This project implements a Transformer architecture from scratch using PyTorch, following the original "Attention Is All You Need" paper. The implementation includes a complete sequence-to-sequence model with encoder-decoder architecture, multi-head attention mechanisms, and positional encodings.
- Full implementation of the Transformer architecture from scratch
- Support for multiple languages through the opus_books dataset
- Mixed precision training for improved performance
- Gradient accumulation for effective batch size management
- Advanced training features including:
- Layer normalization
- Multi-head attention
- Positional encoding
- Residual connections
- Feed-forward networks
- PyTorch: Core deep learning framework
- HuggingFace Datasets: Data loading and preprocessing
- HuggingFace Tokenizers: Custom tokenization
- TorchMetrics: Performance evaluation (CER, WER, BLEU)
- TensorBoard: Training visualization and monitoring
- CUDA: GPU acceleration
- Python: Primary programming language
The implementation includes several key components:
- Custom LayerNormalization
- InputEmbeddings with scaled outputs
- PositionalEncoding using sine and cosine functions
- MultiHeadAttentionBlock with scaled dot-product attention
- EncoderBlock and DecoderBlock with residual connections
- Separate Encoder and Decoder stacks
- ProjectionLayer for output generation
- Mixed precision training using
torch.cuda.amp - Gradient accumulation for larger effective batch sizes
- Memory management with CUDA cache clearing
- Optimized CUDA operations with benchmarking
- Weight initialization using Xavier uniform distribution
Through this project, I gained deep understanding of:
-
Transformer Architecture
- Internal mechanisms of attention
- Position encoding techniques
- Importance of residual connections and layer normalization
-
Deep Learning Best Practices
- Mixed precision training implementation
- Memory management in deep learning
- Gradient accumulation techniques
- Proper weight initialization
-
Performance Optimization
- CUDA optimization techniques
- Batch processing strategies
- Memory efficiency in deep learning models
-
Software Engineering
- Clean code architecture
- Modular design principles
- Type hinting in Python
- Efficient data processing pipelines
- Support for checkpoint saving and loading
- Configurable model parameters
- Dynamic batch size adjustment
- Comprehensive validation metrics
- TensorBoard integration for monitoring
- Automated tokenizer building and management
The implementation tracks multiple metrics:
- Character Error Rate (CER)
- Word Error Rate (WER)
- BLEU Score
- Training and validation loss
- Implementation of beam search for better inference
- Support for different attention mechanisms
- Integration of more advanced regularization techniques
- Addition of more sophisticated learning rate schedules
- Support for different model architectures (e.g., encoder-only, decoder-only)
- Deep understanding of attention mechanisms and their implementation
- Practical experience with PyTorch's advanced features
- Hands-on experience with performance optimization techniques
- Understanding of modern NLP architecture design
- Experience with production-ready deep learning code
# Install requirements
pip install torch torchvision torchaudio
pip install datasets tokenizers torchmetrics tensorboard tqdm
# Train the model
python train.py
# Monitor training
tensorboard --logdir=runs