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Add iszero(x) branches to xlogy and xlog1py #54

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Add iszero(x) branches to xlogy and xlog1py #54

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b3eff55
Add `iszero(x)` branches to `xlogy` and `xlog1py`
Jul 7, 2022
b4d8cf5
Import FiniteDifferences for tests
Jul 7, 2022
50de614
Merge branch 'JuliaStats:master' into xlogy_etc
simsurace Aug 19, 2022
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3 changes: 2 additions & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -21,9 +21,10 @@ julia = "1"

[extras]
ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["ChainRulesTestUtils", "OffsetArrays", "Random", "Test"]
test = ["ChainRulesTestUtils", "FiniteDifferences", "OffsetArrays", "Random", "Test"]
25 changes: 19 additions & 6 deletions src/chainrules.jl
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Expand Up @@ -19,9 +19,15 @@ end
function _Ω_∂_xlogy(x::Real, y::Real)
logy = log(y)
z = x * logy
Ω = iszero(x) && !isnan(y) ? zero(z) : z
∂x = logy
∂y = x / y
w = x / y
if iszero(x) && !isnan(y)
Ω = zero(z)
∂y = zero(w)
else
Ω = z
∂y = iszero(y) ? oftype(w, NaN) : w
end
∂x = iszero(y) ? oftype(logy, NaN) : logy
Comment on lines +28 to +30
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I'm not sure if these should be added, even if it makes the rule match finite differencing. NaN is usually not very useful as it infects everything. So eg the forward rule will always yield NaN for any of these conditions. See eg https://juliadiff.org/ChainRulesCore.jl/stable/maths/nondiff_points.html.

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@simsurace simsurace Jul 8, 2022 •
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This is a bit mysterious to me. On the (seemingly equivalent) branches in the rrule for logpdf of NegativeBinomial we wanted NaN. Was this something specific to Distributions.jl?

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I'm happy to change this but I'm unsure about the discrepancy to Distributions.jl

return Ω, ∂x, ∂y
end
function ChainRulesCore.frule((_, Δx, Δy), ::typeof(xlogy), x::Real, y::Real)
Expand All @@ -38,9 +44,16 @@ end
function _Ω_∂_xlog1py(x::Real, y::Real)
log1py = log1p(y)
z = x * log1py
Ω = iszero(x) && !isnan(y) ? zero(z) : z
∂x = log1py
∂y = x / (1 + y)
yp1 = 1 + y
w = x / yp1
if iszero(x) && !isnan(y)
Ω = zero(z)
∂y = zero(z)
else
Ω = z
∂y = iszero(yp1) ? oftype(w, NaN) : w
end
∂x = iszero(yp1) ? oftype(log1py, NaN) : log1py
return Ω, ∂x, ∂y
end
function ChainRulesCore.frule((_, Δx, Δy), ::typeof(xlog1py), x::Real, y::Real)
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6 changes: 6 additions & 0 deletions test/chainrules.jl
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Expand Up @@ -7,10 +7,16 @@
test_frule(xlogy, x, y)
test_rrule(xlogy, x, y)

test_frule(xlogy, x, 0.0; fdm = forward_fdm(5, 1), nans = true)
test_rrule(xlogy, x, 0.0; fdm = forward_fdm(5, 1), nans = true)

for z in (-y, y)
test_frule(xlog1py, x, z)
test_rrule(xlog1py, x, z)
end

test_frule(xlog1py, x, -1.0; fdm = forward_fdm(5, 1), nans = true)
test_rrule(xlog1py, x, -1.0; fdm = forward_fdm(5, 1), nans = true)
end

@testset "xexpx" begin
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1 change: 1 addition & 0 deletions test/runtests.jl
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Expand Up @@ -2,6 +2,7 @@ using LogExpFunctions
using ChainRulesTestUtils
using ChainRulesCore
using ChangesOfVariables
using FiniteDifferences
using InverseFunctions
using OffsetArrays

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