StaticLint.jl VS Enzyme

Yes, tooling around this is being developed in the form of linters (e.g. https://github.com/julia-vscode/StaticLint.jl) and through real compiler integration tools like the very cool https://aviatesk.github.io/JET.jl/dev/ but this is definitely somewhere that the tooling in julia is weaker than in other languages. It seems to be picking up a lot of speed though.

It is correct if `A` is of type `Array` as normal Array in julia has 1-based indexing. It is incorrect if `A` is of some other type which subtypes `AbstractArray` as these may not follow 1-based indexing. But this case errors normally due to bounds checking. The OP talks about the case where even bounds checking is turned off using `@inbounds` for speed and thus silently giving wrong answers without giving an error.

An issue was created sometime ago in StaticLint.jl to fix this: https://github.com/julia-vscode/StaticLint.jl/issues/337

Statically lint = find errors in the Julia file like using variables that are not defined, and functions with the wrong arguments. For Julia, StaticLint.jl is an actively developed library that does static linting. It basically provides a bunch of functions that spit out errors in your Julia file like those that I mentioned above. If you are an Emacs editor user, this project is like a "convenience" which will run Julia silently in the background, and communicate with it to extract errors in the file that you currently have open. These errors are then highlighted in your editor view using the Flycheck package that is one of the ways to highlight errors in Emacs.

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.

Enzyme dev here, so take everything I say as being a bit biased:

While, by design Enzyme is able to run very fast by operating within the compiler (see https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b... for details) -- it aggressively prioritizes correctness. Of course that doesn't mean that there aren't bugs (we're only human and its a large codebase [https://github.com/EnzymeAD/Enzyme], especially if you're trying out newly-added features).

Notably, this is where the current rough edges for Julia users are -- Enzyme will throw an error saying it couldn't prove correctness, rather than running (there is a flag for "making a best guess, but that's off by default"). The exception to this is garbage collection, for which you can either run a static analysis, or stick to the "officially supported" subset of Julia that Enzyme specifies.

Incidentally, this is also where being a cross-language tool is really nice -- namely we can see edge cases/bug reports from any LLVM-based language (C/C++, Fortran, Swift, Rust, Python, Julia, etc). So far the biggest code we've handled (and verified correctness for) was O(1million) lines of LLVM from some C++ template hell.

I will also add that while I absolutely love (and will do everything I can to support) Enzyme being used throughout arbitrary Julia code: in addition to exposing a nice user-facing interface for custom rules in the Enzyme Julia bindings like Chris mentioned, some Julia-specific features (such as full garbage collection support) also need handling in Enzyme.jl, before Enzyme can be considered an "all Julia AD" framework. We are of course working on all of these things (and the more the merrier), but there's only a finite amount of time in the day. [^]

[^] Incidentally, this is in contrast to say C++/Fortran/Swift/etc, where Enzyme has much closer to whole-language coverage than Julia -- this isn't anything against GC/Julia/etc, but we just have things on our todo list.

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

https://github.com/EnzymeAD/Enzyme

I'm working on merging my build-tool for (oxide)-enzyme into Enzyme itself. Also looking into improving the documentation.

that seems one of the points of enzyme[1], which was mentioned in the article.

[1] - https://enzyme.mit.edu/

being able in effect do interprocedural cross language analysis seems awesome.