Delve into the architecture powering modern AI breakthroughs and learn how to build your own Transformer model

Introduction

Welcome to the next installment in our series aimed at new graduates and early-career professionals eager to dive deep into the world of Generative AI (GenAI). Today, we’ll explore the Transformer architecture, a groundbreaking model that has revolutionized natural language processing (NLP) and enabled the development of powerful language models like GPT-4.

Whether you’re an aspiring AI engineer or a data scientist, understanding Transformers is crucial for advancing your skills. We’ll break down the architecture, discuss its applications, and guide you through building a simple Transformer model using Python and PyTorch.

Table of Contents

1. What are Transformer Models?
2. The Limitations of Previous Models
3. Key Components of the Transformer Architecture
   • Encoder and Decoder
   • Self-Attention Mechanism
   • Positional Encoding
4. Applications of Transformer Models
5. Building a Simple Transformer Model
   • Prerequisites
   • Step 1: Data Preparation
   • Step 2: Importing Libraries
   • Step 3: Defining the Model
   • Step 4: Training the Model
   • Step 5: Evaluating the Model
6. Conclusion
7. Further Resources
8. Advance Your Skills with GenAI Talent Academy

What are Transformer Models?

Transformer models are a type of neural network architecture introduced in the 2017 paper “Attention is All You Need” by Vaswani et al. Unlike traditional sequence models, Transformers rely entirely on self-attention mechanisms to process input data, allowing for parallelization and improved performance.

Key Innovations:

• Self-Attention Mechanism: Enables the model to weigh the importance of different parts of the input data.
• Positional Encoding: Adds information about the position of each element in the sequence.

The Limitations of Previous Models

Before Transformers, models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) were standard for sequence data. However, they had significant limitations:

• Sequential Processing: Cannot process sequences in parallel, leading to longer training times.
• Vanishing Gradients: Difficulty in learning long-range dependencies due to gradient issues.
• Fixed Memory: Limited ability to handle very long sequences.

Key Components of the Transformer Architecture

Encoder and Decoder

The Transformer model consists of two parts:

• Encoder: Processes the input sequence and generates a context-aware representation.
• Decoder: Generates the output sequence by predicting one element at a time, using the encoder’s output and previously generated elements.

Self-Attention Mechanism

Self-attention allows the model to focus on different parts of the input sequence when producing each part of the output. It computes a weighted representation of the entire sequence for each position.

Formula for Scaled Dot-Product Attention:

• Q: Queries
• K: Keys
• V: Values
• d_k​: Dimension of the keys

Positional Encoding

Since Transformers do not process sequences in order, positional encoding injects information about the position of each element in the sequence.

Sinusoidal Positional Encoding:

• pos: Position in the sequence
• i: Dimension index
• d_model​: Model dimensionality

Applications of Transformer Models

Transformers have led to significant advancements in various fields:

• Natural Language Processing (NLP): Language translation, sentiment analysis, text summarization.
• Computer Vision: Image captioning, object detection when combined with CNNs.
• Speech Processing: Speech recognition and synthesis.
• Protein Folding Prediction: Understanding biological sequences.

Notable Models Based on Transformers:

• BERT (Bidirectional Encoder Representations from Transformers): For language understanding tasks.
• GPT Series (Generative Pre-trained Transformer): For text generation and conversational AI.
• T5 (Text-to-Text Transfer Transformer): Unified framework for multiple NLP tasks.

Building a Simple Transformer Model

Let’s build a simplified Transformer model for a machine translation task: translating English sentences to French.

Prerequisites

• Python 3.7+
• PyTorch
• TorchText
• NumPy
• Matplotlib (for visualization, optional)

Install Dependencies:

bashCopy codepip install torch torchtext numpy matplotlib

Step 1: Data Preparation

For simplicity, we’ll use a small dataset of English-French sentence pairs.

Example Data:

data = [
    ('I am a student.', 'Je suis étudiant.'),
    ('How are you?', 'Comment ça va?'),
    ('Good morning.', 'Bonjour.'),
    ('Thank you.', 'Merci.'),
    ('See you later.', 'À plus tard.')
]

Step 2: Importing Libraries

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader

Step 3: Defining the Model

We’ll create a simplified version of the Transformer model using PyTorch’s nn.Transformer module.

Define Tokenizers and Vocabularies:

from torchtext.data.utils import get_tokenizer

tokenizer_en = get_tokenizer('basic_english')
tokenizer_fr = get_tokenizer('basic_english')

def yield_tokens(data_iter, language):
    language_index = 0 if language == 'en' else 1
    for data_sample in data_iter:
        yield tokenizer_en(data_sample[language_index]) if language == 'en' else tokenizer_fr(data_sample[language_index])

vocab_en = build_vocab_from_iterator(yield_tokens(data, 'en'), specials=['<unk>', '<pad>', '<bos>', '<eos>'])
vocab_en.set_default_index(vocab_en['<unk>'])

vocab_fr = build_vocab_from_iterator(yield_tokens(data, 'fr'), specials=['<unk>', '<pad>', '<bos>', '<eos>'])
vocab_fr.set_default_index(vocab_fr['<unk>'])

