Transformers

What is a Transformer?

The Transformer is a neural network architecture introduced in the landmark paper "Attention Is All You Need" (Vaswani et al., 2017). It revolutionized deep learning by replacing recurrent (RNN/LSTM) and convolutional architectures with a purely attention-based mechanism, enabling efficient parallelization and superior modeling of long-range dependencies.

Transformers are the foundation of virtually all modern AI systems including:

High-Level Architecture

The original Transformer uses an encoder-decoder structure designed for sequence-to-sequence tasks like translation:

The original Transformer architecture from "Attention Is All You Need" (Vaswani et al., 2017)

Self-Attention Mechanism

Self-attention is the core innovation of Transformers. It allows each token in a sequence to "attend to" (or consider) every other token, capturing contextual relationships regardless of distance.

Attention(Q, K, V) = softmax(QKᵀ / √dₖ) · V

Multi-Head Attention

Instead of a single attention operation, Transformers use multiple attention heads in parallel. Each head learns different types of relationships:

headᵢ = Attention(QWᵢᴽ, KWᵢᴷ, VWᵢⱽ)
MultiHead(Q,K,V) = Concat(head₁,...,headₕ)Wᴼ

Positional Encoding

Since Transformers process all tokens simultaneously (no inherent order), positional information must be explicitly injected. Common approaches:

BERT vs GPT: Key Differences

Computational Complexity

This has driven research into efficient variants:

• FlashAttention: IO-aware algorithm that reduces memory reads/writes
• Sparse Attention: Attend only to specific positions (Longformer, BigBird)
• Linear Attention: Approximate attention with O(n) complexity (Performer)
• State Space Models: Alternative to attention (Mamba, S4)
• Ring Attention: Distributed attention across multiple devices

Modern Improvements (2024)

Applications

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Related Topics

• Embeddings
• Pre-training
• Model Parameters
• OpenAI GPT Models
• LLaMA Models