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make to_vec(::Integer) an empty vector #189

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2 changes: 1 addition & 1 deletion Project.toml
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "FiniteDifferences"
uuid = "26cc04aa-876d-5657-8c51-4c34ba976000"
version = "0.12.18"
version = "0.12.19"
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should this be breaking instead? i think of it as a bug fix

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My view is treat it as a bug fix for now -- if it turns out to be super breaking and we later decide it's actually a breaking change, we can always bump the minor version number then.


[deps]
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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16 changes: 13 additions & 3 deletions src/grad.jl
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ is defined. Each 2-`Tuple` in `xẋs` contains the value `x` and its tangent `x
function jvp(fdm, f, (x, ẋ)::Tuple{Any, Any})
x_vec, vec_to_x = to_vec(x)
_, vec_to_y = to_vec(f(x))
return vec_to_y(_jvp(fdm, x_vec->to_vec(f(vec_to_x(x_vec)))[1], x_vec, to_vec(ẋ)[1]))
return _int2zero(vec_to_y(_jvp(fdm, x_vec->to_vec(f(vec_to_x(x_vec)))[1], x_vec, to_vec(ẋ)[1])))
end
function jvp(fdm, f, xẋs::Tuple{Any, Any}...)
x, ẋ = collect(zip(xẋs...))
Expand All @@ -70,7 +70,7 @@ Compute an adjoint with any types of arguments `x` for which [`to_vec`](@ref) is
function j′vp(fdm, f, ȳ, x)
x_vec, vec_to_x = to_vec(x)
ȳ_vec, _ = to_vec(ȳ)
return (vec_to_x(_j′vp(fdm, first ∘ to_vec ∘ f ∘ vec_to_x, ȳ_vec, x_vec)), )
return (_int2zero(vec_to_x(_j′vp(fdm, first ∘ to_vec ∘ f ∘ vec_to_x, ȳ_vec, x_vec))), )
end

j′vp(fdm, f, ȳ, xs...) = j′vp(fdm, xs->f(xs...), ȳ, xs)[1]
Expand All @@ -85,4 +85,14 @@ end

Compute the gradient of `f` for any `xs` for which [`to_vec`](@ref) is defined.
"""
grad(fdm, f, xs...) = j′vp(fdm, f, 1, xs...) # `j′vp` with seed of 1
grad(fdm, f, xs...) = j′vp(fdm, f, 1.0, xs...) # `j′vp` with seed of 1

# This deals with the fact that integers are non perturbable
# v, b = to_vec(1);
# v == []
# b(v) == 1
# which means that jvp always returns the integer itself, since [] - [] == []
_int2zero(x) = x
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I suspect that this will miss a few cases (e.g. vectors of integers), but probably that's fine for now -- we can revisit later it needs be.

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On a more general note: is the need for this function a manifestation of the fact that FiniteDifferences doesn't really know how to handle tangents properly?

_int2zero(x::Tuple) = map(_int2zero, x)
_int2zero(x::NamedTuple) = map(_int2zero, x)
_int2zero(::Integer) = ZeroTangent()
5 changes: 5 additions & 0 deletions src/to_vec.jl
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,11 @@ function to_vec(z::Complex)
return [real(z), imag(z)], Complex_from_vec
end

function to_vec(x::Integer)
Integer_from_vec(v) = x
return Bool[], Integer_from_vec
end

# Base case -- if x is already a Vector{<:Real} there's no conversion necessary.
to_vec(x::Vector{<:Real}) = (x, identity)

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65 changes: 34 additions & 31 deletions test/to_vec.jl
Original file line number Diff line number Diff line change
Expand Up @@ -67,8 +67,11 @@ function test_to_vec(x::T; check_inferred=true) where {T}
return nothing
end

myrandn(T::Type{<:Number}, args...) = randn(T, args...)
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i should probably rename this to something sensible.

robust_randn, randn_ints_ok, randn_that_doesnt_hate_ints

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I'm not sure this should have randn in the name, since the integer method doesn't really sample from a normal distribution. Perhaps something a bit more verbose like rand_number?

myrandn(T::Type{<:Integer}, args...) = rand(T[-1, 0, 2], args...)

