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Jun 2, 2025
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docs: make Enzyme warning more visible #808

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8 changes: 3 additions & 5 deletions DifferentiationInterface/README.md
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Expand Up @@ -32,7 +32,7 @@ We support the following backends defined by [ADTypes.jl](https://github.com/Sci

- [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl)
- [Diffractor.jl](https://github.com/JuliaDiff/Diffractor.jl) (currently broken)
- [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl)
- [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) (see below)
- [FastDifferentiation.jl](https://github.com/brianguenter/FastDifferentiation.jl)
- [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl)
- [FiniteDifferences.jl](https://github.com/JuliaDiff/FiniteDifferences.jl)
Expand All @@ -45,10 +45,8 @@ We support the following backends defined by [ADTypes.jl](https://github.com/Sci
- [Tracker.jl](https://github.com/FluxML/Tracker.jl)
- [Zygote.jl](https://github.com/FluxML/Zygote.jl)

Note that in some cases, going through DifferentiationInterface.jl might be slower than a direct call to the backend's API.
This is mostly true for Enzyme.jl, whose handling of activities and multiple arguments unlocks additional performance.
We are working on this challenge, and welcome any suggestions or contributions.
Meanwhile, if differentiation fails or takes too long, consider using Enzyme.jl directly.
> [!CAUTION]
> Note that in some cases, going through DifferentiationInterface.jl might be slower or cause more errors than a direct call to the backend's API. This is especially true for Enzyme.jl, whose handling of activities and multiple arguments is not fully supported here. We are working on this challenge, and welcome any suggestions or contributions. Meanwhile, if differentiation fails or takes too long, consider using Enzyme.jl through its [native API](https://enzymead.github.io/Enzyme.jl/stable/) instead.

## Installation

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4 changes: 3 additions & 1 deletion DifferentiationInterface/docs/make.jl
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Expand Up @@ -16,7 +16,9 @@ links = InterLinks(
"Symbolics" => "https://symbolics.juliasymbolics.org/stable/",
)

cp(joinpath(@__DIR__, "..", "README.md"), joinpath(@__DIR__, "src", "index.md"); force=true)
readme_str = read(joinpath(@__DIR__, "..", "README.md"), String)
readme_str = replace(readme_str, "> [!CAUTION]\n> " => "!!! warning\n ")
write(joinpath(@__DIR__, "src", "index.md"), readme_str)

makedocs(;
modules=[DifferentiationInterface],
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4 changes: 4 additions & 0 deletions DifferentiationInterface/docs/src/explanation/backends.md
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Expand Up @@ -120,6 +120,10 @@ We only implement `pushforward`.
Depending on the `mode` attribute inside [`AutoEnzyme`](@extref ADTypes.AutoEnzyme), we implement either `pushforward` or `pullback` based on `Enzyme.autodiff`.
When necessary, preparation chooses a number of chunks (for `gradient` and `jacobian` in forward mode, for `jacobian` only in reverse mode).

!!! warning
Enzyme.jl's handling of activities and multiple arguments is not fully supported here, which can cause slowdowns or errors.
If differentiation fails or takes too long, consider using Enzyme.jl through its [native API](https://enzymead.github.io/Enzyme.jl/stable/) instead.

### FastDifferentiation

For every operator, preparation generates an [executable function](https://brianguenter.github.io/FastDifferentiation.jl/stable/makefunction/) from the symbolic expression of the differentiated function.
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