A transformer model consists of several key components that work together to process and generate representations for input sequences. The main components of a transformer model are as follows:
Encoder: The encoder is responsible for processing the input sequence and generating representations that capture the contextual information of each element. It consists of multiple identical layers, typically stacked on top of each other. Each layer contains two sub-layers: a self-attention mechanism and a position-wise feed-forward neural network.
Self-Attention Mechanism: This mechanism allows the model to attend to different parts of the input sequence while encoding it. It computes attention scores between each element and every other element in the sequence, resulting in a weighted sum of values. This process allows the model to capture dependencies and relationships between different elements.
Position-wise Feed-Forward Neural Network: After the self-attention mechanism, a feed-forward neural network is applied to each position separately. It consists of fully connected layers with activation functions, enabling non-linear transformations of the input representations.
Decoder: The decoder takes the encoded representations generated by the encoder and generates an output sequence. It also consists of multiple identical layers, each containing sub-layers such as self-attention, cross-attention, and position-wise feed-forward networks.
Self-Attention Mechanism: Similar to the encoder, the decoder uses self-attention to attend to different parts of the decoded sequence while generating the output. It allows the decoder to consider the previously generated elements in the output sequence when generating the next element.
Cross-Attention Mechanism: In addition to self-attention, the decoder employs cross-attention to attend to relevant parts of the encoded input sequence. It allows the decoder to align and extract information from the encoded sequence when generating the output.
Self-Attention and Cross-Attention: These attention mechanisms are fundamental components of the transformer architecture. They enable the model to weigh the importance of different elements in the input and output sequences when generating representations. Attention scores are computed by measuring the compatibility between elements, and the weighted sum of values is used to capture contextual dependencies.
Positional Encoding: Transformers incorporate positional encoding to provide information about the order or position of elements in the input sequence. It is added to the input embeddings and allows the model to understand the sequential nature of the data.
Residual Connections and Layer Normalization: Transformers employ residual connections and layer normalization to facilitate the flow of information and improve gradient propagation. Residual connections enable the model to capture both high-level and low-level features, while layer normalization normalizes the inputs to each layer, improving the stability and performance of the model.
These components collectively enable the transformer model to process and generate representations for input sequences in an efficient and effective manner. The self-attention mechanisms, along with the feed-forward networks and positional encoding, allow the model to capture long-range dependencies, handle the parallel processing, and generate high-quality representations, making transformers highly successful in natural language processing tasks.