@testset "to_vec" begin
@testset "$T" for T in (Float32, ComplexF32, Float64, ComplexF64)
@testset "$T" for T in (Int64, Float32, ComplexF32, Float64, ComplexF64)
if T == Float64
test_to_vec(1.0)
test_to_vec(1)
Expand All @@ -79,52 +82,52 @@ end
test_to_vec(T[])
test_to_vec(Vector{T}[])
test_to_vec(Matrix{T}[])
test_to_vec(randn(T, 3))
test_to_vec(randn(T, 5, 11))
test_to_vec(randn(T, 13, 17, 19))
test_to_vec(randn(T, 13, 0, 19))
test_to_vec([1.0, randn(T, 2), randn(T, 1), 2.0]; check_inferred=false)
test_to_vec([randn(T, 5, 4, 3), (5, 4, 3), 2.0]; check_inferred=false)
test_to_vec(reshape([1.0, randn(T, 5, 4, 3), randn(T, 4, 3), 2.0], 2, 2); check_inferred=false)
test_to_vec(UpperTriangular(randn(T, 13, 13)))
test_to_vec(Diagonal(randn(T, 7)))
test_to_vec(DummyType(randn(T, 2, 9)))
test_to_vec(myrandn(T, 3))
test_to_vec(myrandn(T, 5, 11))
test_to_vec(myrandn(T, 13, 17, 19))
test_to_vec(myrandn(T, 13, 0, 19))
test_to_vec([1.0, myrandn(T, 2), myrandn(T, 1), 2.0]; check_inferred=false)
test_to_vec([myrandn(T, 5, 4, 3), (5, 4, 3), 2.0]; check_inferred=false)
test_to_vec(reshape([1.0, myrandn(T, 5, 4, 3), myrandn(T, 4, 3), 2.0], 2, 2); check_inferred=false)
test_to_vec(UpperTriangular(myrandn(T, 13, 13)))
test_to_vec(Diagonal(myrandn(T, 7)))
test_to_vec(DummyType(myrandn(T, 2, 9)))
test_to_vec(SVector{2, T}(1.0, 2.0); check_inferred=false)
test_to_vec(SMatrix{2, 2, T}(1.0, 2.0, 3.0, 4.0); check_inferred=false)
test_to_vec(@view randn(T, 10)[1:4]) # SubArray -- Vector
test_to_vec(@view randn(T, 10, 2)[1:4, :]) # SubArray -- Matrix
test_to_vec(@view myrandn(T, 10)[1:4]) # SubArray -- Vector
test_to_vec(@view myrandn(T, 10, 2)[1:4, :]) # SubArray -- Matrix
test_to_vec(Base.ReshapedArray(rand(T, 3, 3), (9,), ()))

@testset "$Op" for Op in (Symmetric, Hermitian)
test_to_vec(Op(randn(T, 11, 11)))
test_to_vec(Op(myrandn(T, 11, 11)))
@testset "$uplo" for uplo in (:L, :U)
A = Op(randn(T, 11, 11), uplo)
A = Op(myrandn(T, 11, 11), uplo)
test_to_vec(A)
x_vec, back = to_vec(A)
@test back(x_vec).uplo == A.uplo
end
end

@testset "$Op" for Op in (Adjoint, Transpose)
test_to_vec(Op(randn(T, 4, 4)))
test_to_vec(Op(randn(T, 6)))
test_to_vec(Op(randn(T, 2, 5)))
test_to_vec(Op(myrandn(T, 4, 4)))
test_to_vec(Op(myrandn(T, 6)))
test_to_vec(Op(myrandn(T, 2, 5)))

# Ensure that if an `AbstractVector` is `Adjoint`ed, then the reconstructed
# version also contains an `AbstractVector`, rather than an `AbstractMatrix`
# whose 2nd dimension is of size 1.
@testset "Vector" begin
x_vec, back = to_vec(Op(randn(T, 5)))
x_vec, back = to_vec(Op(myrandn(T, 5)))
@test parent(back(x_vec)) isa AbstractVector
end
end

