[한글자막] Coding a Transformer from scratch on PyTorch

If you are interested in natural language processing and machine learning, you might have heard of the Transformer model. The Transformer is a neural network architecture that has revolutionized the field of NLP, and it is the backbone of many state-of-the-art language models such as BERT and GPT-3. In this article, we will walk through the process of coding a Transformer from scratch using PyTorch, with a full explanation of the architecture, training, and inference.

What is a Transformer?

The Transformer is a type of neural network architecture that is based on a self-attention mechanism. It was introduced in a paper by Vaswani et al. in 2017 and has since become the go-to model for many NLP tasks. The key idea behind the Transformer is the use of self-attention to capture long-range dependencies in the input sequence, which makes it well-suited for processing natural language.

Coding a Transformer in PyTorch

We will now walk through the process of coding a simple Transformer from scratch using PyTorch. We will define the architecture of the model, implement the training loop, and demonstrate how to use the trained model for inference.

Define the Transformer architecture

First, we will define the architecture of the Transformer model. This includes the encoder and decoder modules, as well as the self-attention and feed-forward layers. We will use the nn.Module class in PyTorch to define the model and its components.

Implement the training loop

Next, we will implement the training loop for the Transformer model. This includes defining the loss function, optimizer, and iterating over the training data to update the model’s parameters. We will also monitor the training progress by calculating the accuracy and other metrics.

Use the trained model for inference

Finally, we will demonstrate how to use the trained Transformer model for inference. This involves passing an input sequence through the model, generating the output sequence, and decoding the generated tokens into human-readable text.

Conclusion

In this article, we have walked through the process of coding a Transformer from scratch using PyTorch. We have discussed the architecture of the model, implemented the training loop, and demonstrated how to use the trained model for inference. We hope that this article has provided you with a better understanding of the Transformer model and how it can be implemented in practice.