@@ -13,18 +13,18 @@ const bwd = Diffractor.PrimeDerivativeBack
1313
1414
1515# Regression tests
16- @test gradient (x -> sum (abs2, x .+ 1.0 ), zeros (3 ))[1 ] == [2.0 , 2.0 , 2.0 ] broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
16+ @test gradient (x -> sum (abs2, x .+ 1.0 ), zeros (3 ))[1 ] == [2.0 , 2.0 , 2.0 ]
1717
1818function f_broadcast (a)
1919 l = a / 2.0 * [[0. 1. 1. ]; [1. 0. 1. ]; [1. 1. 0. ]]
2020 return sum (l)
2121end
22- @test fwd (f_broadcast)(1.0 ) == bwd (f_broadcast)(1.0 ) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
22+ @test fwd (f_broadcast)(1.0 ) == bwd (f_broadcast)(1.0 )
2323
2424# Make sure that there's no infinite recursion in kwarg calls
2525g_kw (;x= 1.0 ) = sin (x)
2626f_kw (x) = g_kw (;x)
27- @test bwd (f_kw)(1.0 ) == bwd (sin)(1.0 ) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
27+ @test bwd (f_kw)(1.0 ) == bwd (sin)(1.0 )
2828
2929function f_crit_edge (a, b, c, x)
3030 # A function with two critical edges. This used to trigger an issue where
@@ -43,98 +43,98 @@ function f_crit_edge(a, b, c, x)
4343
4444 return y
4545end
46- @test bwd (x-> f_crit_edge (false , false , false , x))(1.0 ) == 1.0 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
47- @test bwd (x-> f_crit_edge (true , true , false , x))(1.0 ) == 2.0 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
48- @test bwd (x-> f_crit_edge (false , true , true , x))(1.0 ) == 12.0 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
49- @test bwd (x-> f_crit_edge (false , false , true , x))(1.0 ) == 4.0 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
46+ @test bwd (x-> f_crit_edge (false , false , false , x))(1.0 ) == 1.0
47+ @test bwd (x-> f_crit_edge (true , true , false , x))(1.0 ) == 2.0
48+ @test bwd (x-> f_crit_edge (false , true , true , x))(1.0 ) == 12.0
49+ @test bwd (x-> f_crit_edge (false , false , true , x))(1.0 ) == 4.0
5050
5151# Issue #27 - Mixup in lifting of getfield
5252let var"'" = bwd
53- @test (x-> x^ 5 )'' (1.0 ) == 20. broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
53+ @test (x-> x^ 5 )'' (1.0 ) == 20.
5454 @test_broken (x-> x^ 5 )''' (1.0 ) == 60.
5555end
5656
5757# Issue #38 - Splatting arrays
58- @test gradient (x -> max (x... ), (1 ,2 ,3 ))[1 ] == (0.0 , 0.0 , 1.0 ) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
59- @test gradient (x -> max (x... ), [1 ,2 ,3 ])[1 ] == [0.0 , 0.0 , 1.0 ] broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
58+ @test gradient (x -> max (x... ), (1 ,2 ,3 ))[1 ] == (0.0 , 0.0 , 1.0 )
59+ @test gradient (x -> max (x... ), [1 ,2 ,3 ])[1 ] == [0.0 , 0.0 , 1.0 ]
6060
6161# Issue #40 - Symbol type parameters not properly quoted
62- @test Diffractor.∂⃖recurse {1} ()(Val{:transformations })[1 ] === Val {:transformations} () broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
62+ @test Diffractor.∂⃖recurse {1} ()(Val{:transformations })[1 ] === Val {:transformations} ()
6363
6464# PR #43
6565loss (res, z, w) = sum (res. U * Diagonal (res. S) * res. V) + sum (res. S .* w)
6666x43 = rand (10 , 10 )
67- @test Diffractor. gradient (x-> loss (svd (x), x[:,1 ], x[:,2 ]), x43) isa Tuple{Matrix{Float64}} broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
