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add ForwardDiff@1 #378

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d114283
add ForwardDiff@1
penelopeysm Mar 28, 2025
8cd3f7b
Force ForwardDiff=1 in tests
penelopeysm Mar 30, 2025
bdb1eb4
Don't test Dirichlet AD at non-differentiable points
penelopeysm Mar 31, 2025
af33293
Don't run duplicate tests
penelopeysm Apr 3, 2025
55938c6
Use ForwardDiff 1.0.1, fix LKJCholesky Jacobian test
penelopeysm Apr 3, 2025
9d305d8
Remove dead code
penelopeysm Apr 3, 2025
6cf4d24
Don't force FD=1 in test suite
penelopeysm Apr 3, 2025
67fa51e
Merge branch 'main' into py/forwarddiff-1
penelopeysm Apr 14, 2025
f209df8
Add reference to ForwardDiff issue
penelopeysm Apr 14, 2025
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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "Bijectors"
uuid = "76274a88-744f-5084-9051-94815aaf08c4"
version = "0.15.6"
version = "0.15.7"

[deps]
ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
Expand Down Expand Up @@ -51,7 +51,7 @@ Distributions = "0.25.33"
DistributionsAD = "0.6"
DocStringExtensions = "0.9"
EnzymeCore = "0.8.4"
ForwardDiff = "0.10"
ForwardDiff = "0.10, 1.0.1"
Functors = "0.1, 0.2, 0.3, 0.4, 0.5"
InverseFunctions = "0.1"
IrrationalConstants = "0.1, 0.2"
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1 change: 0 additions & 1 deletion src/Bijectors.jl
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Expand Up @@ -86,7 +86,6 @@ _eps(::Type{<:Integer}) = eps(Float64)

function _clamp(x, a, b)
T = promote_type(typeof(x), typeof(a), typeof(b))
ϵ = _eps(T)
clamped_x = ifelse(x < a, convert(T, a), ifelse(x > b, convert(T, b), x))
DEBUG && _debug("x = $x, bounds = $((a, b)), clamped_x = $clamped_x")
return clamped_x
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2 changes: 1 addition & 1 deletion test/Project.toml
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Expand Up @@ -38,7 +38,7 @@ Enzyme = "0.13.12"
EnzymeTestUtils = "0.2.1"
FillArrays = "1"
FiniteDifferences = "0.11, 0.12"
ForwardDiff = "0.10.12"
ForwardDiff = "0.10, 1.0.1"
Functors = "0.1, 0.2, 0.3, 0.4, 0.5"
InverseFunctions = "0.1"
LazyArrays = "1, 2"
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16 changes: 3 additions & 13 deletions test/interface.jl
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Expand Up @@ -145,12 +145,10 @@ end
@testset "Multivariate" begin
vector_dists = [
Dirichlet(2, 3),
Dirichlet([1000 * one(Float64), eps(Float64)]),
Dirichlet([eps(Float64), 1000 * one(Float64)]),
Dirichlet([10.0, 0.1]),
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I am not sure these new tests could replace the current ones. Tests like

Dirichlet([1000 * one(Float64), eps(Float64)]),
        Dirichlet([eps(Float64), 1000 * one(Float64)]),

are aimed at the numerical stability of very extrate examples of Dirichlet distributions, i.e. one axis has a very tiny probability mass in average.

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@penelopeysm penelopeysm Apr 10, 2025 •
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It's actually the sample that's the problem. For the sample x = [1.0, 0.0], the transformed variable is y = [36.0436] which is outside of the range for which Float64 is numerically stable.

The issue comes from these lines:

Bijectors.jl/src/bijectors/simplex.jl

Lines 89 to 90 in d8d781b

@inbounds z = LogExpFunctions.logistic(y[1] - log(T(K - 1)))
@inbounds x[1] = _clamp((z - ϵ) / (one(T) - 2ϵ), 0, 1)

As y[1] tends to +Inf, z tends to 1, and the expression (z - ϵ) / (one(T) - 2ϵ) tends towards 1.0000000000000002. If that expression is greater than 1, then it gets _clamped to 1, and the derivative is set to 0.

The difference between FD 0.10 and FD 1.0 is that the new version sets the derivative to 0 if (z - ϵ) / (one(T) - 2ϵ) is greater than, or equal to, 1. And that in turn means that there is a larger range of y[1] for which the derivative gets clamped. Unfortunately, Float64 36.0436 falls into that category (35.8 would have been fine, or alternatively, BigFloat is ok up until around 175).

As far as I can tell the fact that it used to work with FD 0.10 might have been a happy accident – I wrote more about this in a comment above, but (to me) it makes sense for FD to set the derivative to 0 at the point (z - ϵ) / (one(T) - 2ϵ) == 1.

