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https://github.com/EnzymeAD/Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
https://github.com/EnzymeAD/Enzyme.jl
ad automatic-differentiation compiler differentiable-programming enzyme julia llvm machine-learning
Last synced: about 1 month ago
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Julia bindings for the Enzyme automatic differentiator
- Host: GitHub
- URL: https://github.com/EnzymeAD/Enzyme.jl
- Owner: EnzymeAD
- License: mit
- Created: 2019-09-06T20:06:06.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-06T16:00:53.000Z (7 months ago)
- Last Synced: 2024-04-06T20:24:12.079Z (7 months ago)
- Topics: ad, automatic-differentiation, compiler, differentiable-programming, enzyme, julia, llvm, machine-learning
- Language: Julia
- Homepage: https://enzyme.mit.edu
- Size: 3.1 MB
- Stars: 394
- Watchers: 14
- Forks: 55
- Open Issues: 169
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
README
# 
The Enzyme High-Performance Automatic Differentiator of LLVM
[](https://enzyme.mit.edu/julia/stable)
[](https://enzyme.mit.edu/julia/dev)
[](https://github.com/wsmoses/Enzyme.jl/actions)
[](https://codecov.io/gh/wsmoses/Enzyme.jl)
This is a package containing the Julia bindings for [Enzyme](https://github.com/wsmoses/enzyme). This is very much a work in progress and bug reports/discussion is greatly appreciated!
Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM. It is highly-efficient and its ability perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools.
Enzyme.jl can be installed in the usual way Julia packages are installed
```
] add Enzyme
```
Enzyme.jl can be used by calling `autodiff` on a function to be differentiated as shown below:
```julia
using Enzyme, Test
f1(x) = x*x
# Returns a tuple of active returns, which in this case is simply (2.0,)
@test first(autodiff(Reverse, f1, Active(1.0))[1]) ≈ 2.0
```
For details, see the [package documentation](https://enzyme.mit.edu/julia).
More information on installing and using Enzyme directly (not through Julia) can be found on our website: [https://enzyme.mit.edu](https://enzyme.mit.edu).
To get involved or if you have questions, please join our [mailing list](https://groups.google.com/d/forum/enzyme-dev).
If using this code in an academic setting, please cite the following two papers (first for Enzyme as a whole, then for GPU+optimizations):
```
@inproceedings{NEURIPS2020_9332c513,
author = {Moses, William and Churavy, Valentin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {12472--12485},
publisher = {Curran Associates, Inc.},
title = {Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients},
url = {https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf},
volume = {33},
year = {2020}
}
@inproceedings{10.1145/3458817.3476165,
author = {Moses, William S. and Churavy, Valentin and Paehler, Ludger and H\"{u}ckelheim, Jan and Narayanan, Sri Hari Krishna and Schanen, Michel and Doerfert, Johannes},
title = {Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme},
year = {2021},
isbn = {9781450384421},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3458817.3476165},
doi = {10.1145/3458817.3476165},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
articleno = {61},
numpages = {16},
keywords = {CUDA, LLVM, ROCm, HPC, AD, GPU, automatic differentiation},
location = {St. Louis, Missouri},
series = {SC '21}
}
```