Coding a Transformer from scratch on PyTorch

Transformers have gained immense popularity in the field of natural language processing (NLP) due to their ability to capture long-range dependencies and effectively model sequential data. In this article, we will walk through the process of coding a Transformer from scratch using PyTorch, and then we will cover the training and inference steps.

1. Setting up the environment

Before we begin coding the Transformer, we need to set up our environment. We will need to install PyTorch, a popular deep learning framework, if we haven’t done so already. We can install it using the following command:


$ pip install torch torchvision


2. Coding the Transformer

Now, let’s move on to coding the Transformer. We will define the Transformer architecture as a class in PyTorch, and it will consist of the following components:

• Embedding layers for the input and output sequences
• Positional encoding to provide information about the position of tokens in the input sequence
• Encoder and decoder layers with self-attention and feedforward neural networks

We will initialize the parameters of the model and define the forward method to perform the forward pass through the network. This will involve passing the input sequence through the embedding layer, adding positional encoding, and then passing it through the encoder and decoder layers to generate the output sequence.

3. Training the Transformer

Once we have coded the Transformer, we can move on to training it. We will need a dataset of input and output sequences to train the model. We can use a dataset such as the WMT14 English-German translation dataset for this purpose. We will define a dataloader to load batches of input and output sequences, and then we can use the Adam optimizer to train the model using the mean squared error loss.

4. Inference with the Transformer

After training the Transformer, we can use it for inference on new input sequences. We can input a sequence to the model, and it will generate an output sequence by predicting the next token at each step. We can then use the output sequence as the translated version of the input sequence.

Overall, coding a Transformer from scratch on PyTorch involves defining the architecture of the model, training it on a dataset, and using it for inference. This process allows us to understand the inner workings of the Transformer and gain insights into how it can be applied to real-world NLP tasks.