Zygote.jl VS Flux.jl

If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.

This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.

If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).

I think julia has a lot going for it. I feel like autograd is one of the bigger ones given that it's a language feature basically (https://github.com/FluxML/Zygote.jl for reference). I think the ecosystem is a bit of an uphill battle though.

Also check out https://github.com/FluxML/Zygote.jl which is the AD engine

> There's sadly no performant autodiff system for general purpose Python.

Like there is for general purpose Julia? (https://github.com/FluxML/Zygote.jl)

Thanks again. Like you said it is fun to dream (ask the "Scheme Machine" guys sometime about how they would go about it now), but practically with technology like Julia's Zygote:

https://github.com/FluxML/Zygote.jl

the efficiency of autodiff might be similar to that of an opcode anyway.

So, how did DEC do on the Alpha processor? I always heard good things about it--IIRC it was based on the VAX, but 64 bit. I learned PDP-11 assembler at RPI, during their college program for high school students in about 1984. We hand assembled code and really got to know the architecture.

and make that computable. It seems like line 88 --> 90 of this file in Zygote does that: https://github.com/FluxML/Zygote.jl/blob/master/src/compiler/chainrules.jl

It's super powerful

For example Zygote.jl (https://github.com/FluxML/Zygote.jl) implements reverse mode automatic differentiation, by defining a function that is a generated transformation of the function being differentiated.

If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.

Flux dominance!

I’m no compiler expert but check out flux and zygote https://fluxml.ai/ https://fluxml.ai/

I would suggest you to look into Julia ecosystem instead of C++. Julia is almost identical to Python in terms of how you use it but it's still very fast. You should look into flux.jl package for Julia.

Give Julia a try: https://fluxml.ai

2) Flux treats softmax a little different than most other activation functions (see here for more details) such as relu and sigmoid. When you pass an activation function into a layer like Dense(3, 32, relu), Flux expects that the function is broadcast over the layer's output. However, softmax cannot be broadcast as it operates over vectors rather than scalars. This means that if you want to use softmax as the final activation in your model, you need to pass it into Chain() like so:

Can you point to a concrete example of one that someone would run into when using the differential equation solvers with the default and recommended Enzyme AD for vector-Jacobian products? I'd be happy to look into it, but there do not currently seem to be any correctness issues in the Enzyme issue tracker that are current (3 issues are open but they all seem to be fixed, other than https://github.com/EnzymeAD/Enzyme.jl/issues/278 which is actually an activity analysis bug in LLVM). So please be more specific. The issue with Enzyme right now seems to moreso be about finding functional forms that compile, and it throws compile-time errors in the event that it cannot fully analyze the program and if it has too much dynamic behavior (example: https://github.com/EnzymeAD/Enzyme.jl/issues/368).

Additional note, we recently did a overhaul of SciMLSensitivity (https://sensitivity.sciml.ai/dev/) and setup a system which amounts to 15 hours of direct unit tests doing a combinatoric check of arguments with 4 hours of downstream testing (https://github.com/SciML/SciMLSensitivity.jl/actions/runs/25...). What that identified is that any remaining issues that can arise are due to the implicit parameters mechanism in Zygote (Zygote.params). To counteract this upstream issue, we (a) try to default to never default to Zygote VJPs whenever we can avoid it (hence defaulting to Enzyme and ReverseDiff first as previously mentioned), and (b) put in a mechanism for early error throwing if Zygote hits any not implemented derivative case with an explicit error message (https://github.com/SciML/SciMLSensitivity.jl/blob/v7.0.1/src...). We have alerted the devs of the machine learning libraries, and from this there has been a lot of movement. In particular, a globals-free machine learning library, Lux.jl, was created with fully explicit parameters https://lux.csail.mit.edu/dev/, and thus by design it cannot have this issue. In addition, the Flux.jl library itself is looking to do a redesign that eliminates implicit parameters (https://github.com/FluxML/Flux.jl/issues/1986). Which design will be the one in the end, that's uncertain right now, but it's clear that no matter what the future designs of the deep learning libraries will fully cut out that part of Zygote.jl. And additionally, the other AD libraries (Enzyme and Diffractor for example) do not have this "feature", so it's an issue that can only arise from a specific (not recommended) way of using Zygote (which now throws explicit error messages early and often if used anywhere near SciML because I don't tolerate it).

So from this, SciML should be rather safe and if not, please share some details and I'd be happy to dig in.

> In his item #1, he links to https://discourse.julialang.org/t/loaderror-when-using-inter... The issue is actually a Zygote bug, a Julia package for auto-differentiation, and is not directly related to Julia codebase (or Flux package) itself. Furthermore, the problematic code is working fine now, because DiffEqFlux has switched to Enzyme, which doesn't have that bug. He should first confirm whether the problem he is citing is actually a problem or not.

> Item #2, again another Zygote bug.

If flux chose a buggy package as a dependency, that's on them, and users are well justified in steering clear of Flux if it has buggy dependencies. As of today, the Project.toml for both Flux and DiffEqFlux still lists Zygote as a dependency. Neither list Enzyme.

https://github.com/FluxML/Flux.jl/blob/master/Project.toml