67+ @test Diffractor. gradient (x-> loss (svd (x), x[:,1 ], x[:,2 ]), x43) isa Tuple{Matrix{Float64}}
6868
6969# PR # 45 - Calling back into AD from ChainRules
70- @test_broken y45, back45 = rrule_via_ad (DiffractorRuleConfig (), x -> log (exp (x)), 2 ) # https://github.com/JuliaDiff/Diffractor.jl/issues/170
71- @test_broken y45 ≈ 2.0 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
72- @test_broken back45 (1 ) == (ZeroTangent (), 1.0 ) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
70+ y45, back45 = rrule_via_ad (DiffractorRuleConfig (), x -> log (exp (x)), 2 )
71+ @test y45 ≈ 2.0
72+ @test back45 (1 ) == (ZeroTangent (), 1.0 )
7373
7474z45, delta45 = frule_via_ad (DiffractorRuleConfig (), (0 ,1 ), x -> log (exp (x)), 2 )
7575@test z45 ≈ 2.0
7676@test delta45 ≈ 1.0
7777
7878# PR #82 - getindex on non-numeric arrays
79- @test gradient (ls -> ls[1 ](1. ), [Base. Fix1 (* , 1. )])[1 ][1 ] isa Tangent{<: Base.Fix1 } broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
79+ @test gradient (ls -> ls[1 ](1. ), [Base. Fix1 (* , 1. )])[1 ][1 ] isa Tangent{<: Base.Fix1 }
8080
8181@testset " broadcast" begin
8282 # derivatives_given_output
83- @test gradient (x -> sum (x ./ x), [1 ,2 ,3 ]) == ([0 ,0 ,0 ],) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
84- @test gradient (x -> sum (sqrt .(atan .(x, transpose (x)))), [1 ,2 ,3 ])[1 ] ≈ [0.2338 , - 0.0177 , - 0.0661 ] atol= 1e-3 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
85- @test gradient (x -> sum (exp .(log .(x))), [1 ,2 ,3 ]) == ([1 ,1 ,1 ],) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
83+ @test gradient (x -> sum (x ./ x), [1 ,2 ,3 ]) == ([0 ,0 ,0 ],)
84+ @test gradient (x -> sum (sqrt .(atan .(x, transpose (x)))), [1 ,2 ,3 ])[1 ] ≈ [0.2338 , - 0.0177 , - 0.0661 ] atol= 1e-3
85+ @test gradient (x -> sum (exp .(log .(x))), [1 ,2 ,3 ]) == ([1 ,1 ,1 ],)
8686
8787 # frule_via_ad
88- @test gradient (x -> sum ((exp∘ log). (x)), [1 ,2 ,3 ]) == ([1 ,1 ,1 ],) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
88+ @test gradient (x -> sum ((exp∘ log). (x)), [1 ,2 ,3 ]) == ([1 ,1 ,1 ],)
8989 exp_log (x) = exp (log (x))
90- @test gradient (x -> sum (exp_log .(x)), [1 ,2 ,3 ]) == ([1 ,1 ,1 ],) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
91- @test gradient ((x,y) -> sum (x ./ y), [1 2 ; 3 4 ], [1 ,2 ]) == ([1 1 ; 0.5 0.5 ], [- 3 , - 1.75 ]) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
92- @test gradient ((x,y) -> sum (x ./ y), [1 2 ; 3 4 ], 5 ) == ([0.2 0.2 ; 0.2 0.2 ], - 0.4 ) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
90+ @test gradient (x -> sum (exp_log .(x)), [1 ,2 ,3 ]) == ([1 ,1 ,1 ],)
91+ @test gradient ((x,y) -> sum (x ./ y), [1 2 ; 3 4 ], [1 ,2 ]) == ([1 1 ; 0.5 0.5 ], [- 3 , - 1.75 ])
92+ @test gradient ((x,y) -> sum (x ./ y), [1 2 ; 3 4 ], 5 ) == ([0.2 0.2 ; 0.2 0.2 ], - 0.4 )
9393 # closure:
94- @test gradient (x -> sum ((y -> y/ x). ([1 ,2 ,3 ])), 4 ) == (- 0.375 ,) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
94+ @test gradient (x -> sum ((y -> y/ x). ([1 ,2 ,3 ])), 4 ) == (- 0.375 ,)
9595
9696 # array of arrays
97- @test gradient (x -> sum (sum, (x,) ./ x), [1 ,2 ,3 ])[1 ] ≈ [- 4.1666 , 0.3333 , 1.1666 ] atol= 1e-3 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
