Fix Enzyme extension and add new broken test (#151)

* Fix Enzyme extension and add new test

* Adapt to latest version

* No function annotation

* Test broken

* Fix tests

* Mode

* Const

* Bump version and move constructor doc

Project.toml

@@ -1,7 +1,7 @@
 name = "ImplicitDifferentiation"
 uuid = "57b37032-215b-411a-8a7c-41a003a55207"
 authors = ["Guillaume Dalle", "Mohamed Tarek and contributors"]
-version = "0.6.0"
+version = "0.6.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -21,9 +21,9 @@ ImplicitDifferentiationEnzymeExt = "Enzyme"
 ImplicitDifferentiationForwardDiffExt = "ForwardDiff"
 
 [compat]
-ADTypes = "1.0"
+ADTypes = "1.7.1"
 ChainRulesCore = "1.23.0"
-DifferentiationInterface = "0.5"
+DifferentiationInterface = "0.5.12"
 Enzyme = "0.11.20,0.12"
 ForwardDiff = "0.10.36"
 Krylov = "0.9.5"

examples/3_tricks.jl

@@ -5,6 +5,7 @@ We demonstrate several features that may come in handy for some users.
 =#
 
 using ComponentArrays
+using Enzyme  #src
 using ForwardDiff
 using ImplicitDifferentiation
 using Krylov
@@ -67,6 +68,8 @@ J = ForwardDiff.jacobian(forward_components, x)  #src
 Zygote.jacobian(implicit_components, x)[1]
 @test Zygote.jacobian(implicit_components, x)[1] ≈ J  #src
 
+@test_broken Enzyme.jacobian(Enzyme.Forward, implicit_components, x) ≈ J  #src
+
 #- The full differentiable pipeline looks like this
 
 function full_pipeline(a, b, m)

ext/ImplicitDifferentiationEnzymeExt.jl

@@ -5,6 +5,8 @@ using Enzyme
 using Enzyme.EnzymeCore
 using ImplicitDifferentiation: ImplicitFunction, build_A, build_B, byproduct, output
 
+const FORWARD_BACKEND = AutoEnzyme(; mode=Enzyme.Forward, function_annotation=Enzyme.Const)
+
 function EnzymeRules.forward(
     func::Const{<:ImplicitFunction},
     RT::Type{<:Union{BatchDuplicated,BatchDuplicatedNoNeed}},
@@ -20,12 +22,11 @@ function EnzymeRules.forward(
     y = output(y_or_yz)
     Y = typeof(y)
 
-    suggested_backend = AutoEnzyme(Enzyme.Forward)
+    suggested_backend = FORWARD_BACKEND
     A = build_A(implicit, x, y_or_yz, args...; suggested_backend)
     B = build_B(implicit, x, y_or_yz, args...; suggested_backend)
 
-    dx_batch = reduce(hcat, dx)
-    dc_batch = mapreduce(hcat, eachcol(dx_batch)) do dₖx
+    dc_batch = mapreduce(hcat, dx) do dₖx
         B * dₖx
     end
     dy_batch = implicit.linear_solver(A, -dc_batch)

src/implicit_function.jl

@@ -60,6 +60,26 @@ The value of `lazy` must be chosen together with the `linear_solver`, see below.
 - `conditions_x_backend`: how the conditions will be differentiated w.r.t. the first argument `x` 
 - `conditions_y_backend`: how the conditions will be differentiated w.r.t. the second argument `y` 
 
+# Constructors
+
+    ImplicitFunction(
+        forward, conditions;
+        linear_solver=KrylovLinearSolver(),
+        conditions_x_backend=nothing,
+        conditions_x_backend=nothing,
+    )
+
+Picks the `lazy` parameter automatically based on the `linear_solver`, using the following heuristic: `lazy = linear_solver != \\`.
+
+    ImplicitFunction{lazy}(
+        forward, conditions;
+        linear_solver=lazy ? KrylovLinearSolver() : \\,
+        conditions_x_backend=nothing,
+        conditions_y_backend=nothing,
+    )
+
+Picks the `linear_solver` automatically based on the `lazy` parameter.
+
 # Function signatures
 
 There are two possible signatures for `forward` and `conditions`, which must be consistent with one another:
@@ -87,8 +107,10 @@ Typically, direct solvers work best with dense Jacobians (`lazy = false`) while
 # Condition backends
 
 The provided `conditions_x_backend` and `conditions_y_backend` can be either:
+- `nothing` (the default), in which case the outer backend (the one differentiating through the `ImplicitFunction`) is used.
 - an object subtyping `AbstractADType` from [ADTypes.jl](https://github.com/SciML/ADTypes.jl);
-- `nothing`, in which case the outer backend (the one differentiating through the `ImplicitFunction`) is used.
+
+When differentiating with Enzyme as an outer backend, the default setting assumes that `conditions` does not contain writeable data involved in derivatives.
 """
 struct ImplicitFunction{
     lazy,F,C,L,B1<:Union{Nothing,AbstractADType},B2<:Union{Nothing,AbstractADType}
@@ -101,14 +123,7 @@ struct ImplicitFunction{
 end
 
 """
-    ImplicitFunction{lazy}(
-        forward, conditions;
-        linear_solver=lazy ? KrylovLinearSolver() : \\,
-        conditions_x_backend=nothing,
-        conditions_y_backend=nothing,
-    )
 
-Constructor for an [`ImplicitFunction`](@ref) which picks the `linear_solver` automatically based on the `lazy` parameter.
 """
 function ImplicitFunction{lazy}(
     forward::F,
@@ -126,16 +141,6 @@ function ImplicitFunction{lazy}(
     )
 end
 
-"""
-    ImplicitFunction(
-        forward, conditions;
-        linear_solver=KrylovLinearSolver(),
-        conditions_x_backend=nothing,
-        conditions_x_backend=nothing,
-    )
-
-Constructor for an [`ImplicitFunction`](@ref) which picks the `lazy` parameter automatically based on the `linear_solver`, using the following heuristic: `lazy = linear_solver != \\`.
-"""
 function ImplicitFunction(
     forward,
     conditions;

test/systematic.jl

@@ -12,7 +12,7 @@ include("utils.jl")
 
 backends = [
     AutoForwardDiff(; chunksize=1), #
-    AutoEnzyme(Enzyme.Forward),
+    AutoEnzyme(; mode=Enzyme.Forward, function_annotation=Enzyme.Const),
     AutoZygote(),
 ]
 
@@ -24,7 +24,7 @@ linear_solver_candidates = (
 conditions_backend_candidates = (
     nothing, #
     AutoForwardDiff(; chunksize=1),
-    AutoEnzyme(Enzyme.Forward),
+    AutoEnzyme(; mode=Enzyme.Forward, function_annotation=Enzyme.Const),
 );
 
 x_candidates = (