Enzyme.jl

Julia bindings for the Enzyme automatic differentiator
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September 2019

The Enzyme High-Performance Automatic Differentiator of LLVM

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This is a package containing the Julia bindings for 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:

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.

More information on installing and using Enzyme directly (not through Julia) can be found on our website: https://enzyme.mit.edu.

To get involved or if you have questions, please join our mailing list.

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}
}