20211208
Our talk yesterday was about using Transformer models to win a Kaggle competition at a training cost of less than $50 on Azure. Fortunately, Alon understands what Transformer models are, and he did a wonderful job of summarizing what Transformer model is in about 5 minutes.
I really wanted to learn more about Transforrmer models; I've been treating them mostly as a black box and using pretrained Transformer models to make cool things like my semantic wine review search engine. Coincidentally, this morning someone from work linked this tweet:
and he subsequently found a link to this fantastic explanation of Transformer models called Transformers From Scratch written by Brandon Roher (hi Brandon!) I'm still working through the piece but one thing that I had not understood before was how matrix multiplication and onehot encoded vectors are used to do branchfree selection of rows from a table. Let that sink in for a minute: how would you do that WITHOUT COMPARISONS and BRANCHING? ðŸ¤¯
Apparently this is one of the key insights from Transformers. There's a whole lot of branching and comparing in this Python list comprehension:
[x for x in ['fizz','buzz','fizz','fizz'] if x == 'fizz']
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Continuing to take notes on Brandon's tutorial on the flight home. There is a concept of selective masking that is in the original Transformers paper. Here is an annotated Attention is All You Need paper.
In his explanation, there are a large number of unhelpful predictions where there is a 50:50 probability of some outcome in his highly simplified example. Masking is a concept to drive low probability events to zero to eliminate them from consideration, and is the central idea in Transformers.
He summarizes the first part of his explanation through three ideas:

Turning everything into matrix multiplication is a good idea. As I observed above, being able to select rows out of a table (or matrix) by doing nothing more than matrix multiplication is incredibly efficient.

Each step during training must be differentiable, i.e., each adjustment to a parameter must result in a calculation of the model error / loss function.

Having smooth gradients is really important, He has a nice analogy between ML gradients and hills/mountains/valleys in the real world. He describes the art of data science in ensuring that the gradients are smooth and "wellconditioned", i.e., they shouldn't quickly drop to zero or rise to infinity.