JAX is famous for its speed, efficiency, and flexibility when working with mathematical computations and machine learning. But one of its lesser-known superpowers — something that can save you from writing endless loops and boilerplate code — is automatic vectorization.
If you’ve ever written code that processes arrays or batched data, you know how tedious it can be to optimize for parallelism. But with JAX’s vmap (vectorization map) function, you can say goodbye to ugly loops and hello to concise, efficient, and parallelized code.
In this article, we’re going to dive deep into automatic vectorization in JAX. We’ll explore how vectorization works, why it’s essential for speeding up computations, and how you can leverage JAX’s vmap to avoid writing explicit loops. Along the way, we’ll take some real-world examples and walk through code that will make you love JAX even more.
Ready? Let’s go!
Before we get into JAX specifics, let’s talk about vectorization in general. In traditional programming, you might write code that processes one data point at a time in a loop. For example, if you want to apply a function to every element of an array, you’d probably use a for loop…