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
)
- PyTorch: Deep learning framework (
-
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
- GloVe Embeddings (choose smaller variants):
- WikiText-2: Via Hugging Face Datasets
-
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+
- Minimum:
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
- Weights & Biases: For experiment tracking
- PyTorch Lightning: For structured PyTorch training
- Hugging Face Transformers: For reference implementations