AI
AIPOD

Understanding Transformers: The Architecture That Changed AI

November 5, 2025
4 min read

Tags

transformersdeep-learningnlptutorial

import { Alert, AlertDescription } from '@/components/ui/alert'

Understanding Transformers: The Architecture That Changed AI

The transformer architecture, introduced in the seminal paper "Attention Is All You Need" by Vaswani et al., has revolutionized the field of artificial intelligence. Let's dive deep into how transformers work and why they're so effective.

This tutorial assumes basic knowledge of neural networks and linear algebra.

The Problem with Sequential Processing

Before transformers, most NLP models processed text sequentially using RNNs or LSTMs. This created several limitations:

  1. Sequential bottleneck: Each word had to be processed one after another
  2. Vanishing gradients: Long sequences suffered from gradient decay
  3. Limited parallelization: Training was slow due to sequential nature

The Transformer Solution

Transformers solve these problems through the attention mechanism, which allows the model to focus on relevant parts of the input simultaneously.

Self-Attention Mechanism

The core innovation is self-attention, which computes relationships between all positions in a sequence:

import torch
import torch.nn as nn
import math

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.d_k = d_model // num_heads
        
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)
        
    def scaled_dot_product_attention(self, Q, K, V, mask=None):
        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
        
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
            
        attention_weights = torch.softmax(scores, dim=-1)
        output = torch.matmul(attention_weights, V)
        
        return output, attention_weights
    
    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)
        
        # Linear transformations and reshape
        Q = self.W_q(query).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        K = self.W_k(key).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        V = self.W_v(value).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
        
        # Apply attention
        attention_output, attention_weights = self.scaled_dot_product_attention(Q, K, V, mask)
        
        # Concatenate heads
        attention_output = attention_output.transpose(1, 2).contiguous().view(
            batch_size, -1, self.d_model
        )
        
        # Final linear transformation
        output = self.W_o(attention_output)
        
        return output, attention_weights

Architecture Overview

The transformer consists of an encoder-decoder structure:

graph TB
    subgraph "Encoder"
        E1[Input Embedding] --> E2[Positional Encoding]
        E2 --> E3[Multi-Head Attention]
        E3 --> E4[Add & Norm]
        E4 --> E5[Feed Forward]
        E5 --> E6[Add & Norm]
        E6 --> E7[Encoder Output]
    end
    
    subgraph "Decoder"
        D1[Output Embedding] --> D2[Positional Encoding]
        D2 --> D3[Masked Multi-Head Attention]
        D3 --> D4[Add & Norm]
        D4 --> D5[Multi-Head Attention]
        D5 --> D6[Add & Norm]
        D6 --> D7[Feed Forward]
        D7 --> D8[Add & Norm]
        D8 --> D9[Linear & Softmax]
    end
    
    E7 --> D5

Key Components

1. Positional Encoding

Since transformers don't have inherent sequence order, positional encodings are added:

$$PE_{(pos, 2i)} = \sin\left(\frac{pos}{10000^{2i/d_{model}}}\right)$$

$$PE_{(pos, 2i+1)} = \cos\left(\frac{pos}{10000^{2i/d_{model}}}\right)$$

2. Multi-Head Attention

Multiple attention heads allow the model to focus on different aspects:

def multi_head_attention(query, key, value, num_heads):
    # Split into multiple heads
    heads = []
    for i in range(num_heads):
        head_q = query[:, :, i * d_k:(i + 1) * d_k]
        head_k = key[:, :, i * d_k:(i + 1) * d_k]
        head_v = value[:, :, i * d_k:(i + 1) * d_k]
        
        head_output = attention(head_q, head_k, head_v)
        heads.append(head_output)
    
    # Concatenate and project
    concat_heads = torch.cat(heads, dim=-1)
    return linear_projection(concat_heads)

3. Feed-Forward Networks

Each position is processed through a position-wise feed-forward network:

class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)
        self.relu = nn.ReLU()
        
    def forward(self, x):
        return self.linear2(self.relu(self.linear1(x)))

Training Considerations

Learning Rate Scheduling

Transformers benefit from warmup learning rate scheduling:

def get_lr(step, d_model, warmup_steps=4000):
    step = max(step, 1)  # Avoid division by zero
    return d_model ** -0.5 * min(step ** -0.5, step * warmup_steps ** -1.5)

Regularization Techniques

  1. Dropout: Applied to attention weights and feed-forward outputs
  2. Layer Normalization: Stabilizes training
  3. Label Smoothing: Prevents overconfidence

Impact and Applications

Transformers have enabled breakthrough applications:

  • Language Models: GPT series, BERT, T5
  • Computer Vision: Vision Transformer (ViT)
  • Multimodal: CLIP, DALL-E
  • Code Generation: Codex, GitHub Copilot

Performance Comparison

Model Type Training Speed Inference Speed Long Sequences Parallelization
RNN/LSTM Slow Fast Poor Limited
CNN Fast Fast Limited Excellent
Transformer Fast Medium Excellent Excellent

Conclusion

The transformer architecture's success lies in its ability to:

  1. Parallelize training through self-attention
  2. Capture long-range dependencies effectively
  3. Scale efficiently with increased model size
  4. Transfer knowledge across different tasks

Understanding transformers is crucial for anyone working in modern AI, as they form the backbone of most state-of-the-art models today.

Further Reading