Implementing a simple GPT in PyTorch (Take Two)

PyTorch is a popular machine learning framework that provides a flexible and efficient way to build and train neural networks. In this article, we will walk through the steps to implement a simple Generative Pre-trained Transformer (GPT) model in PyTorch.

GPT Overview

GPT is a type of transformer-based model that is designed to generate text based on a given prompt. It uses self-attention mechanisms to capture the relationships between words in a text sequence, enabling it to produce coherent and contextually relevant responses.

Implementing GPT in PyTorch

To build a simple GPT model in PyTorch, we will need to define the architecture of the transformer and create a training loop to optimize its parameters. Here are the basic steps involved:

1. Define the transformer architecture
2. Prepare the training data
3. Train the GPT model
4. Evaluate the model performance

Sample Code

        
import torch
import torch.nn as nn
from torch.nn.functional import softmax

class GPT(nn.Module):
    def __init__(self, vocab_size, d_model, max_seq_len, n_layers, n_heads):
        super(GPT, self).__init__()
        self.embed = nn.Embedding(vocab_size, d_model)
        self.pe = PositionalEncoding(d_model, max_seq_len)
        self.encoder_layers = nn.TransformerEncoderLayer(d_model, n_heads)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layers, n_layers)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, x):
        x = self.embed(x)
        x = self.pe(x)
        output = self.transformer_encoder(x)
        output = self.fc(output)
        return softmax(output, dim=-1)
        
    

Conclusion

Implementing a simple GPT model in PyTorch is a great way to understand the inner workings of transformer-based models and learn how to apply them to real-world problems. By following the steps outlined in this article, you can get started with building and training your own GPT models in PyTorch.