Merge remote-tracking branch 'origin/main' into breaking

mhauru · mhauru · commit 262d732c9009 · 2025-10-22T16:45:17.000+01:00
diff --git a/HISTORY.md b/HISTORY.md
@@ -2,6 +2,11 @@
 
 ## 0.39.0
 
+## 0.38.1
+
+Added `from_linked_vec_transform` and `from_vec_transform` methods for `ProductNamedTupleDistribution`.
+This patch allows sampling from `ProductNamedTupleDistribution` in DynamicPPL models.
+
 ## 0.38.0
 
 ### Breaking changes
diff --git a/Project.toml b/Project.toml
@@ -51,7 +51,7 @@ AbstractMCMC = "5"
 AbstractPPL = "0.13.1"
 Accessors = "0.1"
 BangBang = "0.4.1"
-Bijectors = "0.13.18, 0.14, 0.15"
+Bijectors = "0.15.11"
 ChainRulesCore = "1"
 Chairmarks = "1.3.1"
 Compat = "4"
diff --git a/src/contexts/init.jl b/src/contexts/init.jl
@@ -61,7 +61,7 @@ struct InitFromUniform{T<:AbstractFloat} <: AbstractInitStrategy
 end
 function init(rng::Random.AbstractRNG, ::VarName, dist::Distribution, u::InitFromUniform)
     b = Bijectors.bijector(dist)
-    sz = Bijectors.output_size(b, size(dist))
+    sz = Bijectors.output_size(b, dist)
     y = u.lower .+ ((u.upper - u.lower) .* rand(rng, sz...))
     b_inv = Bijectors.inverse(b)
     x = b_inv(y)
@@ -166,12 +166,11 @@ function tilde_assume!!(
     # is_transformed(vi) returns true if vi is nonempty and all variables in vi
     # are linked.
     insert_transformed_value = in_varinfo ? is_transformed(vi, vn) : is_transformed(vi)
-    f = if insert_transformed_value
-        link_transform(dist)
+    y, logjac = if insert_transformed_value
+        with_logabsdet_jacobian(link_transform(dist), x)
     else
-        identity
+        x, zero(LogProbType)
     end
-    y, logjac = with_logabsdet_jacobian(f, x)
     # Add the new value to the VarInfo. `push!!` errors if the value already
     # exists, hence the need for setindex!!.
     if in_varinfo
diff --git a/src/utils.jl b/src/utils.jl
@@ -354,7 +354,6 @@ Return the transformation from the vector representation of a realization of siz
 original representation.
 """
 from_vec_transform_for_size(sz::Tuple) = ReshapeTransform(sz)
-# TODO(mhauru) Is the below used? If not, this function can be removed.
 from_vec_transform_for_size(::Tuple{<:Any}) = identity
 
 """
@@ -367,6 +366,60 @@ from_vec_transform(dist::Distribution) = from_vec_transform_for_size(size(dist))
 from_vec_transform(::UnivariateDistribution) = UnwrapSingletonTransform()
 from_vec_transform(dist::LKJCholesky) = ToChol(dist.uplo) ∘ ReshapeTransform(size(dist))
 
