cranelift-jit-demo VS jax

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

The dual numbers exist just as surely as the real numbers and have been used well over 100 years

https://en.m.wikipedia.org/wiki/Dual_number

Pytorch has had them for many years.

https://pytorch.org/docs/stable/generated/torch.autograd.for...

JAX implements them and uses them exactly as stated in this thread.

https://github.com/google/jax/discussions/10157#discussionco...

As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.

On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.

Source: https://github.com/google/jax/discussions/5199#discussioncom...

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html

There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...

I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.

But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566

>

Are they even comparing apples to apples to claim that they see these improvements over NumPy?

> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.

NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.

[1] https://github.com/google/jax

Actually that never changed. The README has always had an example of differentiating through native Python control flow:

https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...

The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!

For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...

2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)

Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:

3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)

4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...

Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.

Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1

The simplest example might be {0}^* in which case

0: “” // because we use *

Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!