Trying to organise the tests, part 1 #405
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Bijectors.jl documentation for PR #405 is available at: |
penelopeysm
commented
Aug 13, 2025
Comment on lines
-112
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| function test_bijector_parameter_gradient(b::Bijectors.Transform, x, y=b(x)) | ||
| args, re = Functors.functor(b) | ||
| recon(k, param) = re(merge(args, NamedTuple{(k,)}((param,)))) | ||
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| # Compute the gradient wrt. one argument at the time. | ||
| for (k, v) in pairs(args) | ||
| test_ad(p -> sum(transform(recon(k, p), x)), v) | ||
| test_ad(p -> logabsdetjac(recon(k, p), x), v) | ||
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| if Bijectors.isinvertible(b) | ||
| test_ad(p -> sum(transform(inv(recon(k, p)), y)), v) | ||
| test_ad(p -> logabsdetjac(inv(recon(k, p)), y), v) | ||
| end | ||
| end | ||
| end |
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this function was not used anywhere
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actually i decided to just fix even more things in #406 so this PR probably doesn't need to be separately reviewed, sorry |
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In the Bijectors test suite there are two kinds of AD tests:
Check that the manual implementation of
Bijectors.logabsdetjacreturns the same numerical result as an AD backend (I think we should ideally like to always use FiniteDifferences, but in some cases we use ForwardDiff). This usually involves a manual call toForwardDiff.jacobianor similar.Check that AD backends can differentiate through Bijectors code. This uses the
test_adfunction.Generally these fall under the 'interface' / 'AD' test suite respectively.
But there are a couple of stragglers where the
test_adfunction is being called in the interface tests. This PR gets rid of the exceptions.This PR also streamlines CI a bit:
minandltsseparately (our min compat is 1.10.8 and lts is 1.10.10 so it's really no different).