98- @test gradient (x -> sum (sum, Ref (x) ./ x), [1 ,2 ,3 ])[1 ] ≈ [- 4.1666 , 0.3333 , 1.1666 ] atol= 1e-3 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
99- @test gradient (x -> sum (sum, (x,) ./ x), [1 ,2 ,3 ])[1 ] ≈ [- 4.1666 , 0.3333 , 1.1666 ] atol= 1e-3 broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
97+ @test gradient (x -> sum (sum, (x,) ./ x), [1 ,2 ,3 ])[1 ] ≈ [- 4.1666 , 0.3333 , 1.1666 ] atol= 1e-3
98+ @test gradient (x -> sum (sum, Ref (x) ./ x), [1 ,2 ,3 ])[1 ] ≈ [- 4.1666 , 0.3333 , 1.1666 ] atol= 1e-3
99+ @test gradient (x -> sum (sum, (x,) ./ x), [1 ,2 ,3 ])[1 ] ≈ [- 4.1666 , 0.3333 , 1.1666 ] atol= 1e-3
100100 # must not take fast path
101- @test gradient (x -> sum (sum, (x,) .* transpose (x)), [1 ,2 ,3 ])[1 ] ≈ [12 , 12 , 12 ] broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
101+ @test gradient (x -> sum (sum, (x,) .* transpose (x)), [1 ,2 ,3 ])[1 ] ≈ [12 , 12 , 12 ]
102102
103- @test gradient (x -> sum (x ./ 4 ), [1 ,2 ,3 ]) == ([0.25 , 0.25 , 0.25 ],) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
103+ @test gradient (x -> sum (x ./ 4 ), [1 ,2 ,3 ]) == ([0.25 , 0.25 , 0.25 ],)
104104 # x/y rule
105- @test gradient (x -> sum ([1 ,2 ,3 ] ./ x), 4 ) == (- 0.375 ,) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
105+ @test gradient (x -> sum ([1 ,2 ,3 ] ./ x), 4 ) == (- 0.375 ,)
106106 # x.^2 rule
107- @test gradient (x -> sum (x.^ 2 ), [1 ,2 ,3 ]) == ([2.0 , 4.0 , 6.0 ],) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
107+ @test gradient (x -> sum (x.^ 2 ), [1 ,2 ,3 ]) == ([2.0 , 4.0 , 6.0 ],)
108108 # scalar^2 rule
109- @test gradient (x -> sum ([1 ,2 ,3 ] ./ x.^ 2 ), 4 ) == (- 0.1875 ,) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
109+ @test gradient (x -> sum ([1 ,2 ,3 ] ./ x.^ 2 ), 4 ) == (- 0.1875 ,)
110110
111- @test gradient (x -> sum ((1 ,2 ,3 ) .- x), (1 ,2 ,3 )) == (Tangent {Tuple{Int,Int,Int}} (- 1.0 , - 1.0 , - 1.0 ),) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
112- @test gradient (x -> sum (transpose ([1 ,2 ,3 ]) .- x), (1 ,2 ,3 )) == (Tangent {Tuple{Int,Int,Int}} (- 3.0 , - 3.0 , - 3.0 ),) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
113- @test gradient (x -> sum ([1 2 3 ] .+ x .^ 2 ), (1 ,2 ,3 )) == (Tangent {Tuple{Int,Int,Int}} (6.0 , 12.0 , 18.0 ),) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
111+ @test gradient (x -> sum ((1 ,2 ,3 ) .- x), (1 ,2 ,3 )) == (Tangent {Tuple{Int,Int,Int}} (- 1.0 , - 1.0 , - 1.0 ),)
112+ @test gradient (x -> sum (transpose ([1 ,2 ,3 ]) .- x), (1 ,2 ,3 )) == (Tangent {Tuple{Int,Int,Int}} (- 3.0 , - 3.0 , - 3.0 ),)
113+ @test gradient (x -> sum ([1 2 3 ] .+ x .^ 2 ), (1 ,2 ,3 )) == (Tangent {Tuple{Int,Int,Int}} (6.0 , 12.0 , 18.0 ),)
114114
115115 # Bool output
116- @test gradient (x -> sum (x .> 2 ), [1 ,2 ,3 ]) |> only |> iszero broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
117- @test gradient (x -> sum (1 .+ iseven .(x)), [1 ,2 ,3 ]) |> only |> iszero broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
118- @test gradient ((x,y) -> sum (x .== y), [1 ,2 ,3 ], [1 2 3 ]) == (NoTangent (), NoTangent ()) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
116+ @test gradient (x -> sum (x .> 2 ), [1 ,2 ,3 ]) |> only |> iszero
