The Illustrated Transformer

Introduction

In the field of natural language processing, the Transformer architecture has revolutionized how we approach sequence-to-sequence tasks. This visual guide aims to break down the complexities of the Transformer model, making it accessible to a wider audience.

The Transformer Architecture

Figure 1: High-level view of the Transformer architecture

The Transformer consists of an encoder and a decoder, each composed of multiple layers. The key innovation lies in its use of self-attention mechanisms, allowing the model to weigh the importance of different parts of the input sequence dynamically.

Self-Attention Mechanism

Figure 2: Visualization of the self-attention mechanism

Self-attention allows each word in a sequence to attend to all other words, capturing complex dependencies regardless of their distance in the sequence. This is achieved through the computation of Query, Key, and Value matrices.

Note: The self-attention mechanism is what gives Transformers their power, allowing them to process input sequences in parallel, unlike RNNs which process sequentially.

Multi-Head Attention

Figure 3: Multi-head attention structure

Multi-head attention extends the idea of self-attention by allowing the model to jointly attend to information from different representation subspaces at different positions. This enables the model to capture various aspects of the sequence simultaneously.

Positional Encoding

Figure 4: Visualization of positional encodings

Since the Transformer doesn't inherently understand the order of the sequence, positional encodings are added to give the model information about the relative or absolute position of the tokens in the sequence.

PE(pos,2i) = sin(pos / 10000^(2i/d_model)) PE(pos,2i+1) = cos(pos / 10000^(2i/d_model))

Feed-Forward Networks

Each layer in the Transformer also contains a fully connected feed-forward network. This network consists of two linear transformations with a ReLU activation in between:

FFN(x) = max(0, xW1 + b1)W2 + b2

This component allows the model to introduce non-linearity and process the attended information further.

Conclusion

The Transformer architecture has become the foundation for many state-of-the-art models in NLP, including BERT, GPT, and T5. Its ability to process sequences in parallel and capture long-range dependencies has made it an indispensable tool in modern machine learning.

For a deeper understanding, it's recommended to explore the original paper "Attention Is All You Need" and experiment with implementing Transformers using libraries like PyTorch or TensorFlow.