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

[Mehdi AMINI via llvm-dev]

Hi,

Since the project is alright going on for some time, what is the current
level of maturity?
You're proposing to get through as an incubator project, how far is it from
getting at the point where it would integrate the monorepo? How do you see
the roadmap on this?

Thanks,

-- 
Mehdi


On Tue, Jan 12, 2021 at 12:58 PM William Moses via llvm-dev <
llvm-dev at lists.llvm.org> 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) 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. 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
>
> [2] https://c.wsmoses.com/posters/Enzyme-llvmdev.pdf
>
> [3] https://www.youtube.com/watch?v=auQNFDlaXdM,
> https://c.wsmoses.com/presentations/enzyme-llvmdev-reduced.pdf
>
> [4] https://github.com/tiberiusferreira/oxide-enzyme
> https://github.com/bytesnake/oxide-enzyme,
>
> [5]
> https://internals.rust-lang.org/t/automatic-differentiation-differential-programming-via-llvm/13188
>
> [6] https://github.com/wsmoses/Enzyme.jl
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>
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