# The Enzyme High-Performance Automatic Differentiator of LLVM and MLIR Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM and MLIR. Enzyme can be used by calling `__enzyme_autodiff` on a function to be differentiated as shown below. Running the Enzyme transformation pass then replaces the call to `__enzyme_autodiff` with the gradient of its first argument. ```c double foo(double); double grad_foo(double x) { return __enzyme_autodiff(foo, x); } ``` Enzyme is highly-efficient and its ability to perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools.

Detailed information on installing and using Enzyme can be found on our website: [https://enzyme.mit.edu](https://enzyme.mit.edu). A short example of how to install Enzyme is below: ``` cd /path/to/Enzyme/enzyme mkdir build && cd build cmake -G Ninja .. -DLLVM_DIR=/path/to/llvm/lib/cmake/llvm -DLLVM_EXTERNAL_LIT=/path/to/lit/lit.py ninja ``` Or, install Enzyme using a package manager: [Homebrew](https://brew.sh) ``` brew install enzyme ``` [Spack](https://spack.io) ``` spack install enzyme ``` [Nix](https://nixos.org/) ``` nix-shell -p enzyme ``` 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 three papers (first for Enzyme as a whole, second for GPU+optimizations, and third for AD of all other parallel programs (OpenMP, MPI, Julia Tasks, etc.)): ``` @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} } @inproceedings{10.5555/3571885.3571964, author = {Moses, William S. and Narayanan, Sri Hari Krishna and Paehler, Ludger and Churavy, Valentin and Schanen, Michel and H\"{u}ckelheim, Jan and Doerfert, Johannes and Hovland, Paul}, title = {Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation}, year = {2022}, isbn = {9784665454445}, publisher = {IEEE Press}, booktitle = {Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis}, articleno = {60}, numpages = {18}, keywords = {automatic differentiation, tasks, OpenMP, compiler, Julia, parallel, Enzyme, C++, RAJA, hybrid parallelization, MPI, distributed, LLVM}, location = {Dallas, Texas}, series = {SC '22} } ``` Both [Julia bindings](https://github.com/EnzymeAD/Enzyme.jl#readme) and [Rust bindings](https://github.com/EnzymeAD/rust#readme) are available for Enzyme.