@testset "PermutedDimsArray" begin
test_to_vec(PermutedDimsArray(randn(T, 3, 1), (2, 1)))
test_to_vec(PermutedDimsArray(randn(T, 4, 2, 3), (3, 1, 2)))
test_to_vec(PermutedDimsArray(myrandn(T, 3, 1), (2, 1)))
test_to_vec(PermutedDimsArray(myrandn(T, 4, 2, 3), (3, 1, 2)))
test_to_vec(
PermutedDimsArray(
[randn(T, 3) for _ in 1:3, _ in 1:2, _ in 1:4], (2, 1, 3),
[myrandn(T, 3) for _ in 1:3, _ in 1:2, _ in 1:4], (2, 1, 3),
),
)
end
Expand All @@ -133,7 +136,7 @@ end
# (100, 100) is needed to test for the NaNs that can appear in the
# qr(M).T matrix
for dims in [(7, 3), (100, 100)]
M = randn(T, dims...)
M = myrandn(T, dims...)
P = M * M' + I # Positive definite matrix
test_to_vec(svd(M))
test_to_vec(cholesky(P))
Expand Down Expand Up @@ -172,25 +175,25 @@ end

@testset "Tuples" begin
test_to_vec((5, 4))
test_to_vec((5, randn(T, 5)); check_inferred = VERSION ≥ v"1.2") # broken on Julia 1.6.0, fixed on 1.6.1
test_to_vec((randn(T, 4), randn(T, 4, 3, 2), 1); check_inferred=false)
test_to_vec((5, randn(T, 4, 3, 2), UpperTriangular(randn(T, 4, 4)), 2.5); check_inferred = VERSION ≥ v"1.2") # broken on Julia 1.6.0, fixed on 1.6.1
test_to_vec(((6, 5), 3, randn(T, 3, 2, 0, 1)); check_inferred=false)
test_to_vec((DummyType(randn(T, 2, 7)), DummyType(randn(T, 3, 9))))
test_to_vec((DummyType(randn(T, 3, 2)), randn(T, 11, 8)))
test_to_vec((5, myrandn(T, 5)); check_inferred = VERSION ≥ v"1.2") # broken on Julia 1.6.0, fixed on 1.6.1
test_to_vec((myrandn(T, 4), myrandn(T, 4, 3, 2), 1); check_inferred=false)
test_to_vec((5, myrandn(T, 4, 3, 2), UpperTriangular(myrandn(T, 4, 4)), 2.5); check_inferred = VERSION ≥ v"1.2") # broken on Julia 1.6.0, fixed on 1.6.1
test_to_vec(((6, 5), 3, myrandn(T, 3, 2, 0, 1)); check_inferred=false)
test_to_vec((DummyType(myrandn(T, 2, 7)), DummyType(myrandn(T, 3, 9))))
test_to_vec((DummyType(myrandn(T, 3, 2)), myrandn(T, 11, 8)))
end
@testset "NamedTuple" begin
if T == Float64
test_to_vec((a=5, b=randn(10, 11), c=(5, 4, 3)); check_inferred = VERSION ≥ v"1.2")
else
test_to_vec((a=3 + 2im, b=randn(T, 10, 11), c=(5+im, 2-im, 1+im)); check_inferred = VERSION ≥ v"1.2")
test_to_vec((a=3 + 2im, b=myrandn(T, 10, 11), c=(5+im, 2-im, 1+im)); check_inferred = VERSION ≥ v"1.2")
end
end
@testset "Dictionary" begin
if T == Float64
test_to_vec(Dict(:a=>5, :b=>randn(10, 11), :c=>(5, 4, 3)); check_inferred=false)
else
test_to_vec(Dict(:a=>3 + 2im, :b=>randn(T, 10, 11), :c=>(5+im, 2-im, 1+im)); check_inferred=false)
test_to_vec(Dict(:a=>3 + 2im, :b=>myrandn(T, 10, 11), :c=>(5+im, 2-im, 1+im)); check_inferred=false)
end
end
end
Expand Down