Title: Building GPT-1 from Scratch Using NumPy: A Step-by-Step Guide
Introduction: Building a transformer model like GPT-1 from scratch can be an incredibly rewarding experience. By understanding the core components and implementing them using only NumPy, you can gain a deep appreciation for the underlying mechanics of these powerful models. In this guide, we will walk through the process of constructing a simplified version of GPT-1, explaining each step along the way.
Detailed Explanation: Hyperparameters We define the dimensions for the model and the feedforward network, along with the sequence length.
Initialize Parameters This function initializes the weights and biases for our transformer model. The weights are initialized with small random values, while biases are initialized to zero.
Self-Attention The self-attention mechanism calculates the attention scores and applies the softmax trick for numerical stability. The output is a weighted sum of the values.
Multi-Head Attention This function applies linear projections to the input and then uses the self-attention mechanism. The output is further transformed using another linear projection.
Feedforward Network A simple feedforward network with a ReLU activation function. This is applied after the attention mechanism to each position separately.
Transformer Block A single transformer block consists of a multi-head attention mechanism followed by a feedforward network. Both have residual connections and normalization.
Forward Pass and Loss Calculation We simulate the forward pass through multiple transformer blocks, as in GPT-1. The loss is computed as the mean squared error between the output and the target labels.
Conclusion: Building a simplified version of GPT-1 from scratch using NumPy provides a great opportunity to understand the core concepts behind transformer models. This hands-on approach demystifies the complexity and gives you a solid foundation to build upon for more advanced models. Feel free to share your thoughts and improvements on this implementation!