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https://github.com/EnzymeAD/oxide-enzyme

Enzyme integration into Rust. Experimental, do not use.
https://github.com/EnzymeAD/oxide-enzyme

ad auto-differentiation derivative enzyme gradient rust

Last synced: 4 months ago
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Enzyme integration into Rust. Experimental, do not use.

Lists

README 

        
#  ARCHIVED 



## Please look at https://github.com/EnzymeAD/rust



This is a package containing a Rust frontend 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.

  

# Supported types

- Scalars  

- Structs, Unions  

- Tuple, Array, Vec  

- Box, Reference, Raw pointer  



We are working on adding support for dyn trait objects, slices and enums.  

Adding Generics to your types or implementing traits is already working fine.



# Setup

First you have to get an adequate rustc/llvm/enzyme build here: [enzyme\_build](https://github.com/ZuseZ4/enzyme\_build).  

Afterwards for your convenience you should export this path for LLVM_SYS



```bash

$ export LLVM_SYS_130_PREFIX=$HOME/.cache/enzyme/rustc-1.59.0-src/build/x86_64-unknown-linux-gnu/llvm  

```



and tell Enzyme about your library locations:  

```bash

$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.cache/enzyme/Enzyme-0.0.29/enzyme/build/Enzyme:$HOME/.cache/enzyme/rustc-1.59.0-src/build/x86_64-unknown-linux-gnu/llvm/build/lib/  

```

  

As an alternative you can also run   

```bash

$ ninja install  

```

inside of your enzyme and llvm build directory.



Afterwards you can execute the following lines in `oxide-enzyme/example`, in order to compile the example.

```bash

$ cargo enzyme

```

You will find your executable in `./target/$TARGET/debug/`



# Compilation

We generate gradient functions based on LLVM-IR code. Therefore we currently need two compilation runs. The first to generate

a llvm-bc file with the LLVM-IR code, the second to process the bc file, generate the gradients, and build the entire crate.

You can do that manually using 

```bash

RUSTFLAGS="--emit=llvm-bc" cargo +enzyme -Z build-std rustc --target x86_64-unknown-linux-gnu -- --emit=llvm-bc -g -C opt-level=3 -Zno-link && RUSTFLAGS="--emit=llvm-bc" cargo +enzyme -Z build-std rustc --target x86_64-unknown-linux-gnu -- --emit=llvm-bc -g -C opt-level=3

```

We have created a wrapper for this command which you can call with:

```bash

cargo enzyme

```

Please be aware that our wrapper will ignore all additional commands.  

This approach won't work on dependencies since cargo doesn't support such a build process.



# FAQ  

- Q: How about Windows / Mac?

- A: WSL might work, the others probably not. Please let us know if you try.



  

# Further Information

More information on installing and using Enzyme directly (not through Rust) 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 paper to appear in NeurIPS 2020



```

@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}

}

```



License

=======



Dual-licensed to be compatible with the Rust project.



Licensed under the Apache License, Version 2.0

http://www.apache.org/licenses/LICENSE-2.0 or the MIT license

http://opensource.org/licenses/MIT, at your

option. This file may not be copied, modified, or distributed

except according to those terms.