Use an older version of DI.jl

Project.toml

@@ -16,7 +16,7 @@ SparseMatrixColorings = "0a514795-09f3-496d-8182-132a7b665d35"
 
 [compat]
 ADTypes = "1.2.1"
-DifferentiationInterface = "0.6.0"
+DifferentiationInterface = "0.5.17"
 ForwardDiff = "0.9.0, 0.10.0"
 NLPModels = "0.18, 0.19, 0.20, 0.21"
 Requires = "1"

src/di.jl

@@ -21,12 +21,14 @@ for (ADGradient, fbackend) in ((:EnzymeADGradient, :AutoEnzyme),
     end
 
     function gradient(b::$ADGradient, f, x)
-      g = DifferentiationInterface.gradient(f, b.extras, b.backend, x)
+      g = DifferentiationInterface.gradient(f, b.backend, x, b.extras)
+      # g = DifferentiationInterface.gradient(f, b.extras, b.backend, x)
       return g
     end
 
     function gradient!(b::$ADGradient, g, f, x)
-      DifferentiationInterface.gradient!(f, g, b.extras, b.backend, x)
+      DifferentiationInterface.gradient!(f, g, b.backend, x, b.extras)
+      # DifferentiationInterface.gradient!(f, g, b.extras, b.backend, x)
       return g
     end
 
@@ -57,7 +59,8 @@ for (ADJprod, fbackend) in ((:EnzymeADJprod, :AutoEnzyme),
     end
 
     function Jprod!(b::$ADJprod, Jv, f, x, v, ::Val)
-      DifferentiationInterface.pushforward!(f, Jv, b.extras, b.backend, x, v)
+      DifferentiationInterface.pushforward!(f, Jv, b.backend, x, v, b.extras)
+      # DifferentiationInterface.pushforward!(f, Jv, b.extras, b.backend, x, v)
       return Jv
     end
 
@@ -88,7 +91,8 @@ for (ADJtprod, fbackend) in ((:EnzymeADJtprod, :AutoEnzyme),
     end
 
     function Jtprod!(b::$ADJtprod, Jtv, f, x, v, ::Val)
-      DifferentiationInterface.pullback!(f, Jtv, b.extras, b.backend, x, v)
+      DifferentiationInterface.pullback!(f, Jtv, b.backend, x, v, b.extras)
+      # DifferentiationInterface.pullback!(f, Jtv, b.extras, b.backend, x, v)
       return Jtv
     end
 
@@ -119,7 +123,9 @@ for (ADJacobian, fbackend) in ((:EnzymeADJacobian, :AutoEnzyme),
     end
 
     function jacobian(b::$ADJacobian, f, x)
-      return DifferentiationInterface.jacobian(f, b.extras, b.backend, x)
+      J = DifferentiationInterface.jacobian(f, b.backend, x, b.extras)
+      # J = DifferentiationInterface.jacobian(f, b.extras, b.backend, x)
+      return J
     end
 
   end
@@ -150,14 +156,15 @@ end
 #     end
 #
 #     function Hessian(b::$ADHessian, f, x)
-#       return DifferentiationInterface.hessian(f, b.extras, b.backend, x)
+#       H = DifferentiationInterface.hessian(f, b.extras, b.backend, x)
+#       return H
 #     end
 #
 #   end
 # end
 
 # for (ADHvprod, fbackend) in ((:EnzymeADHvprod, :AutoEnzyme),
-#                             (:ZygoteADHvprod, :AutoZygote))
+#                              (:ZygoteADHvprod, :AutoZygote))
 #   @eval begin
 #
 #     struct $ADHvprod{B, E} <: ADBackend