🔴 Implementing a Transformer-Based Language Model from Scratch

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Implementing a Transformer-Based Language Model from Scratch

Objective

Build a Transformer-based language model similar to GPT architectures from scratch using PyTorch. This project involves understanding and implementing the Transformer architecture, training the model on a large text corpus, and experimenting with text generation capabilities. You will delve deep into the mechanics of self-attention mechanisms and position-wise feedforward networks.

Learning Outcomes

By completing this project, you will:

• Understand the Transformer architecture in detail.
• Implement multi-head self-attention and positional encoding.
• Train a language model on large-scale text data.
• Explore techniques for efficient training, such as masking and batching.
• Evaluate language models using perplexity and text coherence.
• Gain insights into advanced NLP techniques and model optimization.

Prerequisites and Theoretical Foundations

1. Advanced Python Programming

• Deep Learning Frameworks: Proficiency with PyTorch.
• Efficient Coding Practices: Writing optimized code for GPU execution.
• Data Handling: Experience with large datasets and data pipelines.

2. Mathematics and Machine Learning Foundations

• Linear Algebra: Matrix operations, eigenvalues, eigenvectors.
• Probability and Statistics: Understanding distributions and expectations.
• Optimization: Gradient descent algorithms, learning rate scheduling.

3. Understanding of Transformer Models

• Attention Mechanisms:
  • Scaled dot-product attention.
  • Multi-head attention.
• Positional Encoding:
  • Sinusoidal positional embeddings.
• Feedforward Networks:
  • Position-wise fully connected layers.
• Layer Normalization.
• Key Papers:
  • Attention Is All You Need (Original Transformer paper)
  • Transformers: State of the Art NLP

4. Natural Language Processing

• Tokenization:
  • Byte Pair Encoding (BPE).
  • WordPiece tokenization.
• Language Modeling:
  • Next-word prediction.
  • Sequence-to-sequence tasks.

Tools Required

• Programming Language: Python 3.8+
• Libraries and Frameworks:
  • PyTorch: Deep learning framework (pip install torch>=1.9.0)
  • TorchText: NLP data handling (pip install torchtext>=0.10.0)
  • NumPy: Numerical computations (pip install numpy>=1.20.0)
  • Matplotlib: Visualization (pip install matplotlib>=3.4.0)
  • Hugging Face Tokenizers: (pip install tokenizers>=0.10.3)
• Hardware Requirements:
  • Minimum: GPU with at least 6GB VRAM, 16GB system RAM, and 10GB of available storage.
  • Recommended: GPU with 8GB or more VRAM, 32GB system RAM, and 50GB of available storage.
    

• Dataset Options:
  • WikiText-2: A smaller dataset ideal for development and testing (4MB compressed).
    • Access via Hugging Face Datasets: load_dataset('wikitext', 'wikitext-2-v1')
  • WikiText-103: A larger dataset for more comprehensive training (~1.5GB uncompressed).
    • Access via Hugging Face Datasets: load_dataset('wikitext', 'wikitext-103-v1')
  • Pre-trained Embeddings:
    • GloVe (smaller variants) or Word2Vec for initial development.
    • Use subsets of embeddings as needed to fit your resources.

Project Structure

transformer_language_model/
│
├── data/
│   └── wikitext-103/
│       └── train.txt
│
├── src/
│   ├── model.py
│   ├── train.py
│   ├── generate.py
│   └── utils.py
│
└── notebooks/
    └── exploration.ipynb

Steps and Tasks

1. Data Preparation

Tasks:

• Download and Preprocess the Dataset:
  • Use WikiText-103 or similar large text corpus.
• Implement Tokenization:
  • Use BPE or WordPiece tokenization.
• Create Vocabulary and Encoding:
  • Build a tokenizer and encode the text data.

Implementation:

from torchtext.datasets import WikiText103
from torchtext.data.utils import get_tokenizer
from collections import Counter
from torchtext.vocab import Vocab

# Load dataset
train_iter = WikiText103(split='train')

# Tokenization
tokenizer = get_tokenizer('basic_english')
counter = Counter()
for line in train_iter:
    counter.update(tokenizer(line))

vocab = Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])

def data_process(raw_text_iter):
    data = [torch.tensor([vocab['<bos>']] + [vocab[token] for token in tokenizer(item)] + [vocab['<eos>']], dtype=torch.long)
            for item in raw_text_iter]
    return torch.cat(data)

train_data = data_process(train_iter)

---

2. Implementing the Transformer Model

Tasks:

• Define the Transformer Architecture:
  • Implement encoder and decoder layers.
• Implement Multi-Head Attention:
  • Code the scaled dot-product attention mechanism.
• Add Positional Encoding:
  • Incorporate positional information into embeddings.

