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Zygote AD failure workarounds & test cleanup #414

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merged 10 commits into from
Dec 18, 2021
Merged

Zygote AD failure workarounds & test cleanup #414

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7ccfbef
revert test_utils workaround for broken Zygote
st-- Dec 9, 2021
313535e
finer-grained testsets
st-- Dec 16, 2021
11b2bcd
disable broken Zygote AD test for ChainTransform
st-- Dec 18, 2021
feb5b5c
Apply suggestions from code review
st-- Dec 18, 2021
919e445
add missing test cases to test_AD
st-- Dec 18, 2021
7d38977
replace test_FiniteDiff with test_AD(..., :FiniteDiff, ...)
st-- Dec 18, 2021
a4ad008
more explicit test broken
st-- Dec 18, 2021
51c9250
Merge branch 'st/zygote_again' of github.com:JuliaGaussianProcesses/K…
st-- Dec 18, 2021
821503c
remove more duplication
st-- Dec 18, 2021
8b1cddc
Update test/test_utils.jl
st-- Dec 18, 2021
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100 changes: 56 additions & 44 deletions test/test_utils.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -100,7 +100,7 @@ function check_zygote_type_stability(f, args...; ctx=Zygote.Context())
@inferred f(args...)
@inferred Zygote._pullback(ctx, f, args...)
out, pb = Zygote._pullback(ctx, f, args...)
@test_throws ErrorException @inferred pb(out)
@inferred pb(out)
end

function test_ADs(
Expand Down Expand Up @@ -224,65 +224,77 @@ end

function test_AD(AD::Symbol, kernelfunction, args=nothing, dims=[3, 3])
@testset "$(AD)" begin
# Test kappa function
k = if args === nothing
kernelfunction()
else
kernelfunction(args)
end
rng = MersenneTwister(42)

if k isa SimpleKernel
for d in log.([eps(), rand(rng)])
compare_gradient(AD, [d]) do x
kappa(k, exp(x[1]))
@testset "kappa function" begin
for d in log.([eps(), rand(rng)])
compare_gradient(AD, [d]) do x
kappa(k, exp(x[1]))
end
end
end
end
# Testing kernel evaluations
x = rand(rng, dims[1])
y = rand(rng, dims[1])
compare_gradient(AD, x) do x
k(x, y)
end
compare_gradient(AD, y) do y
k(x, y)
end
if !(args === nothing)
compare_gradient(AD, args) do p
kernelfunction(p)(x, y)
end
end
# Testing kernel matrices
A = rand(rng, dims...)
B = rand(rng, dims...)
for dim in 1:2
compare_gradient(AD, A) do a
testfunction(k, a, dim)
end
compare_gradient(AD, A) do a
testfunction(k, a, B, dim)

@testset "kernel evaluations" begin
x = rand(rng, dims[1])
y = rand(rng, dims[1])
@testset "first argument" begin
compare_gradient(AD, x) do x
k(x, y)
end
end
compare_gradient(AD, B) do b
testfunction(k, A, b, dim)
@testset "second argument" begin
compare_gradient(AD, y) do y
k(x, y)
end
end
if !(args === nothing)
compare_gradient(AD, args) do p
testfunction(kernelfunction(p), A, dim)
@testset "hyperparameters" begin
compare_gradient(AD, args) do p
kernelfunction(p)(x, y)
end
end
end
end

compare_gradient(AD, A) do a
testdiagfunction(k, a, dim)
end
compare_gradient(AD, A) do a
testdiagfunction(k, a, B, dim)
end
compare_gradient(AD, B) do b
testdiagfunction(k, A, b, dim)
end
if args !== nothing
compare_gradient(AD, args) do p
testdiagfunction(kernelfunction(p), A, dim)
Comment on lines -274 to -285
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this was doing exactly the same for testdiagfunction as the code above for testfunction, so I've unified it with a for loop over the two functions.

@testset "kernel matrices" begin
A = rand(rng, dims...)
B = rand(rng, dims...)
for dim in 1:2
compare_gradient(AD, A) do a
testfunction(k, a, dim)
end
compare_gradient(AD, A) do a
testfunction(k, a, B, dim)
end
compare_gradient(AD, B) do b
testfunction(k, A, b, dim)
end
if !(args === nothing)
compare_gradient(AD, args) do p
testfunction(kernelfunction(p), A, dim)
end
end

compare_gradient(AD, A) do a
testdiagfunction(k, a, dim)
end
compare_gradient(AD, A) do a
testdiagfunction(k, a, B, dim)
end
compare_gradient(AD, B) do b
testdiagfunction(k, A, b, dim)
end
if args !== nothing
compare_gradient(AD, args) do p
testdiagfunction(kernelfunction(p), A, dim)
end
end
end
end
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@rossviljoen doing the changes above I spotted that you overloaded test_AD() for MOKernel - this worries me: it seems like all the "fallback" AD tests above are not run. It also seems to suggest that we can't substitute an MOKernel where we need an arbitrary Kernel (which would I believe violate Liskov substitution principle). Could you comment pls? thanks:)

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I don't think that was me?

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Ah, apologies! Now I'm not even sure anymore why I assumed it was you 😔 @willtebbutt / @thomasgudjonwright ?

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Hmm don't think it was me. A lot of this code was @theogf I think?

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I did the original compare gradient but someone else extended it to MOKernel

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Wasn't me! Could always look back at the commit history :)

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Should've checked git blame in the first place... it was #263 (@david-vicente).

This to me seems like a case where overloading the same method isn't really the right thing to do (because it suggests they do the same thing - it's a kernel AD check in either case - but they don't actually seem to check the same things)... now that i've got y'all in a thread, what is your opinion on these ?

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Separated out into #416

Expand Down
4 changes: 3 additions & 1 deletion test/transform/chaintransform.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,8 @@
@test repr(tp ∘ tf) == "Chain of 2 transforms:\n\t - $(tf) |> $(tp)"
test_ADs(
x -> SEKernel() ∘ (ScaleTransform(exp(x[1])) ∘ ARDTransform(exp.(x[2:4]))),
randn(rng, 4),
randn(rng, 4);
ADs=[:ForwardDiff, :ReverseDiff]
st-- marked this conversation as resolved.
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)
@test_broken "test_AD of chain transform is currently broken in Zygote"
end