Fix deprecation warning introduced by DiffRules v1.4

Project.toml

@@ -1,12 +1,13 @@
 name = "ForwardDiff"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.22"
+version = "0.10.23"
 
 [deps]
 CommonSubexpressions = "bbf7d656-a473-5ed7-a52c-81e309532950"
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 DiffRules = "b552c78f-8df3-52c6-915a-8e097449b14b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -18,8 +19,9 @@ StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Calculus = "0.2, 0.3, 0.4, 0.5"
 CommonSubexpressions = "0.3"
 DiffResults = "0.0.1, 0.0.2, 0.0.3, 0.0.4, 1.0.1"
-DiffRules = "1.2.1"
+DiffRules = "1.4.0"
 DiffTests = "0.0.1, 0.1"
+LogExpFunctions = "0.3"
 NaNMath = "0.2.2, 0.3"
 Preferences = "1"
 SpecialFunctions = "0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.10, 1.0"

src/ForwardDiff.jl

@@ -12,6 +12,7 @@ using LinearAlgebra
 import Printf
 import NaNMath
 import SpecialFunctions
+import LogExpFunctions
 import CommonSubexpressions
 
 include("prelude.jl")

src/dual.jl

@@ -392,8 +392,12 @@ Base.float(d::Dual) = convert(float(typeof(d)), d)
 # General Mathematical Operations #
 ###################################
 
-for (M, f, arity) in DiffRules.diffrules()
-    in((M, f), ((:Base, :^), (:NaNMath, :pow), (:Base, :/), (:Base, :+), (:Base, :-))) && continue
+for (M, f, arity) in DiffRules.diffrules(filter_modules = nothing)
+    if (M, f) in ((:Base, :^), (:NaNMath, :pow), (:Base, :/), (:Base, :+), (:Base, :-))
+        continue  # Skip methods which we define elsewhere.
+    elseif !(isdefined(@__MODULE__, M) && isdefined(getfield(@__MODULE__, M), f))
+        continue  # Skip rules for methods not defined in the current scope
+    end
     if arity == 1
         eval(unary_dual_definition(M, f))
     elseif arity == 2

test/DualTest.jl

@@ -6,7 +6,7 @@ using Random
 using ForwardDiff
 using ForwardDiff: Partials, Dual, value, partials
 
-using NaNMath, SpecialFunctions
+using NaNMath, SpecialFunctions, LogExpFunctions
 using DiffRules
 
 import Calculus
@@ -420,12 +420,22 @@ for N in (0,3), M in (0,4), V in (Int, Float32)
     @test abs(NESTED_FDNUM) === NESTED_FDNUM
 
     if V != Int
-        for (M, f, arity) in DiffRules.diffrules()
-            in(f, (:hankelh1, :hankelh1x, :hankelh2, :hankelh2x, :/, :rem2pi)) && continue
+        for (M, f, arity) in DiffRules.diffrules(filter_modules = nothing)
+            if f in (:hankelh1, :hankelh1x, :hankelh2, :hankelh2x, :/, :rem2pi)
+                continue  # Skip these rules
+            elseif !(isdefined(@__MODULE__, M) && isdefined(getfield(@__MODULE__, M), f))
+                continue  # Skip rules for methods not defined in the current scope
+            end
             println("       ...auto-testing $(M).$(f) with $arity arguments")
             if arity == 1
                 deriv = DiffRules.diffrule(M, f, :x)
-                modifier = in(f, (:asec, :acsc, :asecd, :acscd, :acosh, :acoth)) ? one(V) : zero(V)
+                modifier = if in(f, (:asec, :acsc, :asecd, :acscd, :acosh, :acoth))
+                    one(V)
+                elseif in(f, (:log1mexp, :log2mexp))
+                    -one(V)
+                else
+                    zero(V)
+                end
                 @eval begin
                     x = rand() + $modifier
                     dx = $M.$f(Dual{TestTag()}(x, one(x)))