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Merge pull request #189 from JuliaDiff/ox/rand_tangent
Bring back rand_tangent
2 parents 814d2bb + fc3fb1e commit 492364c

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+173
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5 files changed

+173
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‎src/ChainRulesTestUtils.jl‎

Lines changed: 2 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -9,17 +9,16 @@ using LinearAlgebra
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using Random
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using Test
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import FiniteDifferences: rand_tangent
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export TestIterator
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export test_approx, test_scalar, test_frule, test_rrule, generate_well_conditioned_matrix
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export ⊢
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export ⊢, rand_tangent
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export @maybe_inferred
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__init__() = init_test_inferred_setting!()
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include("global_config.jl")
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include("rand_tangent.jl")
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include("generate_tangent.jl")
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include("data_generation.jl")
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include("iterator.jl")

‎src/rand_tangent.jl‎

Lines changed: 63 additions & 0 deletions
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@@ -0,0 +1,63 @@
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"""
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rand_tangent([rng::AbstractRNG,] x)
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Returns a arbitary tangent vector _appropriate_ for the primal value `x`.
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Note that despite the name, no promises on the statistical randomness are made.
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Rather it is an arbitary value, that is generated using the `rng`.
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"""
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rand_tangent(x) = rand_tangent(Random.GLOBAL_RNG, x)
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rand_tangent(rng::AbstractRNG, x::Symbol) = NoTangent()
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rand_tangent(rng::AbstractRNG, x::AbstractChar) = NoTangent()
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rand_tangent(rng::AbstractRNG, x::AbstractString) = NoTangent()
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rand_tangent(rng::AbstractRNG, x::Integer) = NoTangent()
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# Try and make nice numbers with short decimal representations for good error messages
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# while also not biasing the sample space too much
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function rand_tangent(rng::AbstractRNG, x::T) where {T<:Number}
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# multiply by 9 to give a bigger range of values tested: no so tightly clustered around 0.
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return round(9 * randn(rng, T), sigdigits=5, base=2)
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end
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rand_tangent(rng::AbstractRNG, x::Float64) = rand(rng, -9:0.01:9)
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function rand_tangent(rng::AbstractRNG, x::ComplexF64)
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return ComplexF64(rand(rng, -9:0.1:9), rand(rng, -9:0.1:9))
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end
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#BigFloat/MPFR is finicky about short numbers, this doesn't always work as well as it should
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# multiply by 9 to give a bigger range of values tested: no so tightly clustered around 0.
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rand_tangent(rng::AbstractRNG, ::BigFloat) = round(big(9 * randn(rng)), sigdigits=5, base=2)
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rand_tangent(rng::AbstractRNG, x::StridedArray{T, 0}) where {T} = fill(rand_tangent(x[1]))
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rand_tangent(rng::AbstractRNG, x::StridedArray) = rand_tangent.(Ref(rng), x)
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rand_tangent(rng::AbstractRNG, x::Adjoint) = adjoint(rand_tangent(rng, parent(x)))
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rand_tangent(rng::AbstractRNG, x::Transpose) = transpose(rand_tangent(rng, parent(x)))
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function rand_tangent(rng::AbstractRNG, x::T) where {T<:Tuple}
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return Tangent{T}(rand_tangent.(Ref(rng), x)...)
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end
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function rand_tangent(rng::AbstractRNG, xs::T) where {T<:NamedTuple}
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return Tangent{T}(; map(x -> rand_tangent(rng, x), xs)...)
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end
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function rand_tangent(rng::AbstractRNG, x::T) where {T}
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if !isstructtype(T)
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throw(ArgumentError("Non-struct types are not supported by this fallback."))
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end
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field_names = fieldnames(T)
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tangents = map(field_names) do field_name
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rand_tangent(rng, getfield(x, field_name))
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end
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if all(tangent isa NoTangent for tangent in tangents)
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# if none of my fields can be perturbed then I can't be perturbed
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return NoTangent()
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else
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Tangent{T}(; NamedTuple{field_names}(tangents)...)
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end
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end
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rand_tangent(rng::AbstractRNG, ::Type) = NoTangent()
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rand_tangent(rng::AbstractRNG, ::Module) = NoTangent()

‎test/iterator.jl‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -88,7 +88,7 @@
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@testset "rand_tangent" begin
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data = randn(2, 3, 4)
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iter = TestIterator(data, Base.SizeUnknown(), Base.EltypeUnknown())
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∂iter = FiniteDifferences.rand_tangent(iter)
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∂iter = rand_tangent(iter)
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@test ∂iter isa typeof(iter)
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@test size(∂iter.data) == size(iter.data)
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@test eltype(∂iter.data) === eltype(iter.data)

