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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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160 changes: 50 additions & 110 deletions test/test_utils.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,10 @@ function gradient(f, ::Val{:FiniteDiff}, args)
return first(FiniteDifferences.grad(FDM, f, args))
end

function compare_gradient(f, ::Val{:FiniteDiff}, args)
@test_nowarn gradient(f, :FiniteDiff, args)
end

function compare_gradient(f, AD::Symbol, args)
grad_AD = gradient(f, AD, args)
grad_FD = gradient(f, :FiniteDiff, args)
Expand All @@ -88,7 +92,7 @@ testdiagfunction(k::MOKernel, A, B) = sum(kernelmatrix_diag(k, A, B))
function test_ADs(
kernelfunction, args=nothing; ADs=[:Zygote, :ForwardDiff, :ReverseDiff], dims=[3, 3]
)
test_fd = test_FiniteDiff(kernelfunction, args, dims)
test_fd = test_AD(:FiniteDiff, kernelfunction, args, dims)
if !test_fd.anynonpass
for AD in ADs
test_AD(AD, kernelfunction, args, dims)
Expand All @@ -100,7 +104,7 @@ 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 All @@ -114,70 +118,6 @@ function test_ADs(
end
end

function test_FiniteDiff(kernelfunction, args=nothing, dims=[3, 3])
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This was doing exactly the same as test_AD (except for testdiagfunction(kernelfunction(p), A, dim) vs testdiagfunction(kernelfunction(p), A, B, dim), which I've now added to test_AD). The only difference was calling compare_gradient vs the @test_warn, so I simply added a compare_gradient(f, ::Val{:FiniteDiff}, args) so we can avoid all this code duplication.

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Very nice.

# Init arguments :
k = if args === nothing
kernelfunction()
else
kernelfunction(args)
end
rng = MersenneTwister(42)
@testset "FiniteDifferences" begin
if k isa SimpleKernel
for d in log.([eps(), rand(rng)])
@test_nowarn gradient(:FiniteDiff, [d]) do x
kappa(k, exp(first(x)))
end
end
end
## Testing Kernel Functions
x = rand(rng, dims[1])
y = rand(rng, dims[1])
@test_nowarn gradient(:FiniteDiff, x) do x
k(x, y)
end
if !(args === nothing)
@test_nowarn gradient(:FiniteDiff, 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
@test_nowarn gradient(:FiniteDiff, A) do a
testfunction(k, a, dim)
end
@test_nowarn gradient(:FiniteDiff, A) do a
testfunction(k, a, B, dim)
end
@test_nowarn gradient(:FiniteDiff, B) do b
testfunction(k, A, b, dim)
end
if !(args === nothing)
@test_nowarn gradient(:FiniteDiff, args) do p
testfunction(kernelfunction(p), A, B, dim)
end
end

@test_nowarn gradient(:FiniteDiff, A) do a
testdiagfunction(k, a, dim)
end
@test_nowarn gradient(:FiniteDiff, A) do a
testdiagfunction(k, a, B, dim)
end
@test_nowarn gradient(:FiniteDiff, B) do b
testdiagfunction(k, A, b, dim)
end
if args !== nothing
@test_nowarn gradient(:FiniteDiff, args) do p
testdiagfunction(kernelfunction(p), A, B, dim)
end
end
end
end
end

function test_FiniteDiff(k::MOKernel, dims=(in=3, out=2, obs=3))
rng = MersenneTwister(42)
@testset "FiniteDifferences" begin
Expand Down Expand Up @@ -224,68 +164,68 @@ 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])
compare_gradient(AD, x) do x
k(x, y)
end
compare_gradient(AD, B) do b
testfunction(k, A, b, dim)
compare_gradient(AD, y) do y
k(x, y)
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...)
@testset "$(_testfn)" for _testfn in (testfunction, testdiagfunction)
for dim in 1:2
compare_gradient(AD, A) do a
_testfn(k, a, dim)
end
compare_gradient(AD, A) do a
_testfn(k, a, B, dim)
end
compare_gradient(AD, B) do b
_testfn(k, A, b, dim)
end
if !(args === nothing)
@testset "hyperparameters" begin
compare_gradient(AD, args) do p
_testfn(kernelfunction(p), A, dim)
end
compare_gradient(AD, args) do p
_testfn(kernelfunction(p), A, B, dim)
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added missing case

end
end
end
end
end
end
end # kernel matrices
end
end

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], # explicitly pass ADs to exclude :Zygote
)
@test_broken "test_AD of chain transform is currently broken in Zygote, see GitHub issue #263"
end