Introduction to Transformers

A beginner-friendly guide to understanding the Transformer architecture that powers modern NLP.

The Transformer architecture, introduced in the seminal paper "Attention Is All You Need," has revolutionized natural language processing and beyond.

What is a Transformer?

A Transformer is a neural network architecture that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions entirely.

Key Components

Self-Attention

The self-attention mechanism allows the model to weigh the importance of different parts of the input when producing an output.

\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

Multi-Head Attention

Instead of performing a single attention function, multi-head attention runs through the attention mechanism multiple times in parallel.

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.depth = d_model // num_heads
        
        self.wq = nn.Linear(d_model, d_model)
        self.wk = nn.Linear(d_model, d_model)
        self.wv = nn.Linear(d_model, d_model)
        self.dense = nn.Linear(d_model, d_model)

Why Transformers Matter

Parallelization: Unlike RNNs, Transformers can process all positions simultaneously
Long-range dependencies: Self-attention can directly connect distant positions
Scalability: The architecture scales well with more data and compute

Conclusion

Transformers have become the foundation for models like BERT, GPT, and many others. Understanding them is essential for anyone working in modern ML.