jax VS equinox

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!

I wrote a JAX-based neural network library (Equinox [1]) and numerical differential equation solving library (Diffrax [2]).

At the time I was just exploring some new research ideas in numerics -- and frankly, procrastinating from writing up my PhD thesis!

But then one of the teams at Google starting using them, so they offered me a job to keep developing them for their needs. Plus I'd get to work in biotech, which was a big interest of mine. This was a clear dream job offer, so I accepted.

Since then both have grown steadily in popularity (~2.6k GitHub stars) and now see pretty widespread use! I've since started writing several other JAX libraries and we now have a bit of an ecosystem going.

[1] https://github.com/patrick-kidger/equinox

The elevator pitch is Optimistix is really fast, especially to compile. It plays nicely with Optax for first-order gradient-based methods, and takes a lot of design inspiration from Equinox, representing the state of all the solvers as standard JAX PyTrees.

If you like PyTorch then you might like Equinox, by the way. (https://github.com/patrick-kidger/equinox ; 1.4k GitHub stars now!)

You're thinking of `jax.closure_convert`. :)

(Although technically that works by tracing and extracting all constants from the jaxpr, rather than introspecting the function's closure cells -- it sounds like your trick is the latter.)

When you discuss dynamic allocation, I'm guessing you're mainly referring to not being able to backprop through `jax.lax.while_loop`. If so, you might find `equinox.internal.while_loop` interesting, which is an unbounded while loop that you can backprop through! The secret sauce is to use a treeverse-style checkpointing scheme.

https://github.com/patrick-kidger/equinox/blob/f95a8ba13fb35...

I'm a big fan of using ABCs to declare interfaces -- so much so that I have an improved abc.ABCMeta that also handles abstract instance variables and abstract class variables: https://github.com/patrick-kidger/equinox/blob/main/equinox/_better_abstract.py

For the daily research, I use Equinox (https://github.com/patrick-kidger/equinox) as a DL librarry in JAX.

It sounds like you're concerned about how downstream libraries tend to wrap JAX transformations to handle their own thing? (E.g. `haiku.grad`.)

If so, then allow me to make my usual advert here for Equinox:

https://github.com/patrick-kidger/equinox

This actually works with JAX's native transformations. (There's no `equinox.vmap` for example.)

On higher-order functions more generally, Equinox offers a way to control these quite carefully, by making ubiquitous use of callables that are also pytrees. E.g. a neural network is both a callable in that it has a forward pass, and a pytree in that it records its parameters in its tree structure.