🔴 Implementing a Transformer-Based Language Model from Scratch

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:

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)

  • Dataset Options:

    • WikiText-2: Via Hugging Face Datasets
      • Size: ~4MB compressed
      • Perfect for development and testing
      • Access via load_dataset('wikitext', 'wikitext-2-v1')
    • WikiText-103: Via Hugging Face Datasets
      • Size: ~500MB compressed, ~1.5GB uncompressed
      • For more comprehensive training
      • Access via load_dataset('wikitext', 'wikitext-103-v1')
    • Pre-trained Embeddings:
      • GloVe Embeddings (choose smaller variants):
        • Wikipedia 2014 + Gigaword 5 (100d) - 822MB
      • Word2Vec Google News Vectors:
        • Optional for initial development
        • Use subset for testing
  • Hardware Requirements:

    • Minimum:
      • GPU: NVIDIA RTX 2060 (6GB VRAM)
      • RAM: 16GB system memory
      • Storage: 10GB free space
      • CUDA: Version 11.1+
    • Recommended:
      • GPU: NVIDIA RTX 3060 Ti/3070 (8GB) or better
      • RAM: 32GB system memory
      • Storage: 50GB free space
      • CUDA: Version 11.1+

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

Tools and Libraries

Optimization Guides