Backend switching for Mooncake

DifferentiationInterface/docs/src/explanation/backends.md

@@ -95,7 +95,7 @@ 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) or a [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl)-compatible backend.
+At the moment, `DifferentiateWith` only works when `true_backend` is either [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl), [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl) or a [Mooncake.jl](https://github.com/compintell/Mooncake.jl)-compatible backend.
 
 ## Implementations
 

DifferentiationInterface/docs/src/faq/differentiability.md

@@ -111,4 +111,4 @@ There are, however, translation utilities:
 ### Backend switch
 
 Also note the existence of [`DifferentiationInterface.DifferentiateWith`](@ref), which allows the user to wrap a function that should be differentiated with a specific backend.
-Right now it only targets ForwardDiff.jl and ChainRulesCore.jl, but PRs are welcome to define Enzyme.jl and Mooncake.jl rules for this object.
+Right now it only targets ForwardDiff.jl, ChainRulesCore.jl and Mooncake.jl but PRs are welcome to define Enzyme.jl rules for this object.

DifferentiationInterface/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl

@@ -3,14 +3,21 @@ module DifferentiationInterfaceMooncakeExt
 using ADTypes: ADTypes, AutoMooncake
 import DifferentiationInterface as DI
 using Mooncake:
+    Mooncake,
     CoDual,
     Config,
     prepare_gradient_cache,
     prepare_pullback_cache,
     tangent_type,
     value_and_gradient!!,
     value_and_pullback!!,
-    zero_tangent
+    zero_tangent,
+    @is_primitive,
+    zero_fcodual,
+    MinimalCtx,
+    NoRData,
+    fdata,
+    primal
 
 DI.check_available(::AutoMooncake) = true
 
@@ -26,5 +33,6 @@ mycopy(x) = deepcopy(x)
 
 include("onearg.jl")
 include("twoarg.jl")
+include("differentiate_with.jl")
 
 end

DifferentiationInterface/ext/DifferentiationInterfaceMooncakeExt/differentiate_with.jl

@@ -0,0 +1,48 @@
+@is_primitive MinimalCtx Tuple{DI.DifferentiateWith,<:Union{Number,AbstractArray}}
+
+function Mooncake.rrule!!(dw::CoDual{<:DI.DifferentiateWith}, x::CoDual{<:Number})
+    primal_func = primal(dw)
+    primal_x = primal(x)
+    (; f, backend) = primal_func
+    y = zero_fcodual(f(primal_x))
+
+    # output is a vector, so we need to use the vector pullback
+    function pullback!!(dy::NoRData)
+        tx = DI.pullback(f, backend, primal_x, (fdata(y.dx),))
+        return NoRData(), only(tx)
+    end
+
+    # output is a scalar, so we can use the scalar pullback
+    function pullback!!(dy::Number)
+        tx = DI.pullback(f, backend, primal_x, (dy,))
+        return NoRData(), only(tx)
+    end
+
+    return y, pullback!!
+end
+
+function Mooncake.rrule!!(dw::CoDual{<:DI.DifferentiateWith}, x::CoDual{<:AbstractArray})
+    primal_func = primal(dw)
+    primal_x = primal(x)
+    fdata_arg = fdata(x.dx)
+    (; f, backend) = primal_func
+    y = zero_fcodual(f(primal_x))
+    # in case x is mutated in f calls
+    cp_primal_x = copy(primal_x)
+
+    # output is a vector, so we need to use the vector pullback
+    function pullback!!(dy::NoRData)
+        tx = DI.pullback(f, backend, cp_primal_x, (fdata(y.dx),))
+        fdata_arg .+= only(tx)
+        return NoRData(), dy
+    end
+
+    # output is a scalar, so we can use the scalar pullback
+    function pullback!!(dy::Number)
+        tx = DI.pullback(f, backend, cp_primal_x, (dy,))
+        fdata_arg .+= only(tx)
+        return NoRData(), NoRData()
+    end
+
+    return y, pullback!!
+end

DifferentiationInterface/test/Back/DifferentiateWith/test.jl

@@ -1,11 +1,12 @@
 using Pkg
-Pkg.add(["FiniteDiff", "ForwardDiff", "Zygote"])
+Pkg.add(["FiniteDiff", "ForwardDiff", "Zygote", "Mooncake"])
 
 using DifferentiationInterface, DifferentiationInterfaceTest
 import DifferentiationInterfaceTest as DIT
 using FiniteDiff: FiniteDiff
 using ForwardDiff: ForwardDiff
 using Zygote: Zygote
+using Mooncake: Mooncake
 using Test
 
 LOGGING = get(ENV, "CI", "false") == "false"
@@ -24,7 +25,7 @@ function differentiatewith_scenarios()
 end
 
 test_differentiation(
-    [AutoForwardDiff(), AutoZygote()],
+    [AutoForwardDiff(), AutoZygote(), AutoMooncake(; config=nothing)],
     differentiatewith_scenarios();
     excluded=SECOND_ORDER,
     logging=LOGGING,