117+ @test gradient (x -> sum (1 .+ iseven .(x)), [1 ,2 ,3 ]) |> only |> iszero
118+ @test gradient ((x,y) -> sum (x .== y), [1 ,2 ,3 ], [1 2 3 ]) == (NoTangent (), NoTangent ())
119119 # Bool input
120- @test gradient (x -> sum (x .+ [1 ,2 ,3 ]), true ) |> only |> iszero broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
121- @test gradient (x -> sum (x ./ [1 ,2 ,3 ]), [true false ]) |> only |> iszero broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
122- @test gradient (x -> sum (x .* transpose ([1 ,2 ,3 ])), (true , false )) |> only |> iszero broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
120+ @test gradient (x -> sum (x .+ [1 ,2 ,3 ]), true ) |> only |> iszero
121+ @test gradient (x -> sum (x ./ [1 ,2 ,3 ]), [true false ]) |> only |> iszero
122+ @test gradient (x -> sum (x .* transpose ([1 ,2 ,3 ])), (true , false )) |> only |> iszero
123123
124- @test_broken tup_adj = gradient ((x,y) -> sum (2 .* x .+ log .(y)), (1 ,2 ), transpose ([3 ,4 ,5 ])) # https://github.com/JuliaDiff/Diffractor.jl/issues/170
125- @test tup_adj[1 ] == Tangent {Tuple{Int64, Int64}} (6.0 , 6.0 ) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
126- @test tup_adj[2 ] ≈ [0.6666666666666666 0.5 0.4 ] broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
127- @test tup_adj[2 ] isa Transpose broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
128- @test gradient (x -> sum (atan .(x, (1 ,2 ,3 ))), Diagonal ([4 ,5 ,6 ]))[1 ] isa Diagonal broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
124+ tup_adj = gradient ((x,y) -> sum (2 .* x .+ log .(y)), (1 ,2 ), transpose ([3 ,4 ,5 ]))
125+ @test tup_adj[1 ] == Tangent {Tuple{Int64, Int64}} (6.0 , 6.0 )
126+ @test tup_adj[2 ] ≈ [0.6666666666666666 0.5 0.4 ]
127+ @test tup_adj[2 ] isa Transpose
128+ @test gradient (x -> sum (atan .(x, (1 ,2 ,3 ))), Diagonal ([4 ,5 ,6 ]))[1 ] isa Diagonal
129129
130130 # closure:
131- @test gradient (x -> sum ((y -> (x* y)). ([1 ,2 ,3 ])), 4.0 ) == (6.0 ,) broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
131+ @test gradient (x -> sum ((y -> (x* y)). ([1 ,2 ,3 ])), 4.0 ) == (6.0 ,)
132132end
133133
134134@testset " broadcast, 2nd order" begin
135135 # calls "split broadcasting generic" with f = unthunk
136- @test gradient (x -> gradient (y -> sum (y .* y), x)[1 ] |> sum, [1 ,2 ,3.0 ])[1 ] == [2 ,2 ,2 ] broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
137- @test gradient (x -> gradient (y -> sum (y .* x), x)[1 ]. ^ 3 |> sum, [1 ,2 ,3.0 ])[1 ] == [3 ,12 ,27 ] broken = true # https://github.com/JuliaDiff/Diffractor.jl/issues/170
136+ @test gradient (x -> gradient (y -> sum (y .* y), x)[1 ] |> sum, [1 ,2 ,3.0 ])[1 ] == [2 ,2 ,2 ]
137+ @test gradient (x -> gradient (y -> sum (y .* x), x)[1 ]. ^ 3 |> sum, [1 ,2 ,3.0 ])[1 ] == [3 ,12 ,27 ]
138138 # Control flow support not fully implemented yet for higher-order
139139 @test_broken gradient (x -> gradient (y -> sum (y .* 2 .* y' ), x)[1 ] |> sum, [1 ,2 ,3.0 ])[1 ] == [12 , 12 , 12 ]
140140
153153 @test_broken gradient (z -> gradient (x -> sum ((y -> (x^ 2 * y)). ([1 ,2 ,3 ])), z)[1 ], 5.0 ) == (12.0 ,)
154154end
155155
156- end
156+ end
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