A Deep Dive Into Decoders


Remember I mentioned we would revisit the Jay Alammar blogs after The Illustrated Transformer? Well, here we are, on our way to review the nitty-gritty of GPT-2's implementation of the decoder-only architecture. By the end of this section you'll be all caught up with as far as I made it in free YouTube ML self-study: coding GPT from scratch with Andrej Karpathy.

The Illustrated GPT-2

This blog post is one of the best examples of educational media on technical subjects I've honestly ever come across.

There are two things about this post (opens in a new tab) that make it an absolutely top tier example of good instructional design. One are those stunningly intuitive and clever diagrams of the query, key, and value process behaving like a post-it note being used to search through a variety of manila folders. The other is how painstaking he is about showing you every single parameter in every matrix of the entire system as a whole.

query, key, value

This blessed diagram is the moment the query, key, and value system of self-attention finally clicked once and for all for me.

This is what I mean about Alammar's hard work in displaying every single matrix multiplication in the architecture: spreadsheet

Every single one of those matrices in the C column (starting with attn/c_attn and going to ln_2) have their dimensions and parameters calculated and summed. It's all there to see. What a legend.

Let's Build GPT with Andrej Karpathy

And now you're ready for what should hopefully feel like a victory lap. Karpathy starts very small in developing ultra-simplified micro-examples of the decoder-only architecture before gradually scaling the learner up to the real thing. As an added bonus, his code is remarkably instructive and terse--real wizard level skills there.

This is as far as I've gotten in my ML self-study journey at YouTube University. In the next section, I'll list some stuff I hope to study next.