The Illustrated Transformer

The Illustrated Transformer Discussions: Hacker News (65 points, 4 comments) , Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic , Chinese (Simplified) 1 , Chinese (Simplified) 2 , French 1 , French 2 , Italian , Japanese , Korean , Persian , Russian , Spanish 1 , Spanish 2 , Vietnamese Watch: MIT’s Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford , Harvard , MIT , Princeton , CMU and others Update: This post has now become a book! Check out LLM-book.com which contains (Chapter 3) an updated and expanded version of this post speaking about the latest Transformer models and how they've evolved in the seven years since the original Transformer (like Multi-Query Attention and RoPE Positional embeddings). In the previous post, we looked at Attention - a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer - a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions. The Transformer was proposed in the paper Attention is All You Need . A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation . In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter. 2025 Update : We’ve built a free short course that brings the contents of this post up-to-date with animations: A High-Level Look Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another. Popping open that Optimus Prime goodness, we see an encoding component, a decoding component, and connections between them. The encoding component is a stack of encoders (the paper stacks six of them on top of each other - there’s nothing magical about the number six, one can definitely experiment with other arrangements). The decoding component is a stack of decoders of the same number. The encoders are all identical in structure (yet they do not share weights). Each one is broken down into two sub-layers: The encoder’s inputs first flow through a self-attention layer - a layer that helps the encoder look at other words in the input sentence as it encodes a specific word. We’ll look closer at self-attention later in the post. The outputs of the self-attention layer...

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