Merge branch 'JuliaDiff:master' into irrational

.github/workflows/ci.yml

@@ -19,7 +19,7 @@ jobs:
         version:
           - '1.6'
           - '1'
-          - 'nightly'
+          # - 'nightly'
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -32,7 +32,7 @@ LogExpFunctions = "0.3"
 NaNMath = "1"
 Preferences = "1"
 SpecialFunctions = "1, 2"
-StaticArrays = "1.5"
+StaticArrays = "1.5 - 1.6"
 julia = "1.6"
 
 [extras]

test/AllocationsTest.jl

@@ -24,17 +24,25 @@ convert_test_574() = convert(ForwardDiff.Dual{Nothing,ForwardDiff.Dual{Nothing,F
     index = 1
     alloc = @allocated ForwardDiff.seed!(duals, x, index, seeds)
     alloc = @allocated ForwardDiff.seed!(duals, x, index, seeds)
-    @test alloc == 0
+    if VERSION < v"1.9"
+        @test alloc == 0
+    else
+        @test_broken alloc == 0
+    end
 
     index = 1
     alloc = @allocated ForwardDiff.seed!(duals, x, index, seed)
     alloc = @allocated ForwardDiff.seed!(duals, x, index, seed)
-    @test alloc == 0
-
+    if VERSION < v"1.9"
+        @test alloc == 0
+    else
+        @test_broken alloc == 0
+    end
+
     alloc = @allocated convert_test_574()
     alloc = @allocated convert_test_574()
     @test alloc == 0
-  
+
 end
 
 end

test/DerivativeTest.jl

@@ -17,8 +17,7 @@ Random.seed!(1)
 
 const x = 1
 
-for f in DiffTests.NUMBER_TO_NUMBER_FUNCS
-    println("  ...testing $f")
+@testset "$f" for f in DiffTests.NUMBER_TO_NUMBER_FUNCS
     v = f(x)
     d = ForwardDiff.derivative(f, x)
     @test isapprox(d, Calculus.derivative(f, x), atol=FINITEDIFF_ERROR)
@@ -29,8 +28,7 @@ for f in DiffTests.NUMBER_TO_NUMBER_FUNCS
     @test isapprox(DiffResults.derivative(out), d)
 end
 
-for f in DiffTests.NUMBER_TO_ARRAY_FUNCS
-    println("  ...testing $f")
+@testset "$f" for f in DiffTests.NUMBER_TO_ARRAY_FUNCS
     v = f(x)
     d = ForwardDiff.derivative(f, x)
 
@@ -47,8 +45,7 @@ for f in DiffTests.NUMBER_TO_ARRAY_FUNCS
     @test isapprox(DiffResults.derivative(out), d)
 end
 
-for f! in DiffTests.INPLACE_NUMBER_TO_ARRAY_FUNCS
-    println("  ...testing $f!")
+@testset "$(f!)" for f! in DiffTests.INPLACE_NUMBER_TO_ARRAY_FUNCS
     m, n = 3, 2
     y = fill(0.0, m, n)
     f = x -> (tmp = similar(y, promote_type(eltype(y), typeof(x)), m, n); f!(tmp, x); tmp)

test/DualTest.jl

@@ -31,7 +31,6 @@ ForwardDiff.:≺(::Type{TestTag}, ::Type{OuterTestTag}) = true
 ForwardDiff.:≺(::Type{OuterTestTag}, ::Type{TestTag}) = false
 
 @testset "Dual{Z,$V,$N} and Dual{Z,Dual{Z,$V,$M},$N}" for N in (0,3), M in (0,4), V in (Int, Float32)
-    println("  ...testing Dual{TestTag(),$V,$N} and Dual{TestTag(),Dual{TestTag(),$V,$M},$N}")
 
