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Fix exponentiation for NaNMath.pow #717

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merged 8 commits into from
Nov 8, 2024
Merged

Fix exponentiation for NaNMath.pow #717

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f473514
fix NaNMath exponentiation
jClugstor Nov 1, 2024
fd2c647
reuse code
jClugstor Nov 1, 2024
c537287
fix
jClugstor Nov 1, 2024
9864d7f
add tests
jClugstor Nov 1, 2024
23b7351
Update src/dual.jl
jClugstor Nov 4, 2024
0f72cf9
import NaNMath
jClugstor Nov 4, 2024
3fbc475
oops, no begin
jClugstor Nov 4, 2024
db4d9f9
Update test/GradientTest.jl
jClugstor Nov 4, 2024
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6 changes: 3 additions & 3 deletions src/dual.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -552,7 +552,7 @@ end
# exponentiation #
#----------------#

for f in (:(Base.:^), :(NaNMath.pow))
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for (f, log) in ((:(Base.:^), :(Base.log)), (:(NaNMath.pow), :(NaNMath.log)))
@eval begin
@define_binary_dual_op(
$f,
Expand All @@ -565,7 +565,7 @@ for f in (:(Base.:^), :(NaNMath.pow))
elseif iszero(vx) && vy > 0
logval = zero(vx)
else
logval = expv * log(vx)
logval = expv * ($log)(vx)
end
new_partials = _mul_partials(partials(x), partials(y), powval, logval)
return Dual{Txy}(expv, new_partials)
Expand All @@ -583,7 +583,7 @@ for f in (:(Base.:^), :(NaNMath.pow))
begin
v = value(y)
expv = ($f)(x, v)
deriv = (iszero(x) && v > 0) ? zero(expv) : expv*log(x)
deriv = (iszero(x) && v > 0) ? zero(expv) : expv*($log)(x)
return Dual{Ty}(expv, deriv * partials(y))
end
)
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12 changes: 12 additions & 0 deletions test/DerivativeTest.jl
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
module DerivativeTest

import Calculus
import NaNMath

using Test
using Random
Expand Down Expand Up @@ -93,6 +94,17 @@ end
@test (x -> ForwardDiff.derivative(y -> x^y, 1.5))(0.0) === 0.0
end

@testset "exponentiation with NaNMath" begin
@test isnan(ForwardDiff.derivative(x -> NaNMath.pow(NaN, x), 1.0))
@test isnan(ForwardDiff.derivative(x -> NaNMath.pow(x,NaN), 1.0))
@test !isnan(ForwardDiff.derivative(x -> NaNMath.pow(1.0, x),1.0))
@test isnan(ForwardDiff.derivative(x -> NaNMath.pow(x,0.5), -1.0))

@test isnan(ForwardDiff.derivative(x -> x^NaN, 2.0))
@test ForwardDiff.derivative(x -> x^2.0,2.0) == 4.0
@test_throws DomainError ForwardDiff.derivative(x -> x^0.5, -1.0)
end

@testset "dimension error for derivative" begin
@test_throws DimensionMismatch ForwardDiff.derivative(sum, fill(2pi, 3))
end
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11 changes: 11 additions & 0 deletions test/GradientTest.jl
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
module GradientTest

import Calculus
import NaNMath

using Test
using LinearAlgebra
Expand Down Expand Up @@ -200,6 +201,16 @@ end
@test ForwardDiff.gradient(L -> logdet(L), Matrix(L)) ≈ [1.0 -1.3333333333333337; 0.0 1.666666666666667]
end

@testset "gradient for exponential with NaNMath" begin
@test isnan(ForwardDiff.gradient(x -> NaNMath.pow(x[1],x[1]), [NaN, 1.0])[1])
@test ForwardDiff.gradient(x -> NaNMath.pow(x[1], x[2]), [1.0, 1.0]) == [1.0, 0.0]
@test isnan(ForwardDiff.gradient((x) -> NaNMath.pow(x[1], x[2]), [-1.0, 0.5])[1])

@test isnan(ForwardDiff.gradient(x -> x[1]^x[2], [NaN, 1.0])[1])
@test ForwardDiff.gradient(x -> x[1]^x[2], [1.0, 1.0]) == [1.0, 0.0]
@test_throws DomainError ForwardDiff.gradient(x -> x[1]^x[2], [-1.0, 0.5])
end

@testset "branches in mul!" begin
a, b = rand(3,3), rand(3,3)

Expand Down
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