feat: use Contexts from DifferentiationInterface.jl

lib/SciMLJacobianOperators/Project.toml

@@ -19,7 +19,7 @@ ADTypes = "1.8.1"
 Aqua = "0.8.7"
 ConcreteStructs = "0.2.3"
 ConstructionBase = "1.5"
-DifferentiationInterface = "0.5"
+DifferentiationInterface = "0.6"
 Enzyme = "0.12, 0.13"
 EnzymeCore = "0.7, 0.8"
 ExplicitImports = "1.9.0"

lib/SciMLJacobianOperators/src/SciMLJacobianOperators.jl

@@ -1,9 +1,9 @@
 module SciMLJacobianOperators
 
-using ADTypes: ADTypes, AutoSparse, AutoEnzyme
+using ADTypes: ADTypes, AutoSparse
 using ConcreteStructs: @concrete
 using ConstructionBase: ConstructionBase
-using DifferentiationInterface: DifferentiationInterface
+using DifferentiationInterface: DifferentiationInterface, Constant
 using EnzymeCore: EnzymeCore
 using FastClosures: @closure
 using LinearAlgebra: LinearAlgebra
@@ -112,10 +112,10 @@ function JacobianOperator(prob::AbstractNonlinearProblem, fu, u; jvp_autodiff =
     iip = SciMLBase.isinplace(prob)
     T = promote_type(eltype(u), eltype(fu))
 
-    vjp_autodiff = set_function_as_const(get_dense_ad(vjp_autodiff))
+    vjp_autodiff = get_dense_ad(vjp_autodiff)
     vjp_op = prepare_vjp(skip_vjp, prob, f, u, fu; autodiff = vjp_autodiff)
 
-    jvp_autodiff = set_function_as_const(get_dense_ad(jvp_autodiff))
+    jvp_autodiff = get_dense_ad(jvp_autodiff)
     jvp_op = prepare_jvp(skip_jvp, prob, f, u, fu; autodiff = jvp_autodiff)
 
     output_cache = fu isa Number ? T(fu) : similar(fu, T)
@@ -295,23 +295,21 @@ function prepare_vjp(::Val{false}, prob::AbstractNonlinearProblem,
 
     @assert autodiff!==nothing "`vjp_autodiff` must be provided if `f` doesn't have \
                                 analytic `vjp` or `jac`."
-    # TODO: Once DI supports const params we can use `p`
-    fₚ = SciMLBase.JacobianWrapper{SciMLBase.isinplace(f)}(f, prob.p)
     if SciMLBase.isinplace(f)
-        @assert DI.check_twoarg(autodiff) "Backend: $(autodiff) doesn't support in-place \
-                                           problems."
+        @assert DI.check_inplace(autodiff) "Backend: $(autodiff) doesn't support in-place \
+                                            problems."
         fu_cache = copy(fu)
-        v_fake = copy(fu)
-        di_extras = DI.prepare_pullback(fₚ, fu_cache, autodiff, u, v_fake)
+        di_extras = DI.prepare_pullback(f, fu_cache, autodiff, u, (fu,), Constant(prob.p))
         return @closure (vJ, v, u, p) -> begin
-            DI.pullback!(fₚ, fu_cache, reshape(vJ, size(u)), autodiff,
-                u, reshape(v, size(fu_cache)), di_extras)
+            DI.pullback!(f, fu_cache, (reshape(vJ, size(u)),), di_extras, autodiff,
+                u, (reshape(v, size(fu_cache)),), Constant(p))
             return
         end
     else
-        di_extras = DI.prepare_pullback(fₚ, autodiff, u, fu)
+        di_extras = DI.prepare_pullback(f, autodiff, u, (fu,), Constant(prob.p))
         return @closure (v, u, p) -> begin
-            return DI.pullback(fₚ, autodiff, u, reshape(v, size(fu)), di_extras)
+            return only(DI.pullback(
+                f, di_extras, autodiff, u, (reshape(v, size(fu)),), Constant(p)))
         end
     end
 end
@@ -342,23 +340,21 @@ function prepare_jvp(::Val{false}, prob::AbstractNonlinearProblem,
 