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@penelopeysm penelopeysm Apr 10, 2025 •
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I am still not fully sure how to resolve this though, which is why I haven't really come back to this PR. Obviously changing the sample fixes the tests (and the easiest way to change the sample was to change the distribution from which it was drawn), but I can't tell if there's a workaround in the code that makes it work again for (z - ϵ) / (one(T) - 2ϵ) == 1.0, or more generally for large y.

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Pinging @devmotion for your thoughts too :)

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Hmm, I'm not sure. I always had the feeling that this stick-breaking transform (explained in eg the Stan docs) can be numerically problematic. I also always thought that these eps workarounds are unsatisfying. But I'm not sure what exactly would be broken when they would be removed, maybe would be interesting to see.

Dirichlet([0.1, 10.0]),
MvNormal(randn(10), Diagonal(exp.(randn(10)))),
MvLogNormal(MvNormal(randn(10), Diagonal(exp.(randn(10))))),
Dirichlet([1000 * one(Float64), eps(Float64)]),
Dirichlet([eps(Float64), 1000 * one(Float64)]),
MvTDist(1, randn(10), Matrix(Diagonal(exp.(randn(10))))),
transformed(MvNormal(randn(10), Diagonal(exp.(randn(10))))),
transformed(MvLogNormal(MvNormal(randn(10), Diagonal(exp.(randn(10)))))),
Expand All @@ -173,15 +171,7 @@ end
# similar to what we do in test/transform.jl for Dirichlet
if dist isa Dirichlet
b = Bijectors.SimplexBijector()
# HACK(torfjelde): Calling `rand(dist)` will sometimes lead to `[0.999..., 0.0]`
# which in turn will lead to differences between `ForwardDiff.jacobian`
# and `logabsdetjac` due to how we handle the boundary values in `SimplexBijector`.
# We therefore test the realizations _on_ the boundary rather if we're near the boundary.
x = if any(rand(dist) .> 0.9999)
[0.0, 1.0][sortperm(rand(dist))]
else
rand(dist)
end
x = rand(dist)
y = b(x)
@test b(param(x)) isa TrackedArray
@test logabsdet(ForwardDiff.jacobian(b, x)[:, 1:(end - 1)])[1] ≈
Expand Down
35 changes: 7 additions & 28 deletions test/transform.jl
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Expand Up @@ -153,32 +153,10 @@ end
Dirichlet([eps(Float64), 1000 * one(Float64)]),
MvNormal(randn(10), Diagonal(exp.(randn(10)))),
MvLogNormal(MvNormal(randn(10), Diagonal(exp.(randn(10))))),
Dirichlet([1000 * one(Float64), eps(Float64)]),
Dirichlet([eps(Float64), 1000 * one(Float64)]),
]
for dist in vector_dists
if dist isa Dirichlet
single_sample_tests(dist)

# This should fail at the minute. Not sure what the correct way to test this is.

# Workaround for intermittent test failures, result of `logpdf_with_trans(dist, x, true)`
# is incorrect for `x == [0.9999999999999998, 0.0]`:
x =
if params(dist) ==
params(Dirichlet([1000 * one(Float64), eps(Float64)]))
[1.0, 0.0]
else
rand(dist)
end
# `Dirichlet` is no longer mapping between spaces of the same dimensionality,
# so the block below no longer works.
if !(dist isa Dirichlet)
logpdf_turing = logpdf_with_trans(dist, x, true)
J = ForwardDiff.jacobian(x -> link(dist, x), x)
@test logpdf(dist, x .+ ϵ) - _logabsdet(J) ≈ logpdf_turing
end

# Issue #12
stepsize = 1e10
dim = Bijectors.output_length(bijector(dist), length(dist))
Expand Down Expand Up @@ -240,14 +218,15 @@ end
@testset "uplo: $uplo" for uplo in [:L, :U]
dist = LKJCholesky(3, 1, uplo)
single_sample_tests(dist)

x = rand(dist)

inds = [
LinearIndices(size(x))[I] for I in CartesianIndices(size(x)) if
(uplo === :L && I[2] < I[1]) || (uplo === :U && I[2] > I[1])
]
J = ForwardDiff.jacobian(z -> link(dist, Cholesky(z, x.uplo, x.info)), x.UL)
# Remove columns of Jacobian that are all zero (i.e. those
# corresponding to entries above the diagonal for uplo = :U, or below
# the diagonal for uplo = :L). This slightly unscientific approach
# based on filter() is needed to handle both ForwardDiff 0.10 and 1 as
# the exact indices will differ for the two versions; see
# https://github.com/JuliaDiff/ForwardDiff.jl/issues/738.
inds = filter(i -> !all(iszero, J[:, i]), 1:size(J, 2))
J = J[:, inds]
logpdf_turing = logpdf_with_trans(dist, x, true)
@test logpdf(dist, x) - _logabsdet(J) ≈ logpdf_turing
Expand Down
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