Implementation:

import torch.nn as nn
import math

class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers, dim_feedforward):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.pos_encoder = PositionalEncoding(d_model)
        encoder_layers = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
        self.encoder = nn.Embedding(vocab_size, d_model)
        self.d_model = d_model
        self.decoder = nn.Linear(d_model, vocab_size)

    def forward(self, src, src_mask):
        src = self.encoder(src) * math.sqrt(self.d_model)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, src_mask)
        output = self.decoder(output)
        return output

class PositionalEncoding(nn.Module):
    # Implementation of positional encoding
    pass

---

3. Training the Language Model

Tasks:

• Prepare Training Batches:
  • Create input and target sequences.
• Define Loss Function and Optimizer:
  • Use CrossEntropyLoss and an appropriate optimizer.
• Implement Training Loop:
  • Handle masking and batch processing.

Implementation:

# Generate batch data
def batchify(data, batch_size):
    # Divide the data into batches
    pass

# Training loop
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1.0)

for epoch in range(epochs):
    model.train()
    total_loss = 0
    for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
        data, targets = get_batch(train_data, i)
        optimizer.zero_grad()
        output = model(data, src_mask)
        loss = criterion(output.view(-1, vocab_size), targets)
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
        optimizer.step()
        total_loss += loss.item()

---

4. Implementing Efficient Training Techniques

Tasks:

• Apply Masking:
  • Implement source and target masks to prevent the model from peeking ahead.
• Use Learning Rate Scheduling:
  • Adjust the learning rate over time for better convergence.
• Implement Gradient Clipping:
  • Prevent exploding gradients in training.

Implementation:

def generate_square_subsequent_mask(sz):
    # Generate a square mask for the sequence
    pass

scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)

# In training loop
scheduler.step()

---

5. Evaluating the Model

Tasks:

• Compute Perplexity:
  • Evaluate the model’s ability to predict the next word.
• Assess Text Generation Quality:
  • Generate sample texts and assess coherence.
• Compare with Baseline Models:
  • Benchmark against simpler architectures.

Implementation:

import math

def evaluate(eval_model, data_source):
    eval_model.eval()
    total_loss = 0.
    with torch.no_grad():
        for i in range(0, data_source.size(0) - 1, bptt):
            data, targets = get_batch(data_source, i)
            output = eval_model(data, src_mask)
            output_flat = output.view(-1, vocab_size)
            total_loss += len(data) * criterion(output_flat, targets).item()
    return total_loss / (len(data_source) - 1)

perplexity = math.exp(evaluate(model, val_data))
print(f'Perplexity: {perplexity}')

---

6. Text Generation and Inference

Tasks:

• Implement Text Generation Function:
  • Use the trained model to generate text given a prompt.
• Handle Sampling Methods:
  • Apply techniques like greedy search, beam search, or nucleus sampling.

Implementation:

def generate_text(model, start_token, max_len=50):
    model.eval()
    tokens = [vocab[start_token]]
    for _ in range(max_len):
        input_seq = torch.tensor(tokens).unsqueeze(1)
        output = model(input_seq, src_mask)
        next_token = output.argmax(-1)[-1].item()
        tokens.append(next_token)
        if next_token == vocab['<eos>']:
            break
    return ' '.join([vocab.itos[token] for token in tokens])

---

7. Optimization and Scaling

Tasks:

• Experiment with Model Depth and Width:
  • Adjust the number of layers and model dimensions.
• Use Pre-trained Embeddings:
  • Initialize embeddings with GloVe or Word2Vec.
• Parallelize Training:
  • Utilize multiple GPUs or distributed training frameworks.

Implementation:

# Adjusting model parameters
model = TransformerModel(vocab_size, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048)

# DataParallel for multi-GPU
if torch.cuda.device_count() > 1:
    model = nn.DataParallel(model)

---

8. Documentation and Reporting

Tasks:

• Document the Model Implementation:
  • Explain each component of the Transformer architecture.
• Visualize Training Metrics:
  • Plot loss curves and learning rate schedules.
• Prepare a Project Report:
  • Summarize objectives, methods, results, and insights gained.

---

Further Enhancements

• Implement Advanced Attention Mechanisms:
  • Explore relative positional encodings or sparse attention.
• Use Larger Datasets:
  • Train on datasets like OpenWebText or Wikipedia dumps.
• Experiment with Language Model Fine-Tuning:
  • Adapt the model to specific tasks like summarization or translation.
• Integrate with Tokenization Libraries:
  • Use Hugging Face’s Tokenizers for efficient subword tokenization.

---

Conclusion

In this advanced project, you have:

• Implemented the Transformer architecture from scratch.
• Trained a language model on large-scale text data.
• Explored efficient training techniques for deep models.
• Evaluated and analyzed the model’s language generation capabilities.

This project provides deep insights into modern NLP models and equips you with the skills to work on cutting-edge language processing tasks.

Additional Resources

Learning Materials

• Transformer Implementation Tutorial\
• Attention and Transformer Networks

Tools and Libraries

• Weights & Biases: For experiment tracking
• PyTorch Lightning: For structured PyTorch training
• Hugging Face Transformers: For reference implementations

Optimization Guides

• PyTorch Performance Tuning Guide
• Distributed Training with PyTorch
• Mixed Precision Training