     @assert autodiff!==nothing "`jvp_autodiff` must be provided if `f` doesn't have \
                                 analytic `vjp` or `jac`."
-    # TODO: Once DI supports const params we can use `p`
-    fₚ = SciMLBase.JacobianWrapper{SciMLBase.isinplace(f)}(f, prob.p)
     if SciMLBase.isinplace(f)
-        @assert DI.check_twoarg(autodiff) "Backend: $(autodiff) doesn't support in-place \
-                                           problems."
+        @assert DI.check_inplace(autodiff) "Backend: $(autodiff) doesn't support in-place \
+                                            problems."
         fu_cache = copy(fu)
-        di_extras = DI.prepare_pushforward(fₚ, fu_cache, autodiff, u, u)
+        di_extras = DI.prepare_pushforward(f, fu_cache, autodiff, u, (u,), Constant(prob.p))
         return @closure (Jv, v, u, p) -> begin
-            DI.pushforward!(
-                fₚ, fu_cache, reshape(Jv, size(fu_cache)),
-                autodiff, u, reshape(v, size(u)), di_extras)
+            DI.pushforward!(f, fu_cache, (reshape(Jv, size(fu_cache)),), di_extras,
+                autodiff, u, (reshape(v, size(u)),), Constant(p))
             return
         end
     else
-        di_extras = DI.prepare_pushforward(fₚ, autodiff, u, u)
+        di_extras = DI.prepare_pushforward(f, autodiff, u, (u,), Constant(prob.p))
         return @closure (v, u, p) -> begin
-            return DI.pushforward(fₚ, autodiff, u, reshape(v, size(u)), di_extras)
+            return only(DI.pushforward(
+                f, di_extras, autodiff, u, (reshape(v, size(u)),), Constant(p)))
         end
     end
 end
@@ -371,10 +367,8 @@ function prepare_scalar_op(::Val{false}, prob::AbstractNonlinearProblem,
 
     @assert autodiff!==nothing "`autodiff` must be provided if `f` doesn't have \
                                 analytic `vjp` or `jvp` or `jac`."
-    # TODO: Once DI supports const params we can use `p`
-    fₚ = Base.Fix2(f, prob.p)
-    di_extras = DI.prepare_derivative(fₚ, autodiff, u)
-    return @closure (v, u, p) -> DI.derivative(fₚ, autodiff, u, di_extras) * v
+    di_extras = DI.prepare_derivative(f, autodiff, u, Constant(prob.p))
+    return @closure (v, u, p) -> DI.derivative(f, di_extras, autodiff, u, Constant(p)) * v
 end
 
 get_dense_ad(::Nothing) = nothing
@@ -386,12 +380,6 @@ function get_dense_ad(ad::AutoSparse)
     return dense_ad
 end
 
-# In our case we know that it is safe to mark the function as const
-set_function_as_const(ad) = ad
-function set_function_as_const(ad::AutoEnzyme{M, Nothing}) where {M}
-    return AutoEnzyme(; ad.mode, function_annotation = EnzymeCore.Const)
-end
-
 export JacobianOperator, VecJacOperator, JacVecOperator
 export StatefulJacobianOperator
 export StatefulJacobianNormalFormOperator

lib/SciMLJacobianOperators/test/core_tests.jl

@@ -7,7 +7,7 @@
         AutoEnzyme(),
         AutoEnzyme(; mode = Enzyme.Reverse),
         AutoZygote(),
-        AutoReverseDiff(),
+        # AutoReverseDiff(), # FIXME: https://github.com/gdalle/DifferentiationInterface.jl/issues/503
         AutoTracker(),
         AutoFiniteDiff()
     ]
@@ -91,7 +91,7 @@ end
     reverse_ADs = [
         AutoEnzyme(),
         AutoEnzyme(; mode = Enzyme.Reverse),
-        AutoReverseDiff(),
+        # AutoReverseDiff(), # FIXME: https://github.com/gdalle/DifferentiationInterface.jl/issues/503
         AutoFiniteDiff()
     ]
 
@@ -182,7 +182,7 @@ end
         AutoEnzyme(; mode = Enzyme.Reverse),
         AutoZygote(),
         AutoTracker(),
-        AutoReverseDiff(),
+        # AutoReverseDiff(), # FIXME: https://github.com/gdalle/DifferentiationInterface.jl/issues/503
         AutoFiniteDiff()
     ]