Boxing Day Deep Dive: Understanding Transformer Architecture

Boxing Day is perfect for diving deep into something you’ve been meaning to understand. Let’s demystify the transformer architecture that powers modern AI.

Why Transformers Matter

Every major language model - GPT, Claude, Llama - is built on the transformer architecture introduced in “Attention Is All You Need” (2017). Understanding transformers helps you:

• Make better AI architecture decisions
• Debug model behavior issues
• Optimize inference performance
• Evaluate new model releases

The Core Intuition

Transformers solve a key problem: how do you process sequences while understanding relationships between all elements, not just adjacent ones?

The answer: Attention

Attention lets every word “look at” every other word to understand context.

Self-Attention Explained

import numpy as np

def simple_attention(query, keys, values):
    """
    Simplified attention mechanism.

    query: What we're looking for (vector)
    keys: What we compare against (matrix)
    values: What we retrieve (matrix)
    """
    # Step 1: Calculate attention scores
    # How relevant is each key to our query?
    scores = np.dot(query, keys.T)

    # Step 2: Normalize with softmax
    # Convert to probabilities
    attention_weights = softmax(scores)

    # Step 3: Weighted sum of values
    # Combine values based on attention
    output = np.dot(attention_weights, values)

    return output, attention_weights

def softmax(x):
    exp_x = np.exp(x - np.max(x))
    return exp_x / exp_x.sum()

Multi-Head Attention

Instead of one attention mechanism, transformers use multiple “heads” that learn different relationships:

class MultiHeadAttention:
    def __init__(self, d_model: int, num_heads: int):
        self.num_heads = num_heads
        self.d_k = d_model // num_heads

        # Separate projections for each head
        self.W_q = np.random.randn(d_model, d_model)
        self.W_k = np.random.randn(d_model, d_model)
        self.W_v = np.random.randn(d_model, d_model)
        self.W_o = np.random.randn(d_model, d_model)

    def forward(self, x):
        batch_size, seq_len, d_model = x.shape

        # Project to Q, K, V
        Q = np.dot(x, self.W_q)
        K = np.dot(x, self.W_k)
        V = np.dot(x, self.W_v)

        # Split into heads
        Q = Q.reshape(batch_size, seq_len, self.num_heads, self.d_k)
        K = K.reshape(batch_size, seq_len, self.num_heads, self.d_k)
        V = V.reshape(batch_size, seq_len, self.num_heads, self.d_k)

        # Attention for each head
        attention_output = scaled_dot_product_attention(Q, K, V)

        # Concatenate heads and project
        output = np.dot(attention_output.reshape(batch_size, seq_len, -1), self.W_o)

        return output

The Full Transformer Block

class TransformerBlock:
    def __init__(self, d_model: int, num_heads: int, d_ff: int):
        self.attention = MultiHeadAttention(d_model, num_heads)
        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        self.feedforward = FeedForward(d_model, d_ff)

    def forward(self, x):
        # Self-attention with residual connection
        attention_out = self.attention(x)
        x = self.norm1(x + attention_out)  # Add & Norm

        # Feed-forward with residual connection
        ff_out = self.feedforward(x)
        x = self.norm2(x + ff_out)  # Add & Norm

        return x

Key Insights

1. Attention is O(n squared) - This is why context length is expensive
2. Position encoding matters - Transformers don’t inherently understand order
3. Layer normalization stabilizes training - Critical for deep networks
4. Residual connections enable depth - Information can flow through unchanged

Why This Matters for Practitioners

Understanding transformers helps you:

• Choose appropriate context lengths
• Understand why certain prompts work better
• Predict computational costs
• Evaluate efficiency improvements in new models

The transformer is the foundation of modern AI. Understanding it deeply pays dividends in every AI project you build.