Define the Transformer Model:

class TransformerModel(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model=512, nhead=8, num_layers=3):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.src_tok_emb = nn.Embedding(src_vocab_size, d_model)
        self.tgt_tok_emb = nn.Embedding(tgt_vocab_size, d_model)
        self.positional_encoding = nn.Parameter(torch.empty(1, 1000, d_model))
        nn.init.uniform_(self.positional_encoding, -0.1, 0.1)
        self.transformer = nn.Transformer(d_model=d_model, nhead=nhead, num_encoder_layers=num_layers, num_decoder_layers=num_layers)
        self.fc_out = nn.Linear(d_model, tgt_vocab_size)
        self.dropout = nn.Dropout(0.1)
    
    def forward(self, src, tgt):
        src_seq_length = src.size(0)
        tgt_seq_length = tgt.size(0)
        
        src_positions = torch.arange(0, src_seq_length).unsqueeze(1).expand(src_seq_length, src.size(1))
        tgt_positions = torch.arange(0, tgt_seq_length).unsqueeze(1).expand(tgt_seq_length, tgt.size(1))
        
        src_emb = self.dropout((self.src_tok_emb(src) + self.positional_encoding[:, :src_seq_length, :]))
        tgt_emb = self.dropout((self.tgt_tok_emb(tgt) + self.positional_encoding[:, :tgt_seq_length, :]))
        
        tgt_mask = self.transformer.generate_square_subsequent_mask(tgt_seq_length).to(tgt.device)
        
        output = self.transformer(src_emb, tgt_emb, tgt_mask=tgt_mask)
        output = self.fc_out(output)
        return output

Step 4: Training the Model

Define Hyperparameters and Initialize the Model:

src_vocab_size = len(vocab_en)
tgt_vocab_size = len(vocab_fr)
model = TransformerModel(src_vocab_size, tgt_vocab_size)
criterion = nn.CrossEntropyLoss(ignore_index=vocab_fr['<pad>'])
optimizer = optim.Adam(model.parameters(), lr=0.0001)

Prepare Data for Training:

def data_process(data):
    src_list = []
    tgt_list = []
    for (src_sentence, tgt_sentence) in data:
        src_tensor = torch.tensor([vocab_en['<bos>']] + vocab_en(tokenizer_en(src_sentence)) + [vocab_en['<eos>']], dtype=torch.long)
        tgt_tensor = torch.tensor([vocab_fr['<bos>']] + vocab_fr(tokenizer_fr(tgt_sentence)) + [vocab_fr['<eos>']], dtype=torch.long)
        src_list.append(src_tensor)
        tgt_list.append(tgt_tensor)
    return src_list, tgt_list

src_data, tgt_data = data_process(data)

Create DataLoader:

from torch.nn.utils.rnn import pad_sequence

def generate_batch(data_batch):
    src_batch, tgt_batch = [], []
    for src_item, tgt_item in data_batch:
        src_batch.append(src_item)
        tgt_batch.append(tgt_item)
    src_batch = pad_sequence(src_batch, padding_value=vocab_en['<pad>'])
    tgt_batch = pad_sequence(tgt_batch, padding_value=vocab_fr['<pad>'])
    return src_batch, tgt_batch

batch_size = 2
train_iter = DataLoader(list(zip(src_data, tgt_data)), batch_size=batch_size, shuffle=True, collate_fn=generate_batch)

Training Loop:

model.train()
num_epochs = 20

for epoch in range(num_epochs):
    total_loss = 0
    for src_batch, tgt_batch in train_iter:
        optimizer.zero_grad()
        tgt_input = tgt_batch[:-1, :]
        targets = tgt_batch[1:, :].reshape(-1)
        output = model(src_batch, tgt_input)
        output = output.reshape(-1, output.shape[-1])
        loss = criterion(output, targets)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    avg_loss = total_loss / len(train_iter)
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {avg_loss:.4f}')

Step 5: Evaluating the Model

Function to Translate a New Sentence:

def translate(model, sentence):
    model.eval()
    src_tensor = torch.tensor([vocab_en['<bos>']] + vocab_en(tokenizer_en(sentence)) + [vocab_en['<eos>']], dtype=torch.long).unsqueeze(1)
    tgt_tensor = torch.tensor([vocab_fr['<bos>']], dtype=torch.long).unsqueeze(1)
    max_len = 10
    for i in range(max_len):
        output = model(src_tensor, tgt_tensor)
        pred_token = output.argmax(2)[-1, :].item()
        tgt_tensor = torch.cat([tgt_tensor, torch.tensor([[pred_token]])], dim=0)
        if pred_token == vocab_fr['<eos>']:
            break
    translated_sentence = ' '.join(vocab_fr.get_itos()[token] for token in tgt_tensor.squeeze().tolist()[1:-1])
    return translated_sentence

# Example Usage
print(translate(model, "Thank you."))

Expected Output:

Merci .

Conclusion

Understanding the Transformer architecture is a significant step toward mastering modern AI techniques. While we’ve built a simplified version, real-world models are much more complex and trained on vast datasets. Nevertheless, this exercise provides a foundational understanding of how Transformers work and how you can implement them.

Keep experimenting and exploring more advanced concepts like multi-head attention, layer normalization, and pre-training techniques used in state-of-the-art models.

Further Resources

• Research Paper: “Attention is All You Need” by Vaswani et al.
• PyTorch Tutorials: Sequence-to-Sequence Modeling with nn.Transformer
• Blogs and Articles:
  • The Illustrated Transformer by Jay Alammar
  • Transformers from Scratch by Peter Bloem

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Frequently Asked Questions

Q: Do I need prior experience with neural networks to understand Transformers?

A: Basic knowledge of neural networks and deep learning concepts is helpful but not strictly necessary. This post provides explanations to get you started.

Q: Why are Transformers better than RNNs for sequence tasks?

A: Transformers handle long-range dependencies more effectively and allow for parallel processing, leading to faster training times and better performance.

Q: Can Transformers be used for tasks other than NLP?

A: Yes, Transformers are being adapted for computer vision, speech processing, and even protein folding prediction.

Call to Action

If you found this tutorial insightful, share it with your peers and colleagues. Let’s learn and innovate together in the exciting field of Generative AI!

Author: GenAI Talent Academy Team

Date: October 14, 2023

Comments

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This post is part of our “Mastering GenAI: Advanced Techniques” series. Stay tuned for the next installment!