the query key value calculation process in intra attention

Intra-attention, also known as self-attention or intra-self attention, is a mechanism used in transformer-based models to capture dependencies between different positions or words within a sequence. It allows the model to attend to different parts of the input sequence when generating the output.

The query, key, value calculation process in intra-attention involves the following steps:

Embedding: Each word or position in the input sequence is first embedded into a lower-dimensional vector representation. This embedding step helps capture the semantic meaning of the words or positions.
Linear Transformation: The embedded input sequence is then linearly transformed into three vectors: query, key, and value. This transformation is performed using learnable weight matrices specific to each attention head in the model.
Query, Key, Value Calculation: For each position in the input sequence, the query, key, and value vectors are calculated. The query vector represents the current position and is used to compare with other positions in the sequence. The key vectors represent all positions in the sequence and are used to compute the attention scores. The value vectors contain information about each position and are used to compute the weighted sum based on the attention scores.
Attention Scores: The attention scores are calculated by taking the dot product between the query vector and the key vectors for each position in the sequence. The dot product captures the similarity between the query and key vectors, indicating how much attention should be given to a particular position.
Softmax and Weighted Sum: The attention scores are then passed through a softmax function to obtain a probability distribution over all positions. This ensures that the attention weights sum up to 1. The weighted sum of the value vectors, using the attention scores as weights, gives the final representation for the current position in the sequence.
Multi-Head Attention: In transformer-based models, multiple attention heads are used to capture different types of dependencies. The query, key, and value calculations are performed independently for each attention head, resulting in multiple sets of attention scores and weighted sums. These multiple sets of outputs are then concatenated or combined to form the final output representation.

The query, key, value calculation process in intra-attention allows the model to attend to different parts of the input sequence based on the similarity between positions. It helps the model capture long-range dependencies and improves the performance of tasks such as machine translation, text classification, and language modeling