+struct ProductNamedTupleUnvecTransform{names,T<:NamedTuple{names}}
+    dists::T
+    # The `i`-th input range corresponds to the segment of the input vector
+    # that belongs to the `i`-th distribution.
+    input_ranges::Vector{UnitRange}
+    function ProductNamedTupleUnvecTransform(
+        d::Distributions.ProductNamedTupleDistribution{names}
+    ) where {names}
+        offset = 1
+        input_ranges = UnitRange[]
+        for name in names
+            this_dist = d.dists[name]
+            this_name_size = _input_length(from_vec_transform(this_dist))
+            push!(input_ranges, offset:(offset + this_name_size - 1))
+            offset += this_name_size
+        end
+        return new{names,typeof(d.dists)}(d.dists, input_ranges)
+    end
+end
+
+@generated function (trf::ProductNamedTupleUnvecTransform{names})(
+    x::AbstractVector
+) where {names}
+    expr = Expr(:tuple)
+    for (i, name) in enumerate(names)
+        push!(
+            expr.args,
+            :($name = from_vec_transform(trf.dists.$name)(x[trf.input_ranges[$i]])),
+        )
+    end
+    return expr
+end
+
+function from_vec_transform(dist::Distributions.ProductNamedTupleDistribution)
+    return ProductNamedTupleUnvecTransform(dist)
+end
+function Bijectors.with_logabsdet_jacobian(f::ProductNamedTupleUnvecTransform, x)
+    return f(x), zero(LogProbType)
+end
+
+# This function returns the length of the vector that the function from_vec_transform
+# expects. This helps us determine which segment of a concatenated vector belongs to which
+# variable.
+_input_length(from_vec_trfm::UnwrapSingletonTransform) = 1
+_input_length(from_vec_trfm::ReshapeTransform) = prod(from_vec_trfm.output_size)
+function _input_length(trfm::ProductNamedTupleUnvecTransform)
+    return sum(_input_length ∘ from_vec_transform, values(trfm.dists))
+end
+function _input_length(
+    c::ComposedFunction{<:DynamicPPL.ToChol,<:DynamicPPL.ReshapeTransform}
+)
+    return _input_length(c.inner)
+end
+
 """
     from_vec_transform(f, size::Tuple)
 
@@ -405,7 +458,9 @@ function from_linked_vec_transform(dist::UnivariateDistribution)
     sz = Bijectors.output_size(f_combined, size(dist))
     return UnwrapSingletonTransform(sz) ∘ f_combined
 end
-
+function from_linked_vec_transform(dist::Distributions.ProductNamedTupleDistribution)
+    return invlink_transform(dist)
+end
 # Specializations that circumvent the `from_vec_transform` machinery.
 function from_linked_vec_transform(dist::LKJCholesky)
     return inverse(Bijectors.VecCholeskyBijector(dist.uplo))
diff --git a/test/utils.jl b/test/utils.jl
@@ -1,3 +1,21 @@
+module DynamicPPLUtilsTests
+
+using Bijectors: Bijectors
+using Distributions
+using DynamicPPL
+using LinearAlgebra: LinearAlgebra
+using Test
+
+isapprox_nested(a::Number, b::Number; kwargs...) = isapprox(a, b; kwargs...)
+isapprox_nested(a::AbstractArray, b::AbstractArray; kwargs...) = isapprox(a, b; kwargs...)
+function isapprox_nested(a::LinearAlgebra.Cholesky, b::LinearAlgebra.Cholesky; kwargs...)
+    return isapprox(a.U, b.U; kwargs...) && isapprox(a.L, b.L; kwargs...)
+end
+function isapprox_nested(a::NamedTuple, b::NamedTuple; kwargs...)
+    keys(a) == keys(b) || return false
+    return all(k -> isapprox_nested(a[k], b[k]; kwargs...), keys(a))
+end
+
 @testset "utils.jl" begin
     @testset "addlogprob!" begin
         @model function testmodel()
@@ -31,35 +49,130 @@
         end
     end
 