     PARTIALS = Partials{N,V}(ntuple(n -> intrand(V), N))
     PRIMAL = intrand(V)
@@ -466,13 +465,12 @@ ForwardDiff.:≺(::Type{OuterTestTag}, ::Type{TestTag}) = false
     @test abs(NESTED_FDNUM) === NESTED_FDNUM
 
     if V != Int
-        @testset "$f" for (M, f, arity) in DiffRules.diffrules(filter_modules = nothing)
+        @testset "$(M).$(f) with $arity arguments" for (M, f, arity) in DiffRules.diffrules(filter_modules = nothing)
             if f in (:/, :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 = if in(f, (:asec, :acsc, :asecd, :acscd, :acosh, :acoth))

test/GradientTest.jl

@@ -21,7 +21,6 @@ v = f(x)
 g = [-9.4, 15.6, 52.0]
 
 @testset "Rosenbrock, chunk size = $c and tag = $(repr(tag))" for c in (1, 2, 3), tag in (nothing, Tag(f, eltype(x)))
-    println("  ...running hardcoded test with chunk size = $c and tag = $(repr(tag))")
     cfg = ForwardDiff.GradientConfig(f, x, ForwardDiff.Chunk{c}(), tag)
 
     @test eltype(cfg) == Dual{typeof(tag), eltype(x), c}
@@ -60,8 +59,7 @@ cfgx = ForwardDiff.GradientConfig(sin, x)
     v = f(X)
     g = ForwardDiff.gradient(f, X)
     @test isapprox(g, Calculus.gradient(f, X), atol=FINITEDIFF_ERROR)
-    for c in CHUNK_SIZES, tag in (nothing, Tag(f, eltype(x)))
-        println("  ...testing $f with chunk size = $c and tag = $(repr(tag))")
+    @testset "... with chunk size = $c and tag = $(repr(tag))" for c in CHUNK_SIZES, tag in (nothing, Tag(f, eltype(x)))
         cfg = ForwardDiff.GradientConfig(f, X, ForwardDiff.Chunk{c}(), tag)
 
         out = ForwardDiff.gradient(f, X, cfg)
@@ -82,9 +80,7 @@ end
 # test specialized StaticArray codepaths #
 ##########################################
 
-println("  ...testing specialized StaticArray codepaths")
-
-@testset "$T" for T in (StaticArrays.SArray, StaticArrays.MArray)
+@testset "Specialized StaticArray codepaths: $T" for T in (StaticArrays.SArray, StaticArrays.MArray)
     x = rand(3, 3)
 
     sx = T{Tuple{3,3}}(x)
@@ -172,7 +168,7 @@ end
     function f(p)
         sum(collect(0.0:p[1]:p[2]))
     end
-    @test ForwardDiff.gradient(f, [0.2,25.0]) == [7875.0, 0.0]
+    @test ForwardDiff.gradient(f, [0.3, 25.0]) == [3486.0, 0.0]
 end
 
 @testset "det with branches" begin
@@ -193,7 +189,7 @@ end
 
     # And issue 407
     @test ForwardDiff.hessian(det, A) ≈ ForwardDiff.hessian(det2, A)
-    
+
     # https://discourse.julialang.org/t/forwarddiff-and-zygote-return-wrong-jacobian-for-log-det-l/77961
     S = [1.0 0.8; 0.8 1.0]
     L = cholesky(S).L

test/HessianTest.jl

@@ -23,8 +23,7 @@ h = [-66.0  -40.0    0.0;
      -40.0  130.0  -80.0;
        0.0  -80.0  200.0]
 
-for c in HESSIAN_CHUNK_SIZES, tag in (nothing, Tag((f,ForwardDiff.gradient), eltype(x)))
-    println("  ...running hardcoded test with chunk size = $c and tag = $(repr(tag))")
+@testset "running hardcoded test with chunk size = $c and tag = $(repr(tag))" for c in HESSIAN_CHUNK_SIZES, tag in (nothing, Tag((f,ForwardDiff.gradient), eltype(x)))
     cfg = ForwardDiff.HessianConfig(f, x, ForwardDiff.Chunk{c}(), tag)
     resultcfg = ForwardDiff.HessianConfig(f, DiffResults.HessianResult(x), x, ForwardDiff.Chunk{c}(), tag)
 
