To understand the differences between automatic differentiation libraries, let’s talk about the engineering trade-offs that were made. I would personally say that none of these libraries are “better” than another, they simply all make engineering trade-offs based on the domains and use cases they were aiming to satisfy. The easiest way to describe these trade-offs is to follow the evolution and see how each new library tweaked the trade-offs made of the previous.

Early TensorFlow used a graph building system, i.e. it required users to essentially define variables in a specific graph language separate from the host language. You had to define “TensorFlow variables” and “TensorFlow ops”, and the AD would then be performed on this static graph. Control flow constructs were limited to the constructs that could be represented statically. For example, an `ifelse` function statement is very different from … READ MORE