its corresponding attention weight, and the weighted values are summed up. This weighted sum represents the attended representation of the input sequence, where tokens with higher attention weights have a greater impact on the final output.
The process of self-attention is typically performed in parallel for all tokens in the sequence. This means that each token attends to all other tokens, including itself, allowing for the capture of long-range dependencies and relationships.
Multiple attention heads can be employed in the self-attention mechanism, each learning different representations and capturing different aspects of the input sequence. This multi-head attention allows the model to attend to different parts of the sequence simultaneously and learn diverse patterns.
Self-attention provides a powerful mechanism for modeling dependencies in sequential data. It enables the transformer model to understand the importance and relationships between elements in the input sequence, contributing to its ability to handle complex natural language processing tasks effectively.