GPT-2 from Scratch in C (Day 1/2)

Introduction

This tutorial guides you through the process of building a GPT-2 model from scratch using the C programming language. It is aimed at developers and machine learning enthusiasts who want to understand the underlying mechanics of GPT-2 and implement it independently. By the end of this tutorial, you'll have a foundational understanding of the model architecture and the coding techniques necessary to bring it to life.

Step 1: Setting Up Your Environment

To get started, you need to prepare your development environment for coding in C.

• Install a C compiler: Ensure you have a C compiler installed, such as GCC.
• Choose an IDE or text editor: Use an Integrated Development Environment (IDE) like Code::Blocks or a simple text editor like Visual Studio Code.
• Clone the source code: Download the starter code from the provided GitHub repository.

  git clone https://github.com/rkaehn/gpt-2
  cd gpt-2
  

Step 2: Understanding the Model Architecture

Before diving into the coding, familiarize yourself with the architecture of GPT-2.

• Transformer Architecture: GPT-2 is based on the transformer architecture, which consists of:
  • Multi-head self-attention mechanisms
  • Feed-forward neural networks
  • Layer normalization
• Key Components: Understand the role of the encoder and decoder. GPT-2 primarily uses the decoder part.

Step 3: Implementing the Input Processing

Next, you will implement the code to process input data for the model.

• Tokenization: Break down input text into tokens. This can be achieved using a predefined tokenizer or by implementing a simple one.
  • Example of a simple tokenizer function:

    void tokenize(char* input) {
        // Tokenization logic here
    }
    

• Encoding: Convert tokens into numerical representations that the model can understand.

Step 4: Building the Model

Now, you can start constructing the GPT-2 model itself.

• Define the model structure:
  • Create a structure to hold model parameters such as weights and biases.
  • Implement functions for the multi-head self-attention mechanism.
• Initialize the parameters: Set up random values for weights and biases for the model's layers.

Step 5: Training the Model

Once the model is built, the next step is to train it using a dataset.

• Select a dataset: Choose a text corpus to train your model.
• Implement the training loop:
  • Forward pass: Compute predictions based on current model parameters.
  • Backward pass: Update model parameters using gradient descent.
  • Example of a training loop:

    for (epoch = 0; epoch < num_epochs; epoch++) {
        forward_pass();
        backward_pass();
    }
    

Step 6: Evaluating the Model

After training, you'll need to evaluate the performance of your GPT-2 model.

• Generate samples: Use the trained model to generate text samples.
• Assess quality: Evaluate the coherence and relevance of the generated text.

Conclusion

In this tutorial, you learned how to set up an environment for building a GPT-2 model in C, understood the model's architecture, and implemented key components including input processing and training. As a next step, you can experiment with different datasets, optimize your model's parameters, or dive deeper into advanced features like fine-tuning and adjusting hyperparameters for better performance. Happy coding!