Enzyme VS Zygote.jl

Compare Enzyme vs Zygote.jl and see what are their differences.

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Enzyme Zygote.jl
16 9
1,133 1,431
2.9% 1.0%
9.6 8.1
7 days ago 4 days ago
LLVM Julia
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Enzyme

Posts with mentions or reviews of Enzyme. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-06.
  • Show HN: Port of OpenAI's Whisper model in C/C++
    9 projects | news.ycombinator.com | 6 Dec 2022
    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.

  • Do you consider making a physics engine (for RL) worth it?
    3 projects | /r/rust | 8 Oct 2022
    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.
  • What is a really cool thing you would want to write in Rust but don't have enough time, energy or bravery for?
    21 projects | /r/rust | 8 Jun 2022
    Have you taken a look at enzymeAD? There is a group porting it to rust.
  • The Julia language has a number of correctness flaws
    19 projects | news.ycombinator.com | 16 May 2022
    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.

  • Jax vs. Julia (Vs PyTorch)
    4 projects | news.ycombinator.com | 4 May 2022
    Idk, Enzyme is pretty next gen, all the way down to LLVM code.

    https://github.com/EnzymeAD/Enzyme

  • What's everyone working on this week (7/2022)?
    15 projects | /r/rust | 14 Feb 2022
    I'm working on merging my build-tool for (oxide)-enzyme into Enzyme itself. Also looking into improving the documentation.
  • Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
    7 projects | news.ycombinator.com | 25 Dec 2021
    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.

  • Enzyme: towards state-of-the-art AutoDiff in Rust
    2 projects | /r/rust | 11 Dec 2021
    Enzyme is an LLVM (incubator) project, which performs automatic differentiation of LLVM-IR code. Here is an introduction to AutoDiff, which was recommended by @DoogoMiercoles in an earlier post. You can also try it online, if you know some C/C++: https://enzyme.mit.edu/explorer.
  • Oxide-Enzyme: Integrating LLVM's Static Automatic Differentiation Plugin
    3 projects | /r/rust | 30 Nov 2021
    To give a little bit of context here, this is a Rust frontend for Enzyme itself, which is a leading Auto-Diff tool. The key advantage is that unlike most of the existing tools it does generate gradient functions after applying a lot of (LLVM's) optimizations, which leads to very efficient gradients (benchmarks here: https://enzyme.mit.edu/). Working on LLVM level also allows it to work across language barriers. Finally it is also the first AD library to support generic AMD-HIP / NVIDIA-CUDA code and works also with OpenMP and MPI. https://c.wsmoses.com/papers/EnzymeGPU.pdf I have intentions to add rayon support, since that is more likely to be used on our Rust side :)
  • Cerebras’ New Monster AI Chip Adds 1.4T Transistors
    4 projects | news.ycombinator.com | 22 Apr 2021
    The answer is an API, like NNAPI. AD is a frontend concern and doesn't really matter to accelerators.

    For AD, I am bullish for Enzyme, which does AD on LLVM IR, avoiding deep compiler integration: https://enzyme.mit.edu/

Zygote.jl

Posts with mentions or reviews of Zygote.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-22.
  • Yann Lecun: ML would have advanced if other lang had been adopted versus Python
    9 projects | news.ycombinator.com | 22 Feb 2023
    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.).

  • Neural networks with automatic differentiation.
    3 projects | /r/Julia | 13 Apr 2021
    Also check out https://github.com/FluxML/Zygote.jl which is the AD engine
  • PyTorch 1.8 release with AMD ROCm support
    8 projects | news.ycombinator.com | 4 Mar 2021
    > There's sadly no performant autodiff system for general purpose Python.

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

  • Ask HN: Show me your Half Baked project
    154 projects | news.ycombinator.com | 9 Jan 2021
    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.

What are some alternatives?

When comparing Enzyme and Zygote.jl you can also consider the following projects:

ForwardDiff.jl - Forward Mode Automatic Differentiation for Julia

Flux.jl - Relax! Flux is the ML library that doesn't make you tensor

Tullio.jl - â…€

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Lux.jl - Explicitly Parameterized Neural Networks in Julia

linfa - A Rust machine learning framework.

TensorFlow.jl - A Julia wrapper for TensorFlow

faust - Functional programming language for signal processing and sound synthesis

zygote - Explorations in area of programming languages: concepts, typing, formal verification

tensorflow - An Open Source Machine Learning Framework for Everyone

InvertibleNetworks.jl - A Julia framework for invertible neural networks