Implementation of Transformer from Scratch in PyTorch

This project provides a complete implementation of the Transformer architecture from scratch using PyTorch. The implementation covers the full architecture explanation, training procedures, and inference steps.

Repository Structure

• transformers/tokenizer.py: Implementation of the tokenizer for preprocessing text data
• transformers/model.py: Core Transformer model implementation including attention mechanisms, encoder, and decoder
• transformers/training.py: Training utilities and procedures
• transformers/inference.py: Functions for running inference with trained models
• transformers/utils.py: Helper functions and utilities
• examples/: Example usage scripts and notebooks
• tests/: Unit tests for the implementation

Features

• Complete implementation of the Transformer architecture as described in "Attention is All You Need"
• Self-attention and multi-head attention mechanisms
• Positional encoding
• Layer normalization and residual connections
• Training procedures with customizable hyperparameters
• Inference pipeline for using trained models

Visuals

Below are some visuals illustrating the architecture and components:

Getting Started

1. Clone the repository:

   git clone <repository-url>
   cd Transformer_from_scratch_PyTorch

2. Install dependencies:

   pip install -r requirements.txt

3. Explore the implementation:

   • See the model implementation in transformers/model.py
   • Check out example usage in the examples/ directory
   • Read through the code documentation for detailed explanations

4. Train your own model:

   python -m transformers.training --config config.json

5. Run inference:

   python -m transformers.inference --model model.pt --input "Your input text"

Architecture Details

This implementation follows the original Transformer architecture with:

• Multi-head attention mechanisms
• Position-wise feed-forward networks
• Layer normalization
• Residual connections
• Positional encoding

For detailed explanations of each component, refer to the documentation and comments in the code.

Contributing

Feel free to contribute by submitting issues or pull requests. Contributions to improve the implementation, documentation, or examples are welcome.

License

This project is licensed under [Your License Here]. Replace this section with the appropriate license information.