@@ -69,8 +68,7 @@ for f in DiffTests.VECTOR_TO_NUMBER_FUNCS
     h = ForwardDiff.hessian(f, X)
     # finite difference approximation error is really bad for Hessians...
     @test isapprox(h, Calculus.hessian(f, X), atol=0.02)
-    for c in HESSIAN_CHUNK_SIZES, tag in (nothing, Tag((f,ForwardDiff.gradient), eltype(x)))
-        println("  ...testing $f with chunk size = $c and tag = $(repr(tag))")
+    @testset "$f with chunk size = $c and tag = $(repr(tag))" for c in HESSIAN_CHUNK_SIZES, tag in (nothing, Tag((f,ForwardDiff.gradient), eltype(x)))
         cfg = ForwardDiff.HessianConfig(f, X, ForwardDiff.Chunk{c}(), tag)
         resultcfg = ForwardDiff.HessianConfig(f, DiffResults.HessianResult(X), X, ForwardDiff.Chunk{c}(), tag)
 
@@ -93,7 +91,7 @@ end
 # test specialized StaticArray codepaths #
 ##########################################
 
-println("  ...testing specialized StaticArray codepaths")
+@info "testing specialized StaticArray codepaths"
 
 x = rand(3, 3)
 for T in (StaticArrays.SArray, StaticArrays.MArray)

test/JacobianTest.jl

@@ -103,11 +103,10 @@ for f in DiffTests.ARRAY_TO_ARRAY_FUNCS
     v = f(X)
     j = ForwardDiff.jacobian(f, X)
     @test isapprox(j, Calculus.jacobian(x -> vec(f(x)), X, :forward), atol=1.3FINITEDIFF_ERROR)
-    for c in CHUNK_SIZES, tag in (nothing, Tag)
+    @testset "$f with chunk size = $c and tag = $(repr(tag))" for c in CHUNK_SIZES, tag in (nothing, Tag)
         if tag == Tag
             tag = Tag(f, eltype(X))
         end
-        println("  ...testing $f with chunk size = $c and tag = $(repr(tag))")
         cfg = JacobianConfig(f, X, ForwardDiff.Chunk{c}(), tag)
 
         out = ForwardDiff.jacobian(f, X, cfg)
@@ -129,8 +128,7 @@ for f! in DiffTests.INPLACE_ARRAY_TO_ARRAY_FUNCS
     f!(v, X)
     j = ForwardDiff.jacobian(f!, fill!(similar(Y), 0.0), X)
     @test isapprox(j, Calculus.jacobian(x -> (y = fill!(similar(Y), 0.0); f!(y, x); vec(y)), X, :forward), atol=FINITEDIFF_ERROR)
-    for c in CHUNK_SIZES, tag in (nothing, Tag(f!, eltype(X)))
-        println("  ...testing $(f!) with chunk size = $c and tag = $(repr(tag))")
+    @testset "$(f!) with chunk size = $c and tag = $(repr(tag))" for c in CHUNK_SIZES, tag in (nothing, Tag(f!, eltype(X)))
         ycfg = JacobianConfig(f!, fill!(similar(Y), 0.0), X, ForwardDiff.Chunk{c}(), tag)
 
         y = fill!(similar(Y), 0.0)
@@ -164,7 +162,7 @@ end
 # test specialized StaticArray codepaths #
 ##########################################
 
-println("  ...testing specialized StaticArray codepaths")
+@info "testing specialized StaticArray codepaths"
 
 x = rand(3, 3)
 for T in (StaticArrays.SArray, StaticArrays.MArray)

test/PartialsTest.jl

@@ -8,7 +8,6 @@ using ForwardDiff: Partials
 samerng() = MersenneTwister(1)
 
 @testset "Partials{$N,$T}" for N in (0, 3), T in (Int, Float32, Float64)
-    println("  ...testing Partials{$N,$T}")
 
     VALUES = (rand(T,N)...,)
     PARTIALS = Partials{N,T}(VALUES)