stackformer 0.1.2

Modular transformer blocks built in PyTorch

🧱 Stackformer

Stackformer is a modular transformer-building framework written entirely in PyTorch. It is designed primarily for experimentation, providing various transformer blocks such as attention mechanisms, normalization layers, feed-forward networks, and a simple model architecture. The project is a work-in-progress with plans for further enhancements and expansions.

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📖 About Me

My name is Gurumurthy, and I am a final-year Bachelor of Engineering student from India. I created this library as my own size project to showcase my skills and knowledge in deep learning and transformer architectures.

I am also interested and free to work with others on different projects for knowledge sharing and building connections.

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🌟 Features

• Multiple attention mechanisms including multi-head, group query, linear, local, and KV cache variants
• Token embedding via tiktoken
• Absolute and sinusoidal positional embeddings
• Normalization layers like LayerNorm and RMSNorm
• Several feed-forward network variants with activations such as ReLU, GELU, SiLU, LeakyReLU, and Sigmoid
• A simple GPT-style transformer model implementation

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📁 Project Structure

stackformer/
|-- modules/
| |-- tokenizer.py # Token embedding using tiktoken
| |-- position_embedding.py # Absolute and sinusoidal embeddings
| |-- Attention.py # Attention mechanisms
| |-- Normalization.py # LayerNorm and RMSNorm
| |-- Feed_forward.py # Feed-forward layers with various activations
|-- models/
| -- GPT_2.py # GPT-style transformer stack model
-- trainer.py # Training loop and utilities \

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💻 Installation

✅ Method 1: Install from PyPI:

pip install Stackformer
import stackformer

🔧 Method 2: Clone the repository:

git clone https://github.com/Gurumurthy30/Stackformer
cd Stackformer
pip install -e .

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🚀 Future Plans

Currently, I am working on improving and optimizing the existing components while fixing known bugs and issues. After stabilizing the current modules, I plan to add more advanced blocks like Mixture of Experts (MoE), mask handling, and other essential transformer components. Eventually, I will expand the library by developing more comprehensive model architectures.