Understanding Transformer Architecture: A High-Level Overview

The landscape of Natural Language Processing (NLP) shifted dramatically in 2017 with the release of the "Attention Is All You Need" paper. This introduced the Transformer architecture, which moved away from recurrent and convolutional neural networks in favor of a mechanism known as Self-Attention.

Why Transformers?

Before Transformers, models like RNNs and LSTMs processed data sequentially. This meant that to understand the 10th word in a sentence, the model had to pass through the preceding nine words first. This sequential nature made it difficult to parallelize training and capture long-range dependencies.

Transformers solved this by processing the entire sequence of data simultaneously.

The Core Components

1. The Attention Mechanism

Self-attention is the "secret sauce" of the Transformer. It allows the model to look at other words in the input sequence to help get a better encoding for the current word.

When the model processes the word "it" in the sentence "The animal didn't cross the street because it was too tired", self-attention allows the model to associate "it" with "animal".

2. Encoder-Decoder Structure

The original Transformer consists of two main parts:

• The Encoder: Processes the input sequence and generates a continuous representation.
• The Decoder: Uses the encoder's representation along with previous outputs to generate an output sequence (e.g., a translation).

3. Multi-Head Attention

Instead of performing a single attention function, Transformers use "Multi-Head" attention. This allows the model to jointly attend to information from different representation subspaces at different positions.

# A conceptual look at self-attention calculation
import numpy as np

def scaled_dot_product_attention(Q, K, V):
    matmul_qk = np.matmul(Q, K.T)
    dk = K.shape[-1]
    scaled_attention_logits = matmul_qk / np.sqrt(dk)
    
    # Softmax to get weights
    weights = softmax(scaled_attention_logits)
    output = np.matmul(weights, V)
    
    return output, weights

4. Positional Encoding

Since Transformers process words in parallel, they have no inherent sense of the order of words. To fix this, researchers add Positional Encodings to the input embeddings—mathematical signals that tell the model where each word is located in the sequence.

Conclusion

The Transformer architecture has become the foundation for nearly all state-of-the-art models in AI today, including BERT, GPT-4, and Claude. Its ability to scale with data and compute has opened the door to the era of Large Language Models.