Understanding the architecture and mechanics of transformer models
Transformers have revolutionized machine learning, particularly in natural language processing. This chapter provides a thorough introduction to the transformer architecture, including its key components and operating principles.
The transformer model was introduced in the 2017 paper "Attention Is All You Need" by Vaswani et al. Unlike previous sequence-to-sequence models that relied on recurrence or convolution, transformers are based entirely on attention mechanisms, making them more parallelizable and efficient for training.
The transformer architecture consists of an encoder and a decoder, each containing stacked layers of self-attention and feed-forward neural networks.
The encoder processes the input sequence in parallel. Each encoder layer has two sub-layers:
Each sub-layer employs residual connections and layer normalization.
The decoder generates the output sequence, with each step predicting the next token. It contains three sub-layers:
Attention mechanisms allow the model to focus on different parts of the input when generating each part of the output. The self-attention mechanism in transformers, specifically the "Scaled Dot-Product Attention," computes attention weights as follows:
Attention(Q, K, V) = softmax(QK^T/√d_k)V
Where Q (query), K (key), and V (value) are matrices, and d_k is the dimension of the keys.
Multi-head attention allows the model to jointly attend to information from different representation subspaces, enabling it to capture different aspects of the input.
Large language models like GPT (Generative Pre-trained Transformer) use transformer architectures with modifications. These models are trained on vast amounts of text data to predict the next token in a sequence.
Key transformer-based language models include:
Transformers have become the foundation of modern NLP and are expanding into other domains like computer vision and reinforcement learning. Understanding their architecture is essential for work in mechanistic interpretability, as it provides the necessary context for analyzing how these models process and represent information.