+    @testset "transformations" begin
+        function test_transformation(
+            dist::Distribution; test_bijector_type_stability::Bool=true
+        )
+            unlinked = rand(dist)
+            unlinked_vec = DynamicPPL.tovec(unlinked)
+            @test unlinked_vec isa AbstractVector
+
+            from_vec_trfm = DynamicPPL.from_vec_transform(dist)
+            unlinked_again, logjac = Bijectors.with_logabsdet_jacobian(
+                from_vec_trfm, unlinked_vec
+            )
+            @test isapprox_nested(unlinked, unlinked_again)
+            @test iszero(logjac)
+            # Type stability
+            @inferred DynamicPPL.from_vec_transform(dist)
+            @inferred Bijectors.with_logabsdet_jacobian(from_vec_trfm, unlinked_vec)
+
+            # Typically the same as `bijector(dist)`, but technically a different
+            # function
+            b = DynamicPPL.link_transform(dist)
+            @test (b(unlinked); true)
+            linked, logjac = Bijectors.with_logabsdet_jacobian(b, unlinked)
+            @test logjac isa Real
+
+            binv = DynamicPPL.invlink_transform(dist)
+            unlinked_again, logjac_inv = Bijectors.with_logabsdet_jacobian(binv, linked)
+            @test isapprox_nested(unlinked, unlinked_again)
+            @test isapprox(logjac, -logjac_inv)
+
+            linked_vec = DynamicPPL.tovec(linked)
+            @test linked_vec isa AbstractVector
+            from_linked_vec_trfm = DynamicPPL.from_linked_vec_transform(dist)
+            unlinked_again_again = from_linked_vec_trfm(linked_vec)
+            @test isapprox_nested(unlinked, unlinked_again_again)
+
+            # Sometimes the bijector itself is not type stable. In this case there is not
+            # much we can do in DynamicPPL except skip these tests (it has to be fixed
+            # upstream in Bijectors.)
+            if test_bijector_type_stability
+                @inferred DynamicPPL.from_linked_vec_transform(dist)
+                @inferred Bijectors.with_logabsdet_jacobian(
+                    from_linked_vec_trfm, linked_vec
+                )
+            end
+
+            # Create a model and check that we can evaluate it with both unlinked and linked
+            # VarInfo. This relies on the transformations working correctly so is more of an
+            # 'end to end' test
+            @model test() = x ~ dist
+            model = test()
+            vi_unlinked = VarInfo(model)
+            vi_linked = DynamicPPL.link!!(VarInfo(model), model)
+            @test (DynamicPPL.evaluate!!(model, vi_unlinked); true)
+            @test (DynamicPPL.evaluate!!(model, vi_linked); true)
+            model_init = DynamicPPL.setleafcontext(model, DynamicPPL.InitContext())
+            @test (DynamicPPL.evaluate!!(model_init, vi_unlinked); true)
+            @test (DynamicPPL.evaluate!!(model_init, vi_linked); true)
+        end
+
+        # Unconstrained univariate
+        test_transformation(Normal())
+        # Constrained univariate
+        test_transformation(LogNormal())
+        test_transformation(truncated(Normal(); lower=0))
+        test_transformation(Exponential(1.0))
+        test_transformation(Uniform(-2, 2))
+        test_transformation(Beta(2, 2))
+        test_transformation(InverseGamma(2, 3))
+        # Discrete univariate
+        test_transformation(Poisson(3))
+        test_transformation(Binomial(10, 0.5))
+        # Multivariate
+        test_transformation(MvNormal(zeros(3), LinearAlgebra.I))
+        test_transformation(
+            product_distribution([Normal(), LogNormal()]);
+            test_bijector_type_stability=false,
+        )
+        test_transformation(product_distribution([LogNormal(), LogNormal()]))
+        # Matrixvariate
+        test_transformation(LKJ(3, 0.5))
+        test_transformation(Wishart(7, [1.0 0.0; 0.0 1.0]))
+        # This is a pathological example: the linked representation is a matrix
+        test_transformation(product_distribution(fill(Dirichlet(ones(4)), 2, 3)))
+        # Cholesky
+        test_transformation(LKJCholesky(3, 0.5))
+        # ProductNamedTupleDistribution
+        d = product_distribution((a=Normal(), b=LogNormal()))
+        test_transformation(d)
+        d_nested = product_distribution((x=LKJCholesky(2, 0.5), y=d))
+        test_transformation(d_nested)
+    end
+
     @testset "getargs_dottilde" begin
         # Some things that are not expressions.
-        @test getargs_dottilde(:x) === nothing
-        @test getargs_dottilde(1.0) === nothing
-        @test getargs_dottilde([1.0, 2.0, 4.0]) === nothing
+        @test DynamicPPL.getargs_dottilde(:x) === nothing
+        @test DynamicPPL.getargs_dottilde(1.0) === nothing
+        @test DynamicPPL.getargs_dottilde([1.0, 2.0, 4.0]) === nothing
 
