kotlingrad VS Enzyme

and that there is a mature library for autodiff https://github.com/breandan/kotlingrad

It makes use of https://github.com/EnzymeAD/enzyme which is an LLVM plugin and since Rust also uses LLVM in its backend, we can enable this plugin in our Rust toolchain when autodiff is enabled. So, it is a bit of compiler black magic rather than a direct implementation in the standard library.

I believe Enzyme comes close to what you describe. It works on the LLVM IR level. https://enzyme.mit.edu

Automatic differentiation is done using https://enzyme.mit.edu/

lattner left google and was the primary reason they chose swift, so they lost interest.

if you're asking from an ML perspective, i believe the original motivation was to incorporate automatic differentiation in the swift compiler. i believe enzyme is the spiritual successor.

https://github.com/EnzymeAD/Enzyme

https://ispc.github.io/ispc.html

For the auto-differentiation when I need performance or memory, I currently use tapenade ( http://tapenade.inria.fr:8080/tapenade/index.jsp ) and/or manually written gradient when I need to fuse some kernel, but Enzyme ( https://enzyme.mit.edu/ ) is also very promising.

MPI for parallelization across machines.

For autodiff, we are currently working again on publishing a new Enzyme (https://enzyme.mit.edu) Frontend for Rust which can also handle pure Rust types, first version should be done in ~ a week.

Have you taken a look at enzymeAD? There is a group porting it to rust.

Idk, Enzyme is pretty next gen, all the way down to LLVM code.

https://github.com/EnzymeAD/Enzyme