jax VS bevy

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 don't see WASM/WebGPU changing anything when it comes to gaming, as an industry, personally. 3d visualizations and interactive websites? Yeah definitely a nice improvement over WebGL 2, if years late.

WebGPU is pretty far behind what AAA games are using even as of 6 years ago. There's extra overhead and security in the WebGPU spec that AAA games do not want. Browsers do not lend themselves to downloading 300gb of assets.

Additionally, indie devs aren't using Steam for the technical capabilities. It's purely about marketshare. Video games are a highly saturated market. The users are all on Steam, getting their recommendations from Steam, and buying games in Steam sales. Hence all the indie developers publish to Steam. I don't see a web browser being appealing as a platform, because there's no way for developers to advertise to users.

That's also only indie games. AAA games use their own launchers, because they don't _need_ the discoverability from being on Steam. So they don't, and avoid the fees. If anything users _want_ the Steam monopoly, because they like the platform, and hate the walled garden launchers from AAA companies.

(I work on high end rendering features for the Bevy game engine https://bevyengine.org, and have extensive experience with WebGPU)

I was working through an example in the repo for the Bevy game engine recently and came across this code

That's possible. I did spend quite a bit of time tinkering with compiler flags, and followed the recommendations.

Some notes I found just now seems to agree with my results, though: https://github.com/bevyengine/bevy/issues/3978#issuecomment-...

I cannot recommend immediate mode GUI programming based on the limitations I've experienced working with egui.

egui does not support putting two widgets in the center of the screen: https://github.com/emilk/egui/issues/3211

It's really easy to get started with immediate mode, it's really easy to bust out some UI, but the second you start trying to involve dynamically resized context and responsive layouts -- abandon all hope. The fact it has to calculate everything in a single pass makes these things hard/impossible.

... that said, I'm still using it for https://ant.care/ (https://github.com/MeoMix/symbiants) because it's the best thing I've found. I'm crossing my fingers that Bevy's UI story (or Kayak https://github.com/StarArawn/kayak_ui) become significantly more fleshed out sooner rather than later. Bevy 0.13 should have lots more in this area though (https://github.com/bevyengine/bevy/discussions/9538)

I've also been enjoying building My First Game™ in Bevy using ECS. The community around Bevy really shines, but Flecs (https://github.com/SanderMertens/flecs) is arguably a more mature, open-source ECS implementation. You don't get to write in Rust, though, which makes it less cool in my book :)

I'm not very proud of the code I've written because I've found writing a game to be much more confusing than building websites + backends, but, as the author notes, it certainly feels more elegant than OOP or globals given the context.

I'm building for WASM and Bevy's parallelism isn't supported in that context (yet? https://github.com/bevyengine/bevy/issues/4078), so the performance wins are just so-so. Sharing a thread with UI rendering suuucks.

If anyone wants to browse some code or ask questions, feel free! https://github.com/MeoMix/symbiants

These days, some game engines have done pretty well at making compute shaders easy to use (such as Bevy [1] -- disclaimer, I contribute to that engine). But telling the scientific/financial/etc. community that they need to run their code inside a game engine to get a decent experience is a hard sell. It's not a great situation compared to how easy it is on NVIDIA's stack.

[1]: https://github.com/bevyengine/bevy/blob/main/examples/shader...

This is the API you need: https://github.com/bevyengine/bevy/pull/9774

Most of Nanite (at least, everything but the LOD system, I haven't tried that part, and the compute rasterizer due to lack of storage image atomics because Metal lacks them...) is implementable in WebGPU actually.

I have a PR that does a lot of the same things (meshlets, visbuffer, material depth, two pass occlusion culling) open for Bevy https://github.com/bevyengine/bevy/pull/10164 that I've been working on, which uses WebGPU.

WebGPU is actually a pretty good API imo. It's missing some advanced features like raytracing, mesh shaders, and subgroup operations (coming soon!), but it can still do a lot.

The much bigger missing feature is "bindless" support (non-uniform arrays of bound resources). BindGroup overhead (and ergonomics) is a significant downside.