Enzyme.jl
py2many
Enzyme.jl | py2many | |
---|---|---|
10 | 29 | |
401 | 593 | |
2.7% | 1.5% | |
9.5 | 8.1 | |
about 2 hours ago | about 1 month ago | |
Julia | Python | |
MIT License | MIT License |
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.jl
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Custom gradients in Enzyme
It's possible but at this time it's not recommended or documented as right now it requires writing some LLVM-level stuff and a better system is coming soon (see https://github.com/EnzymeAD/Enzyme.jl/pull/177)
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“Why I still recommend Julia”
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.
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The Julia language has a number of correctness flaws
Lots of things are being rewritten. Remember we just released a new neural network library the other day, SimpleChains.jl, and showed that it gave about a 10x speed improvement on modern CPUs with multithreading enabled vs Jax Equinox (and 22x when AVX-512 is enabled) for smaller neural network and matrix-vector types of cases (https://julialang.org/blog/2022/04/simple-chains/). Then there's Lux.jl fixing some major issues of Flux.jl (https://github.com/avik-pal/Lux.jl). Pretty much everything is switching to Enzyme which improves performance quite a bit over Zygote and allows for full mutation support (https://github.com/EnzymeAD/Enzyme.jl). So an entire machine learning stack is already seeing parts release.
Right now we're in a bit of an uncomfortable spot where we have to use Zygote for a few things and then Enzyme for everything else, but the custom rules system is rather close and that's the piece that's needed to make the full transition.
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Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia
enzyme.jl is probably the quickest way to play with enzyme: https://github.com/wsmoses/Enzyme.jl
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Useful Algorithms That Are Not Optimized by Jax, PyTorch, or TensorFlow
"Maybe they let you declare some subgraph as 'dynamic' to avoid static optimizations?" What you just described is Tensorflow Eager and why it has some performance issues. XLA makes some pretty strong assumptions and I don't that should change. Tensorflow's ability to automatically generate good parallelized production code stems from the restrictions it has imposed. So I wouldn't even try for a "one true AD to rule them all" since making things more flexible will reduce the amount of compiler optimizations that can be automatically performed.
To get the more flexible form, you really would want to do it in a way that uses a full programming language's IR as its target. I think trying to use a fully dynamic programming language IR directly (Python, R, etc.) directly would be pretty insane because it would be hard to enforce rules and get performance. So some language that has a front end over an optimizing compiler (LLVM) would probably make the most sense. Zygote and Diffractor uses Julia's IR, but there are other ways to do this as well. Enzyme (https://github.com/wsmoses/Enzyme.jl) uses the LLVM IR directly for doing source-to-source translations. Using some dialect of LLVM (provided by MLIR) might be an interesting place to write a more ML-focused flexible AD system. Swift for Tensorflow used the Swift IR. This mindset starts to show why those tools were chosen.
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Julia Computing Raises $24M Series A
Have you explored the SciML landscape at all (?):
https://sciml.ai/
There are a number of components here which enable (what I would call) the expression of more advanced models using Julia's nice compositional properties.
Flux.jl is of course what most people would think of here (one of Julia's deep learning frameworks). But the reality behind Flux.jl is that it is just Julia code -- nothing too fancy.
There's ongoing work for AD in several directions -- including a Julia interface to Enzyme: https://github.com/wsmoses/Enzyme.jl
Also, a new AD system which Keno (who you'll see comment below or above) has been working on -- see Diffractor.jl on the JuliaCon schedule (for example).
Long story short -- there's quite a lot of work going on.
It may not seem like there is a "unified" package -- but that's because packages compose so well together in Julia, there's really no need for that.
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Swift for TensorFlow Shuts Down
The name of the LLVM AD tool is actually Enzyme [http://enzyme.mit.edu/] (Zygote is a Julia tool)
- Enzyme – High-performance automatic differentiation of LLVM (r/MachineLearning)
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Enzyme – High-performance automatic differentiation of LLVM
Also see the Julia package that makes it acessible with a high level interface and probably one of the easier ways to play with it: https://github.com/wsmoses/Enzyme.jl.
py2many
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Transpiler, a Meaningless Word
> Another problem is that there are hundreds of built-in library functions that need to be compiled from Python from C
An approach I've advocated as one of the main authors of py2many is that all of the python builtin functions be written in a subset of python[1] and then compiled into native code. This has the benefit of avoiding GIL, problems with C-API among other things.
Do checkout the examples here[2] which work out of the box for many of the 8-9 supported backends.
[1] https://github.com/py2many/py2many/blob/main/doc/langspec.md
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py2many VS kithon - a user suggested alternative
2 projects | 17 Jun 2023
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Why I'm still using Python
https://github.com/py2many/py2many/blob/main/doc/langspec.md
Reimplement a large enough, commonly used subset of python stdlib using this dialect and we may be in the business of writing cross platform apps (perhaps start with android and Ubuntu/Gnome)
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Codon: A high-performance Python compiler
For py2many, there is an informal specification here:
https://github.com/py2many/py2many/blob/main/doc/langspec.md
Would be great if all the authors of "python-like" languages get together and come up with a couple of specs.
I say a couple, because there are ones that support the python runtime (such as cython) and the ones which don't (like py2many).
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A Python-compatible statically typed language erg-lang/erg
It'd not fully solve your issue, but have you ever seen https://github.com/py2many/py2many ?
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Omyyyy/pycom: A Python compiler, down to native code, using C++
Cython doesn't consume python3 type hints and needs special type hints of its own. But it's certainly more mature than other players in the field.
What we need is a rpython suitable for app programming and a stdlib written in that dialect.
https://github.com/py2many/py2many/blob/main/doc/langspec.md
- I made a Python compiler, that can compile Python source down to fast, standalone executables.
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
No intermediate AST. To understand the various stages of transpilation and separation of language specific and independent rewriters, this file is a good starting point:
https://github.com/adsharma/py2many/blob/main/py2many/cli.py...
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Implicit Overflow Considered Harmful (and how to fix it)
Link to the test that's relevant for this discussion:
https://github.com/adsharma/py2many/blob/main/tests/cases/in...
This is an explicit deviation from python's bigint, which doesn't map very well to systemsey languages. The next logical step is to build on this to have dependent and refinement types.
Work in progress here:
https://github.com/adsharma/Typpete
What are some alternatives?
ChainRules.jl - forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
pybind11 - Seamless operability between C++11 and Python
ForwardDiff.jl - Forward Mode Automatic Differentiation for Julia
PyO3 - Rust bindings for the Python interpreter
MLJ.jl - A Julia machine learning framework
PythonNet - Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
swift - Swift for TensorFlow
PyCall.jl - Package to call Python functions from the Julia language
Lux.jl - Explicitly Parameterized Neural Networks in Julia
julia - The Julia Programming Language
NBodySimulator.jl - A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
rust-numpy - PyO3-based Rust bindings of the NumPy C-API