Harnessing Pure Functions for Gradient Computation

Automatic differentiation requires the ability to compute gradients efficiently, particularly in optimized environments like GPUs or TPUs. Compilers play a crucial role in facilitating this process by generating the backward pass for annotated functions at compile time, given that the functions used are compatible. Swift is positioned as a superior language for this task compared to Python because it promotes a functional programming approach with less statefulness, enabling the use of pure functions that align more logically with derivative computations. Furthermore, the compiler's enhanced understanding of the function's behavior in Swift allows for better optimization and error checking in the differentiation process.