[llvm-dev] RFC: Enzyme, Automatic Differentiation for LLVM as an LLVM Incubator Project

[Vassil Vassilev via llvm-dev]

Hi,

   This would be very useful for the field of high energy physics and 
data science in general.

   Having enzyme as part of llvm would reduce the amount of custom code 
we have in our autodiff tool clad. I can see it being a common ground of 
a bunch of other tools across languages generating llvm.

   Thanks for working on making this upstream!

Best, Vassil

On 1/12/21 10:57 PM, William Moses wrote:
> Hi all,
> Automatic differentiation (AD) is a key component in algorithms used 
> in machine learning, scientific computing, and elsewhere.
> For the last year-and-a-half, the `Enzyme` group have been looking at 
> the practical possibility of doing automatic differentiation as part 
> of the LLVM optimization pipeline. Performing automatic 
> differentiation in LLVM is quite beneficial as it allows all of the 
> languages that lower to LLVM to incorporate automatic differentiation 
> without much additional work. It also allows for automatic 
> differentiation across languages, which is similarly beneficial.
> One unexpected benefit we found of doing AD at the LLVM-level is that 
> there is a significant performance benefit (4.2x in our tests) to be 
> gained by performing AD after LLVM’s optimization passes [1].
>
> After several months of testing with various users including the Rust 
> [4, 5], C/C++, Julia [6], Fortran, and machine learning communities, 
> we’d like to share LLVM-based automatic differentiation more widely 
> and ask to be considered as an LLVM incubator project.
>
>
> Our code is available here 
> (https://github.com/wsmoses/Enzyme/tree/master/enzyme 
> <https://github.com/wsmoses/Enzyme/tree/master/enzyme>) as a plugin 
> for LLVM versions 7 through master. We’ve had weekly meetings for the 
> past several months with folks from MIT, Argonne, Princeton, Google, 
> NVIDIA, and Facebook and welcome anyone who wants to join. 
> Documentation and install instructions for Enzyme is available here: 
> https://enzyme.mit.edu <https://enzyme.mit.edu>. We have our charter 
> available here:
> https://docs.google.com/document/d/10IK2EgZa-4WF0lOSlkND1_cX3IQLAxEVSOWqbQzNpcs/edit# 
> <https://docs.google.com/document/d/10IK2EgZa-4WF0lOSlkND1_cX3IQLAxEVSOWqbQzNpcs/edit#>
>
> Performing automatic differentiation inside of LLVM presents several 
> interesting technical questions, which we’ve explored with the 
> community in a poster and SRC talk at the 2020 US LLVM Dev Meeting [2, 3].
>
>
> The Enzyme team
>
>
> [1] 
> https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf 
> <https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf>
>
> [2] https://c.wsmoses.com/posters/Enzyme-llvmdev.pdf 
> <https://c.wsmoses.com/posters/Enzyme-llvmdev.pdf>
>
> [3] https://www.youtube.com/watch?v=auQNFDlaXdM 
> <https://www.youtube.com/watch?v=auQNFDlaXdM>, 
> https://c.wsmoses.com/presentations/enzyme-llvmdev-reduced.pdf 
> <https://c.wsmoses.com/presentations/enzyme-llvmdev-reduced.pdf>
>
> [4] https://github.com/tiberiusferreira/oxide-enzyme 
> <https://github.com/tiberiusferreira/oxide-enzyme>https://github.com/bytesnake/oxide-enzyme 
> <https://github.com/bytesnake/oxide-enzyme>,
>
> [5] 
> https://internals.rust-lang.org/t/automatic-differentiation-differential-programming-via-llvm/13188 
> <https://internals.rust-lang.org/t/automatic-differentiation-differential-programming-via-llvm/13188>
>
> [6] https://github.com/wsmoses/Enzyme.jl 
> <https://github.com/wsmoses/Enzyme.jl>
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