cranelift-jit-demo VS Enzyme

I'm working on a small stack-based programming language. I'm currently at a stage where I'm trying to compile it using Cranelift. Altrough the Cranelift documentation is extensive, I'm lacking a broader picture on how to approach some things like heap-allocations and stack-management. The only example project I found are cranelift-jit-demo and this wonderful post.

First, it's much easier than you think, I swear. I strongly suggest that you start with the cranelift JIT toy language demo, it has everything that you need to get started.

Fyi I've checked a few (from here; https://github.com/appcypher/awesome-wasm-langs): - assembly script complier is written is typescript/javascript and in theory could be compiled to wasm, and hence could be embedded, but it is only theory as noone has managed to complete this flow - rust-driver requires the linker and calls it as an external tool to link the rustcore to the user code. without the core lib i could not manage to create anything usable. - zig (somewhat similar to rust): on discord some experr said it cannot be embedded and he see no option/plan for it. - lua: they have lua runtime running in wasm, but no transpiller to wasm I've also checked a few other without any success and closest I coild get was the example language for cranelift (https://github.com/bytecodealliance/cranelift-jit-demo)

JITs are somehow more tricky and differ in the a few points including: a) Codegen is much more time critical. b) JITs must know what's allready generated and what isn't. c) JITs often rely on informations only generated at runtime and must respond to that. See here for a JIT example witten with cranelift: https://github.com/bytecodealliance/cranelift-jit-demo.

You could also try Cranelift. The resulting code isn't as optimized as with LLVM, but it's faster and pleasant to use (and is written in Rust).

Backend IRs for code generation: - Cranelift (see https://github.com/bytecodealliance/cranelift-jit-demo as well as the messages on the Zulip chat if you get stuck)

As for LLVM, I'm not sure if there are any tutorials but I would really advise writing a bytecode interpreter first, unless you already have some grasp of assembly. However, this repository: https://github.com/bytecodealliance/cranelift-jit-demo is really great for learning cranelift which is essentially an LLVM alternative.

It was mainly built for wasm compilation. So no it is not married to rust. https://github.com/bytecodealliance/cranelift-jit-demo

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.