As I pondered the topic for my next series, the idea of explaining how the attention mechanism works immediately stood out. Indeed, when launching a new series, starting with the fundamentals is a wise strategy, and Large Language Models (LLMs) are the talk of the town.
However, the internet is already saturated with stories about attention — its mechanics, its efficacy, and its applications. So, if I want to keep you from snoozing before we even start, I have to find a unique perspective.
So, what if we explore the concept of attention from a different angle? Rather than discussing its benefits, we could examine its challenges and propose strategies to mitigate some of them.
With this approach in mind, this series will focus on FlashAttention: a fast and memory-efficient exact Attention with IO-awareness. This description might seem overwhelming at first, but I’m confident everything will become clear by the end.
Learning Rate is a newsletter for those who are curious about the world of ML and MLOps. If you want to learn more about topics like this subscribe here.
This series will follow our customary format: four parts, with one installment released each week.