         # Some expressions.
-        @test getargs_dottilde(:(x ~ Normal(μ, σ))) === nothing
-        @test getargs_dottilde(:((.~)(x, Normal(μ, σ)))) == (:x, :(Normal(μ, σ)))
-        @test getargs_dottilde(:((~).(x, Normal(μ, σ)))) == (:x, :(Normal(μ, σ)))
-        @test getargs_dottilde(:(x .~ Normal(μ, σ))) == (:x, :(Normal(μ, σ)))
-        @test getargs_dottilde(:(@. x ~ Normal(μ, σ))) === nothing
-        @test getargs_dottilde(:(@. x ~ Normal(μ, $(Expr(:$, :(sqrt(v))))))) === nothing
-        @test getargs_dottilde(:(@~ Normal.(μ, σ))) === nothing
+        @test DynamicPPL.getargs_dottilde(:(x ~ Normal(μ, σ))) === nothing
+        @test DynamicPPL.getargs_dottilde(:((.~)(x, Normal(μ, σ)))) == (:x, :(Normal(μ, σ)))
+        @test DynamicPPL.getargs_dottilde(:((~).(x, Normal(μ, σ)))) == (:x, :(Normal(μ, σ)))
+        @test DynamicPPL.getargs_dottilde(:(x .~ Normal(μ, σ))) == (:x, :(Normal(μ, σ)))
+        @test DynamicPPL.getargs_dottilde(:(@. x ~ Normal(μ, σ))) === nothing
+        @test DynamicPPL.getargs_dottilde(:(@. x ~ Normal(μ, $(Expr(:$, :(sqrt(v))))))) ===
+            nothing
+        @test DynamicPPL.getargs_dottilde(:(@~ Normal.(μ, σ))) === nothing
     end
 
     @testset "getargs_tilde" begin
         # Some things that are not expressions.
-        @test getargs_tilde(:x) === nothing
-        @test getargs_tilde(1.0) === nothing
-        @test getargs_tilde([1.0, 2.0, 4.0]) === nothing
+        @test DynamicPPL.getargs_tilde(:x) === nothing
+        @test DynamicPPL.getargs_tilde(1.0) === nothing
+        @test DynamicPPL.getargs_tilde([1.0, 2.0, 4.0]) === nothing
 
         # Some expressions.
-        @test getargs_tilde(:(x ~ Normal(μ, σ))) == (:x, :(Normal(μ, σ)))
-        @test getargs_tilde(:((.~)(x, Normal(μ, σ)))) === nothing
-        @test getargs_tilde(:((~).(x, Normal(μ, σ)))) === nothing
-        @test getargs_tilde(:(@. x ~ Normal(μ, σ))) === nothing
-        @test getargs_tilde(:(@. x ~ Normal(μ, $(Expr(:$, :(sqrt(v))))))) === nothing
-        @test getargs_tilde(:(@~ Normal.(μ, σ))) === nothing
+        @test DynamicPPL.getargs_tilde(:(x ~ Normal(μ, σ))) == (:x, :(Normal(μ, σ)))
+        @test DynamicPPL.getargs_tilde(:((.~)(x, Normal(μ, σ)))) === nothing
+        @test DynamicPPL.getargs_tilde(:((~).(x, Normal(μ, σ)))) === nothing
+        @test DynamicPPL.getargs_tilde(:(@. x ~ Normal(μ, σ))) === nothing
+        @test DynamicPPL.getargs_tilde(:(@. x ~ Normal(μ, $(Expr(:$, :(sqrt(v))))))) ===
+            nothing
+        @test DynamicPPL.getargs_tilde(:(@~ Normal.(μ, σ))) === nothing
     end
 
     @testset "tovec" begin
@@ -97,3 +210,5 @@
         @test DynamicPPL.group_varnames_by_symbol(vns_tuple) == vns_nt
     end
 end
+
+end
