s4tf
DISCONTINUED
Enzyme
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s4tf | Enzyme | |
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1 | 16 | |
30 | 1,133 | |
- | 2.9% | |
4.9 | 9.6 | |
over 1 year ago | 6 days ago | |
Swift | LLVM | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
s4tf
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Future of swift on non Apple platforms (and non Arm architectures)
There has been active work recently on Swift for Tensorflow: https://github.com/s4tf/s4tf
Enzyme
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Show HN: Port of OpenAI's Whisper model in C/C++
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.
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Do you consider making a physics engine (for RL) worth it?
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.
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What is a really cool thing you would want to write in Rust but don't have enough time, energy or bravery for?
Have you taken a look at enzymeAD? There is a group porting it to rust.
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The Julia language has a number of correctness flaws
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.
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Jax vs. Julia (Vs PyTorch)
Idk, Enzyme is pretty next gen, all the way down to LLVM code.
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What's everyone working on this week (7/2022)?
I'm working on merging my build-tool for (oxide)-enzyme into Enzyme itself. Also looking into improving the documentation.
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Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
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.
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Enzyme: towards state-of-the-art AutoDiff in Rust
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.
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Oxide-Enzyme: Integrating LLVM's Static Automatic Differentiation Plugin
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 :)
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Cerebras’ New Monster AI Chip Adds 1.4T Transistors
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/
What are some alternatives?
Zygote.jl - 21st century AD
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
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.
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
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations