Docs

Author: gdalle

DifferentiationInterface/docs/src/explanation/backends.md

@@ -12,7 +12,7 @@ We support the following dense backend choices from [ADTypes.jl](https://github.
 - [`AutoFiniteDifferences`](@extref ADTypes.AutoFiniteDifferences)
 - [`AutoForwardDiff`](@extref ADTypes.AutoForwardDiff)
 - [`AutoGTPSA`](@extref ADTypes.AutoGTPSA)
-- [`AutoMooncake`](@extref ADTypes.AutoMooncake) and [`AutoMooncakeForward`](@extref ADTypes.AutoMooncake)
+- [`AutoMooncake`](@extref ADTypes.AutoMooncake) and [`AutoMooncakeForward`](@extref ADTypes.AutoMooncake) (the latter is experimental)
 - [`AutoPolyesterForwardDiff`](@extref ADTypes.AutoPolyesterForwardDiff)
 - [`AutoReverseDiff`](@extref ADTypes.AutoReverseDiff)
 - [`AutoSymbolics`](@extref ADTypes.AutoSymbolics)
@@ -97,7 +97,7 @@ In general, using a forward outer backend over a reverse inner backend will yiel
 The wrapper [`DifferentiateWith`](@ref) allows you to switch between backends.
 It takes a function `f` and specifies that `f` should be differentiated with the substitute backend of your choice, instead of whatever true backend the surrounding code is trying to use.
 In other words, when someone tries to differentiate `dw = DifferentiateWith(f, substitute_backend)` with `true_backend`, then `substitute_backend` steps in and `true_backend` does not dive into the function `f` itself.
-At the moment, `DifferentiateWith` only works when `true_backend` is either [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl), [Mooncake.jl](https://github.com/chalk-lab/Mooncake.jl), or a [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl)-compatible backend (e.g., [Zygote.jl](https://github.com/FluxML/Zygote.jl)).
+At the moment, `DifferentiateWith` only works when `true_backend` is either [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl), reverse-mode [Mooncake.jl](https://github.com/chalk-lab/Mooncake.jl), or a [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl)-compatible backend (e.g., [Zygote.jl](https://github.com/FluxML/Zygote.jl)).
 
 ## Implementations
 

DifferentiationInterface/src/misc/differentiate_with.jl

@@ -13,7 +13,7 @@ Moreover, any larger algorithm `alg` that calls `f2` instead of `f` will also be
 
 !!! warning
     `DifferentiateWith` only supports out-of-place functions `y = f(x)` without additional context arguments.
-    It only makes these functions differentiable if the true backend is either [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl), [Mooncake](https://github.com/chalk-lab/Mooncake.jl) or automatically importing rules from [ChainRules](https://github.com/JuliaDiff/ChainRules.jl) (e.g. [Zygote](https://github.com/FluxML/Zygote.jl)). Some backends are also able to [manually import rules](https://juliadiff.org/ChainRulesCore.jl/stable/#Packages-supporting-importing-rules-from-ChainRules.) from ChainRules.
+    It only makes these functions differentiable if the true backend is either [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl), reverse-mode [Mooncake](https://github.com/chalk-lab/Mooncake.jl), or if it automatically importing rules from [ChainRules](https://github.com/JuliaDiff/ChainRules.jl) (e.g. [Zygote](https://github.com/FluxML/Zygote.jl)). Some backends are also able to [manually import rules](https://juliadiff.org/ChainRulesCore.jl/stable/#Packages-supporting-importing-rules-from-ChainRules.) from ChainRules.
     For any other true backend, the differentiation behavior is not altered by `DifferentiateWith` (it becomes a transparent wrapper).
 
 !!! warning