The Multi-Head Attention Layer

https://www.youtube.com/watch?v=HQn1QKQYXVg

https://www.youtube.com/watch?v=eMlx5fFNoYc&t=4s

https://www.youtube.com/watch?v=lmepFoddjgQ

The Multi-Head Attention layer is a critical component of the Transformer model, a groundbreaking architecture in the field of natural language processing. The concept of Multi-Head Attention is designed to allow the model to jointly attend to information from different representation subspaces at different positions. Here’s a breakdown of the basics:

1. Attention Mechanism:

• The core idea behind attention in deep learning is to focus on certain parts of the input sequence when processing each part of that sequence, much like how humans pay attention to different parts of a visual scene or a piece of text.
• In the context of the Transformer model, the attention mechanism used is called “Scaled Dot-Product Attention”.

2. Scaled Dot-Product Attention:

•   The inputs to the attention layer are queries (Q), keys (K), and values (V).
•   The attention scores are computed by taking the dot product of the query with all keys, scaling these scores by the square root of the dimension of the keys, and then applying a softmax function to get the weights on the values.

3. Multi-Head Attention:

•  In Multi-Head Attention, the idea is to run multiple attention mechanisms (heads) in parallel. Each head computes its own set of Q, K, and V using different, learned linear projections.
• This allows the model to capture different types of relationships in different representation subspaces. For example, one head might focus on the syntactic structure, while another might focus on semantic content.

4. Linear Projections:

• The queries, keys, and values are linearly projected multiple times with different, learned linear transformations for each head.

5. Concatenation and Final Linear Projection:

• The outputs of the individual heads are then concatenated and once again linearly transformed into the expected dimensions.

6. Advantages:

• This architecture allows the model to better disentangle different types of relationships in the input data.
• Multi-Head Attention provides the flexibility to focus on different parts of the input sequence and to consider different aspects of each part.

7. Application in Transformers:

• In the Transformer model, Multi-Head Attention is used in both the encoder and the decoder. The encoder uses it to process the input sequence, while the decoder uses it to focus on relevant parts of the input sequence and its own output so far.

The innovation of the Multi-Head Attention mechanism was one of the key reasons the Transformer model achieved such impressive results in various natural language processing tasks, as it allows the model to dynamically focus on different parts of the input sequence in a highly efficient and parallelizable way.