Flash Attention Tutorial

Welcome to the Flash Attention Tutorial repository! This repository provides an in-depth tutorial on the concept of Flash Attention, including high-level intuition, detailed explanation, and practical implementation.

Repository Overview

  • Notebook: Flash Attention.ipynb
    • This Jupyter Notebook contains all the tutorial content, including explanations and code implementations.

Contents

  1. Introduction

    • Overview of attention mechanisms and their significance in neural networks.
    • High-level intuition behind Flash Attention.
  2. Detailed Explanation

    • In-depth discussion on how Flash Attention reduces memory usage, speeds up computations, and maintains accuracy.
    • Comparison with traditional attention mechanisms.
  3. Implementation

    • Step-by-step implementation of Flash Attention using PyTorch.
    • Example usage and demonstration of the implemented Flash Attention mechanism.

Key Features

  • Efficient Memory Usage: Learn how Flash Attention minimizes memory requirements by processing smaller chunks of the sequence.
  • Enhanced Computational Speed: Understand how modern hardware capabilities and optimized memory access patterns improve computation time.
  • Maintained Accuracy: See how Flash Attention retains the accuracy of traditional attention mechanisms while offering efficiency improvements.

Usage

  1. Clone the Repository

    git clone https://github.com/galenwilkerson/Flash-Attention-Tutorial.git
    cd Flash-Attention-Tutorial
  2. Open the Jupyter Notebook

    Launch Jupyter Notebook in your terminal:

    jupyter notebook

    Navigate to Flash Attention.ipynb and open it to start exploring the tutorial.

Requirements

  • Python: 3.6 or higher
  • PyTorch: 1.7.0 or higher
  • Jupyter Notebook: 6.0.3 or higher

You can install the required packages using pip:

pip install torch jupyter

Contributing

We welcome contributions to improve this tutorial. If you have suggestions or find any issues, please feel free to create a pull request or open an issue.

License

This project is licensed under the MIT License.


We hope you find this tutorial helpful and insightful! Happy learning and coding!


Galen Wilkerson


Feel free to explore the notebook and enhance your understanding of Flash Attention!