In this tutorial, we will build a basic Transformer model from scratch using PyTorch. The Transformer model, introduced by Vaswani et al. in the paper “Attention is All You Need,” is a deep learning architecture designed for sequence-to-sequence tasks, such as machine translation and text summarization. It is based on self-attention mechanisms and has become the foundation for many state-of-the-art natural language processing models, like GPT and BERT.
To understand Transformer models in detail kindly visit these two articles:
1. All you need to know about ‘Attention’ and ‘Transformers’ — In-depth Understanding — Part 1
2. All you need to know about ‘Attention’ and ‘Transformers’ — In-depth Understanding — Part 2
To build our Transformer model, we’ll follow these steps:
- Import necessary libraries and modules
- Define the basic building blocks: Multi-Head Attention, Position-wise Feed-Forward Networks, Positional Encoding
- Build the Encoder and Decoder layers
- Combine Encoder and Decoder layers to create the complete Transformer model
- Prepare sample data
- Train the model
Let’s start by importing the necessary libraries and modules.
import torch import torch.nn as nn import torch.optim as optim import torch.utils.data as data import math import copy
Now, we’ll define the basic building blocks of the Transformer model.