fixes + tests

Author: mcabbott

src/extra_rules.jl

@@ -246,6 +246,7 @@ end
 @ChainRules.non_differentiable Base.:(|)(a::Integer, b::Integer)
 @ChainRules.non_differentiable Base.throw(err)
 @ChainRules.non_differentiable Core.Compiler.return_type(args...)
+
 ChainRulesCore.canonicalize(::NoTangent) = NoTangent()
 
 # Disable thunking at higher order (TODO: These should go into ChainRulesCore)
@@ -262,3 +263,12 @@ Base.real(z::NoTangent) = z  # TODO should be in CRC, https://github.com/JuliaDi
 
 # Avoid https://github.com/JuliaDiff/ChainRulesCore.jl/pull/495
 ChainRulesCore._backing_error(P::Type{<:Base.Pairs}, G::Type{<:NamedTuple}, E::Type{<:AbstractDict}) = nothing
+
+# For gradient(pow_simd, 2, 3)[1] in zygote_features.jl
+ChainRulesCore.@non_differentiable Base.SimdLoop.simd_inner_length(::Any, ::Any)
+
+# This allows fill!(similar([1,2,3], ZeroTangent), false)
+function Base.convert(::Type{ZeroTangent}, x::Number)
+    iszero(x) || throw(InexactError(:convert, ZeroTangent, x))
+    ZeroTangent()
+end

src/runtime.jl

@@ -28,3 +28,9 @@ accum(x::Tangent{T}, y::Tangent) where T = _tangent(T, accum(backing(x), backing
 _tangent(::Type{T}, z) where T = Tangent{T,typeof(z)}(z)
 _tangent(::Type, ::NamedTuple{()}) = NoTangent()
 _tangent(::Type, ::NamedTuple{<:Any, <:Tuple{Vararg{AbstractZero}}}) = NoTangent()
+
+function accum(x::Tangent{T}, y::Tuple) where {T<:Tuple}
+  # @warn "gradient is both a Tangent and a Tuple" x y
+  _tangent(T, accum(backing(x), y))
+end
+accum(x::Tuple, y::Tangent{<:Tuple}) = accum(y, x)

test/chainrules.jl

@@ -1,9 +1,13 @@
 
 # This file has integration tests for some rules defined in ChainRules.jl,
 # especially those which aim to support higher derivatives, as properly
-# testing those is difficult.
+# testing those is difficult. Organised according to the files in CR.jl.
+
+using Diffractor, ForwardDiff, ChainRulesCore
+using Test, LinearAlgebra
+
+using Test: Threw, eval_test
 
-using Diffractor, ChainRulesCore, ForwardDiff
 
 #####
 ##### Base/array.jl
@@ -13,7 +17,6 @@ using Diffractor, ChainRulesCore, ForwardDiff
 
 
 
-
 #####
 ##### Base/arraymath.jl
 #####
@@ -33,21 +36,58 @@ using Diffractor, ChainRulesCore, ForwardDiff
 ##### Base/indexing.jl
 #####
 
+@testset "getindex, first" begin
+    @test_broken gradient(x -> sum(abs2, gradient(first, x)[1]), [1,2,3])[1] == [0, 0, 0]  # MethodError: no method matching +(::Tuple{ZeroTangent, ZeroTangent}, ::Tuple{ZeroTangent, ZeroTangent})
+    @test_broken gradient(x -> sum(abs2, gradient(sqrt∘first, x)[1]), [1,2,3])[1] ≈ [-0.25, 0, 0]  # error() in perform_optic_transform(ff::Type{Diffractor.∂⃖recurse{2}}, args::Any)
+    @test gradient(x -> sum(abs2, gradient(x -> x[1]^2, x)[1]), [1,2,3])[1] == [8, 0, 0]
+    @test_broken gradient(x -> sum(abs2, gradient(x -> sum(x[1:2])^2, x)[1]), [1,2,3])[1] == [48, 0, 0]  # MethodError: no method matching +(::Tuple{ZeroTangent, ZeroTangent}, ::Tuple{ZeroTangent, ZeroTangent})
+end
 
-
+@testset "eachcol etc" begin
+    @test gradient(m -> sum(prod, eachcol(m)), [1 2 3; 4 5 6])[1] == [4 5 6; 1 2 3]
+    @test gradient(m -> sum(first, eachcol(m)), [1 2 3; 4 5 6])[1] == [1 1 1; 0 0 0]
+    @test gradient(m -> sum(first(eachcol(m))), [1 2 3; 4 5 6])[1] == [1 0 0; 1 0 0]
+    @test_skip gradient(x -> sum(sin, gradient(m -> sum(first(eachcol(m))), x)[1]), [1 2 3; 4 5 6])[1]  # MethodError: no method matching one(::Base.OneTo{Int64}), unzip_broadcast, split_bc_forwards
+end
 