‎test/rand_tangent.jl‎

Lines changed: 106 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,106 @@
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# Test struct for `rand_tangent` and `difference`.
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struct Bar
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a::Float64
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b::Int
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c::Any
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end
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@testset "rand_tangent" begin
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rng = MersenneTwister(123456)
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@testset "Primal: $(typeof(x)), Tangent: $T_tangent" for (x, T_tangent) in [
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# Things without sensible tangents.
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("hi", NoTangent),
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('a', NoTangent),
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(:a, NoTangent),
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(true, NoTangent),
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(4, NoTangent),
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(FiniteDifferences, NoTangent), # Module object
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# Types (not instances of type)
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(Bar, NoTangent),
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(Union{Int, Bar}, NoTangent),
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(Union{Int, Bar}, NoTangent),
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(Vector, NoTangent),
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(Vector{Float64}, NoTangent),
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(Integer, NoTangent),
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(Type{<:Real}, NoTangent),
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# Numbers.
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(5.0, Float64),
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(5.0 + 0.4im, Complex{Float64}),
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(big(5.0), BigFloat),
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# StridedArrays.
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(fill(randn(Float32)), Array{Float32, 0}),
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(fill(randn(Float64)), Array{Float64, 0}),
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(randn(Float32, 3), Vector{Float32}),
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(randn(Complex{Float64}, 2), Vector{Complex{Float64}}),
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(randn(5, 4), Matrix{Float64}),
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(randn(Complex{Float32}, 5, 4), Matrix{Complex{Float32}}),
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([randn(5, 4), 4.0], Vector{Any}),
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# Wrapper Arrays
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(randn(5, 4)', Adjoint{Float64, Matrix{Float64}}),
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(transpose(randn(5, 4)), Transpose{Float64, Matrix{Float64}}),
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# Tuples.
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((4.0, ), Tangent{Tuple{Float64}}),
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((5.0, randn(3)), Tangent{Tuple{Float64, Vector{Float64}}}),
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# NamedTuples.
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((a=4.0, ), Tangent{NamedTuple{(:a,), Tuple{Float64}}}),
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((a=5.0, b=1), Tangent{NamedTuple{(:a, :b), Tuple{Float64, Int}}}),
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# structs.
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(Bar(5.0, 4, rand(rng, 3)), Tangent{Bar}),
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(Bar(4.0, 3, Bar(5.0, 2, 4)), Tangent{Bar}),
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(sin, NoTangent),
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# all fields NoTangent implies NoTangent
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(Pair(:a, "b"), NoTangent),
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(1:10, NoTangent),
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(1:2:10, NoTangent),
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# LinearAlgebra types (also just structs).
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(
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UpperTriangular(randn(3, 3)),
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Tangent{UpperTriangular{Float64, Matrix{Float64}}},
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),
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(
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Diagonal(randn(2)),
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Tangent{Diagonal{Float64, Vector{Float64}}},
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),
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(
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Symmetric(randn(2, 2)),
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Tangent{Symmetric{Float64, Matrix{Float64}}},
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),
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(
78+
Hermitian(randn(ComplexF64, 1, 1)),
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Tangent{Hermitian{ComplexF64, Matrix{ComplexF64}}},
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),
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]
82+
@test rand_tangent(rng, x) isa T_tangent
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@test rand_tangent(x) isa T_tangent
84+
end
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86+
@testset "erroring cases" begin
87+
# Ensure struct fallback errors for non-struct types.
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@test_throws ArgumentError invoke(rand_tangent, Tuple{AbstractRNG, Any}, rng, 5.0)
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end
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@testset "compsition of addition" begin
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x = Bar(1.5, 2, Bar(1.1, 3, [1.7, 1.4, 0.9]))
93+
@test x + rand_tangent(x) isa typeof(x)
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@test x + (rand_tangent(x) + rand_tangent(x)) isa typeof(x)
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end
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# Julia 1.6 changed to using Ryu printing algorithm and seems better at printing short
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VERSION > v"1.6" && @testset "niceness of printing" begin
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for i in 1:50
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@test length(string(rand_tangent(1.0))) <= 6
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@test length(string(rand_tangent(1.0 + 1.0im))) <= 12
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@test length(string(rand_tangent(1f0))) <= 12
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@test length(string(rand_tangent(big"1.0"))) <= 12
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end
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end
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end

‎test/runtests.jl‎

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Original file line numberDiff line numberDiff line change
@@ -14,6 +14,7 @@ ChainRulesTestUtils.TEST_INFERRED[] = true
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include("check_result.jl")
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include("testers.jl")
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include("data_generation.jl")
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include("rand_tangent.jl")
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include("deprecated.jl")
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end

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