Use AbstractDifferentiation package

Project.toml

@@ -4,6 +4,7 @@ authors = ["Gabriel S. Gerlero <[email protected]>"]
 version = "2.5.2"
 
 [deps]
+AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -20,6 +21,7 @@ StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 ToeplitzMatrices = "c751599d-da0a-543b-9d20-d0a503d91d24"
 
 [compat]
+AbstractDifferentiation = "0.6.2"
 ArgCheck = "2"
 ForwardDiff = "0.10"
 LinearAlgebra = "1"

src/_Diff.jl

@@ -1,18 +1,26 @@
 module _Diff
 
-using ForwardDiff: derivative
-using ForwardDiff: Dual, Tag, value, extract_derivative
+import AbstractDifferentiation
+import ForwardDiff
+
+@inline function derivative(f, x::Real)
+    return only(AbstractDifferentiation.derivative(AbstractDifferentiation.ForwardDiffBackend(),
+        f,
+        x))
+end
 
 @inline function value_and_derivative(f, x::Real)
-    T = typeof(Tag(f, typeof(x)))
-    ydual = f(Dual{T}(x, oneunit(x)))
-    return value(T, ydual), extract_derivative(T, ydual)
+    a, b = AbstractDifferentiation.value_and_derivative(AbstractDifferentiation.ForwardDiffBackend(),
+        f,
+        x)
+    return a, only(b)
 end
 
 @inline function value_and_derivatives(f, x::Real)
-    T = typeof(Tag(f, typeof(x)))
-    ydual, ddual = value_and_derivative(f, Dual{T}(x, oneunit(x)))
-    return value(T, ydual), value(T, ddual), extract_derivative(T, ddual)
+    a, b, c = AbstractDifferentiation.value_derivative_and_second_derivative(AbstractDifferentiation.ForwardDiffBackend(),
+        f,
+        x)
+    return a, only(b), only(c)
 end
 
 export derivative, value_and_derivative, value_and_derivatives