Let's build GPT: from scratch, in code, spelled out.

Introduction

In this tutorial, we will build a Generatively Pretrained Transformer (GPT) from scratch, following the principles outlined in the "Attention is All You Need" paper and leveraging concepts from OpenAI's GPT-2 and GPT-3. This guide will help you understand the architecture and implementation of a transformer model, offering practical insights and coding examples along the way.

Step 1: Setting Up Your Environment

• Use Google Colab for an easy-to-access coding environment.
• Clone the GitHub repository for the video here.
• Familiarize yourself with the earlier makemore videos to grasp the autoregressive language modeling framework.

Step 2: Exploring and Preparing the Data

• Read and load your dataset, ensuring you understand its structure and content.
• Implement tokenization to convert raw text into numerical representations.
• Split your data into training and validation sets:
  • Use a simple ratio like 80/20 for training/validation.

Step 3: Creating a Data Loader

• Implement a data loader that creates batches of data chunks:
  • Ensure that it can handle the input size for the model efficiently.
• Use PyTorch's DataLoader class for better management of batches.

Step 4: Building a Basic Language Model

• Start with a simple bigram language model:
  • Calculate the loss using CrossEntropyLoss.
  • Generate text samples for qualitative evaluation.
• Train the bigram model on your dataset to establish a baseline performance.

Step 5: Implementing Self-Attention

• Build the self-attention mechanism:
  • Begin with a basic averaging method over past contexts using for loops.
  • Transition to matrix multiplication for weighted aggregation.

Code Example for Self-Attention

def self_attention(query, key, value):
    scores = torch.matmul(query, key.transpose(-2, -1))
    weights = torch.nn.functional.softmax(scores, dim=-1)
    output = torch.matmul(weights, value)
    return output

Step 6: Enhancing Self-Attention with Softmax

• Integrate softmax into your self-attention mechanism to normalize the scores.
• Ensure that your model can handle varying input sizes.

Step 7: Adding Positional Encoding

• Implement positional encoding to give the model a sense of word order:
  • This allows the transformer to understand the sequence of input data.

Step 8: Constructing the Transformer Architecture

• Assemble the transformer by stacking self-attention blocks.
• Implement multi-headed self-attention for better context understanding.
• Add feedforward layers and residual connections:
  • This helps in retaining important information across layers.

Step 9: Normalization and Regularization

• Use Layer Normalization instead of Batch Normalization within the transformer architecture.
• Implement dropout to prevent overfitting.

Step 10: Scaling the Model

• Adjust hyperparameters to scale up the model as needed:
  • Consider increasing the size of the hidden layers and attention heads.

Step 11: Training and Fine-Tuning

• Train your model on a substantial dataset.
• Fine-tune the pre-trained model on a smaller dataset (e.g., Tiny Shakespeare) for specific tasks:
  • Use a lower learning rate during fine-tuning.

Conclusion

In this tutorial, we covered the essential steps to build a GPT from scratch, including data preparation, model architecture, and training strategies. You can explore additional exercises by training the model on unique datasets or implementing advanced features from recent transformer papers. For further learning, check the supplementary links provided in the video description and consider joining discussions in the provided Discord channel.