 #####
 ##### Base/mapreduce.jl
 #####
 
+@testset "sum" begin
+    @test gradient(x -> sum(abs2, gradient(sum, x)[1]), [1,2,3])[1] == [0,0,0]
+    @test gradient(x -> sum(abs2, gradient(x -> sum(abs2, x), x)[1]), [1,2,3])[1] == [8,16,24]
+
+    @test gradient(x -> sum(abs2, gradient(sum, x .^ 2)[1]), [1,2,3])[1] == [0,0,0]
+    @test gradient(x -> sum(abs2, gradient(sum, x .^ 3)[1]), [1,2,3])[1] == [0,0,0]
+end
 
+@testset "foldl" begin
+
+    @test gradient(x -> foldl(*, x), [1,2,3,4])[1] == [24.0, 12.0, 8.0, 6.0]
+    @test gradient(x -> foldl(*, x; init=5), [1,2,3,4])[1] == [120.0, 60.0, 40.0, 30.0]
+    @test gradient(x -> foldr(*, x), [1,2,3,4])[1] == [24, 12, 8, 6]
+
+    @test gradient(x -> foldl(*, x), (1,2,3,4))[1] == Tangent{NTuple{4,Int}}(24.0, 12.0, 8.0, 6.0)
+    @test_broken gradient(x -> foldl(*, x; init=5), (1,2,3,4))[1] == Tangent{NTuple{4,Int}}(120.0, 60.0, 40.0, 30.0)  # does not return a Tangent
+    @test gradient(x -> foldl(*, x; init=5), (1,2,3,4)) |> only |> Tuple == (120.0, 60.0, 40.0, 30.0)
+    @test_broken gradient(x -> foldr(*, x), (1,2,3,4))[1] == Tangent{NTuple{4,Int}}(24, 12, 8, 6)
+    @test gradient(x -> foldr(*, x), (1,2,3,4)) |> only |> Tuple == (24, 12, 8, 6)
+
+end
 
 
 #####
 ##### LinearAlgebra/dense.jl
 #####
 
 
+@testset "dot" begin
+
+    @test gradient(x -> dot(x, [1,2,3])^2, [4,5,6])[1] == [64,128,192]
+    @test_broken gradient(x -> sum(gradient(x -> dot(x, [1,2,3])^2, x)[1]), [4,5,6])[1] == [12,24,36]  # MethodError: no method matching +(::Tuple{Tangent{ChainRules.var
+
+end
 
 
 #####

test/diffractor_01.jl

@@ -1,16 +1,20 @@
-# The rest of this file is unchanged, except the very end,
-# but IMO we should move these tests to a new file.
+# This file has tests written specifically for Diffractor v0.1,
+# which were in runtests.jl before PR 73 moved them all.
+# (This commit has all changes to 27 Dec 2022.)
 
-# Loading Diffractor: var"'" globally will break many tests above, which use it for adjoint.
+using Test
+
+using Diffractor
+using Diffractor: ∂⃖, DiffractorRuleConfig
 
-using Diffractor: var"'", ∂⃖, DiffractorRuleConfig
 using ChainRules
 using ChainRulesCore
 using ChainRulesCore: ZeroTangent, NoTangent, frule_via_ad, rrule_via_ad
 using Symbolics
-using LinearAlgebra
 
+using LinearAlgebra
 
+# Loading Diffractor: var"'" globally will break many tests above, which use it for adjoint.
 const fwd = Diffractor.PrimeDerivativeFwd
 const bwd = Diffractor.PrimeDerivativeBack
 
@@ -48,8 +52,10 @@ ChainRules.rrule(::typeof(my_tuple), args...) = args, Δ->Core.tuple(NoTangent()
 @test isequal(simplify(x8), simplify((η + (α*ζ) + (β*ϵ) + (δ*(γ + (α*β))))*exp(ω)))
 
 # Minimal 2-nd order forward smoke test
-@test Diffractor.∂☆{2}()(Diffractor.ZeroBundle{2}(sin),
-    Diffractor.TangentBundle{2}(1.0, (1.0, 1.0, 0.0)))[Diffractor.CanonicalTangentIndex(1)] == sin'(1.0)
+let var"'" = Diffractor.PrimeDerivativeFwd
+    @test Diffractor.∂☆{2}()(Diffractor.ZeroBundle{2}(sin),
+        Diffractor.ExplicitTangentBundle{2}(1.0, (1.0, 1.0, 0.0)))[Diffractor.CanonicalTangentIndex(1)] == sin'(1.0)
+end
 
 function simple_control_flow(b, x)
     if b
@@ -95,8 +101,8 @@ let var"'" = Diffractor.PrimeDerivativeBack
     @test @inferred(sin''(1.0)) == -sin(1.0)
     @test sin'''(1.0) == -cos(1.0)
     @test sin''''(1.0) == sin(1.0)
-    @test sin'''''(1.0) == cos(1.0)  # broken = VERSION >= v"1.8"
-    @test sin''''''(1.0) == -sin(1.0)  # broken = VERSION >= v"1.8"
+    @test sin'''''(1.0) == cos(1.0)
+    @test sin''''''(1.0) == -sin(1.0)
 
     f_getfield(x) = getfield((x,), 1)
     @test f_getfield'(1) == 1
@@ -149,7 +155,7 @@ function sin_twice_fwd(x)
     end
 end
 let var"'" = Diffractor.PrimeDerivativeFwd
-    @test sin_twice_fwd'(1.0) == sin'''(1.0)
+    @test_broken sin_twice_fwd'(1.0) == sin'''(1.0)
 end
 
 # Regression tests
@@ -262,24 +268,31 @@ z45, delta45 = frule_via_ad(DiffractorRuleConfig(), (0,1), x -> log(exp(x)), 2)
     @test tup_adj[2] ≈ [0.6666666666666666 0.5 0.4]
     @test tup_adj[2] isa Transpose
     @test gradient(x -> sum(atan.(x, (1,2,3))), Diagonal([4,5,6]))[1] isa Diagonal
-    
+
     @test gradient(x -> sum((y -> (x*y)).([1,2,3])), 4.0) == (6.0,)  # closure
 end
 
 @testset "broadcast, 2nd order" begin
     @test gradient(x -> gradient(y -> sum(y .* y), x)[1] |> sum, [1,2,3.0])[1] == [2,2,2]  # calls "split broadcasting generic" with f = unthunk
     @test gradient(x -> gradient(y -> sum(y .* x), x)[1].^3 |> sum, [1,2,3.0])[1] == [3,12,27]
-    @test_broken gradient(x -> gradient(y -> sum(y .* 2 .* y'), x)[1] |> sum, [1,2,3.0])[1] == [12, 12, 12]  # Control flow support not fully implemented yet for higher-order
+    @test_broken gradient(x -> gradient(y -> sum(y .* 2 .* y'), x)[1] |> sum, [1,2,3.0])[1] == [12, 12, 12]
 
     @test_broken gradient(x -> sum(gradient(x -> sum(x .^ 2 .+ x'), x)[1]), [1,2,3.0])[1] == [6,6,6]  # BoundsError: attempt to access 18-element Vector{Core.Compiler.BasicBlock} at index [0]
     @test_broken gradient(x -> sum(gradient(x -> sum((x .+ 1) .* x .- x), x)[1]), [1,2,3.0])[1] == [2,2,2]
     @test_broken gradient(x -> sum(gradient(x -> sum(x .* x ./ 2), x)[1]), [1,2,3.0])[1] == [1,1,1]
-  
+
     @test_broken gradient(x -> sum(gradient(x -> sum(exp.(x)), x)[1]), [1,2,3])[1] ≈ exp.(1:3)  # MethodError: no method matching copy(::Nothing)
     @test_broken gradient(x -> sum(gradient(x -> sum(atan.(x, x')), x)[1]), [1,2,3.0])[1] ≈ [0,0,0]
     @test_broken gradient(x -> sum(gradient(x -> sum(transpose(x) .* x), x)[1]), [1,2,3]) == ([6,6,6],) # accum(a::Transpose{Float64, Vector{Float64}}, b::ChainRulesCore.Tangent{Transpose{Int64, Vector{Int64}}, NamedTuple{(:parent,), Tuple{ChainRulesCore.NoTangent}}})
     @test_broken gradient(x -> sum(gradient(x -> sum(transpose(x) ./ x.^2), x)[1]), [1,2,3])[1] ≈ [27.675925925925927, -0.824074074074074, -2.1018518518518516]
-    
+
     @test_broken gradient(z -> gradient(x -> sum((y -> (x^2*y)).([1,2,3])), z)[1], 5.0) == (12.0,)
 end
 
+# Issue 67, due to https://github.com/JuliaDiff/ChainRulesCore.jl/pull/495
+@test gradient(identity∘sqrt, 4.0) == (0.25,)
+
+# Issue #70 - Complex & getproperty
+@test_broken gradient(x -> x.re, 2+3im)[1] == 1  # Tangent{Complex{Int64}}(re = 1,)
+@test_broken gradient(x -> abs2(x * x.re), 4+5im)[1] == 456 + 160im  # accum(a::ComplexF64, b::Tangent)
+@test gradient(x -> abs2(x * real(x)), 4+5im)[1] == 456 + 160im