Try #414:

docs/src/api.md

@@ -95,7 +95,9 @@ pointwise_loglikelihoods
 ```
 
 ```@docs
+WrappedDistribution
 NamedDist
+NoDist
 ```
 
 ## Testing Utilities

src/DynamicPPL.jl

@@ -105,6 +105,7 @@ export AbstractVarInfo,
     dot_tilde_assume,
     dot_tilde_observe,
     # Pseudo distributions
+    WrappedDistribution,
     NamedDist,
     NoDist,
     # Prob macros

src/distribution_wrappers.jl

@@ -2,20 +2,41 @@ using Distributions: Distributions
 using Bijectors: Bijectors
 using Distributions: Univariate, Multivariate, Matrixvariate
 
+"""
+Base type for distribution wrappers.
+"""
+abstract type WrappedDistribution{variate,support,Td<:Distribution{variate,support}} <:
+              Distribution{variate,support} end
+
+wrapped_dist_type(::Type{<:WrappedDistribution{<:Any,<:Any,Td}}) where {Td} = Td
+wrapped_dist_type(d::WrappedDistribution) = wrapped_dist_type(d)
+
+wrapped_dist(d::WrappedDistribution) = d.dist
+
+Base.length(d::WrappedDistribution{<:Multivariate}) = length(wrapped_dist(d))
+Base.size(d::WrappedDistribution{<:Multivariate}) = size(wrapped_dist(d))
+Base.eltype(::Type{T}) where {T<:WrappedDistribution} = eltype(wrapped_dist_type(T))
+Base.eltype(d::WrappedDistribution) = eltype(wrapped_dist_type(d))
+
+function Distributions.rand(rng::Random.AbstractRNG, d::WrappedDistribution)
+    return rand(rng, wrapped_dist(d))
+end
+Distributions.minimum(d::WrappedDistribution) = minimum(wrapped_dist(d))
+Distributions.maximum(d::WrappedDistribution) = maximum(wrapped_dist(d))
+
+Bijectors.bijector(d::WrappedDistribution) = bijector(wrapped_dist(d))
+
 """
 A named distribution that carries the name of the random variable with it.
 """
 struct NamedDist{variate,support,Td<:Distribution{variate,support},Tv<:VarName} <:
-       Distribution{variate,support}
+       WrappedDistribution{variate,support,Td}
     dist::Td
     name::Tv
 end
 
 NamedDist(dist::Distribution, name::Symbol) = NamedDist(dist, VarName{name}())
 
-Base.length(dist::NamedDist) = Base.length(dist.dist)
-Base.size(dist::NamedDist) = Base.size(dist.dist)
-
 Distributions.logpdf(dist::NamedDist, x::Real) = Distributions.logpdf(dist.dist, x)
 function Distributions.logpdf(dist::NamedDist, x::AbstractArray{<:Real})
     return Distributions.logpdf(dist.dist, x)
@@ -27,29 +48,27 @@ function Distributions.loglikelihood(dist::NamedDist, x::AbstractArray{<:Real})
     return Distributions.loglikelihood(dist.dist, x)
 end
 
-Bijectors.bijector(d::NamedDist) = Bijectors.bijector(d.dist)
+"""
+Wrapper around distribution `Td` that suppresses `logpdf()` calculation.
 
+Note that *SampleFromPrior* would still sample from `Td`.
+"""
 struct NoDist{variate,support,Td<:Distribution{variate,support}} <:
-       Distribution{variate,support}
+       WrappedDistribution{variate,support,Td}
     dist::Td
 end
 NoDist(dist::NamedDist) = NamedDist(NoDist(dist.dist), dist.name)
 
 nodist(dist::Distribution) = NoDist(dist)
 nodist(dists::AbstractArray) = nodist.(dists)
 
-Base.length(dist::NoDist) = Base.length(dist.dist)
-Base.size(dist::NoDist) = Base.size(dist.dist)
-
 Distributions.rand(rng::Random.AbstractRNG, d::NoDist) = rand(rng, d.dist)
 Distributions.logpdf(d::NoDist{<:Univariate}, ::Real) = 0
 Distributions.logpdf(d::NoDist{<:Multivariate}, ::AbstractVector{<:Real}) = 0
 function Distributions.logpdf(d::NoDist{<:Multivariate}, x::AbstractMatrix{<:Real})
     return zeros(Int, size(x, 2))
 end
 Distributions.logpdf(d::NoDist{<:Matrixvariate}, ::AbstractMatrix{<:Real}) = 0
-Distributions.minimum(d::NoDist) = minimum(d.dist)
-Distributions.maximum(d::NoDist) = maximum(d.dist)
 
 Bijectors.logpdf_with_trans(d::NoDist{<:Univariate}, ::Real, ::Bool) = 0
 function Bijectors.logpdf_with_trans(
@@ -67,5 +86,3 @@ function Bijectors.logpdf_with_trans(
 )
     return 0
 end
-
-Bijectors.bijector(d::NoDist) = Bijectors.bijector(d.dist)

test/context_implementations.jl

@@ -68,5 +68,42 @@
                 end
             end
         end
+
+        @testset "multivariate NoDist" begin
+            @model function genmodel()
+                x ~ NoDist(Product(fill(Uniform(-20, 20), 5)))
+                for i in eachindex(x)
+                    x[i] ~ Normal(0, 1)
+                end
+            end
+            gen_model = genmodel()
+            vi_gen = VarInfo(gen_model)
+            @test isfinite(logjoint(gen_model, vi_gen))
+            # test for bijector
+            link!(vi_gen, DynamicPPL.SampleFromPrior())
+            invlink!(vi_gen, DynamicPPL.SampleFromPrior())
+
+            # explicit model specification
+            expl_model = DynamicPPL.Model(NamedTuple()) do model, varinfo, context
+                DynamicPPL.tilde_assume!!(
+                    context,
+                    NoDist(Product(fill(Uniform(-20, 20), 5))),
+                    @varname(x),
+                    varinfo,
+                )
+                x = varinfo[@varname(x)]
+                @test x isa Vector{<:Real}
+                @test length(x) == 5
+                return (
+                    nothing,
+                    DynamicPPL.acclogp!!(varinfo, sum(logpdf.(Ref(Normal(0, 1)), x))),
+                )
+            end
+            vi_expl = VarInfo(expl_model)
+            @test isfinite(logjoint(expl_model, vi_expl))
+            # test for bijector
+            link!(vi_expl, DynamicPPL.SampleFromPrior())
+            invlink!(vi_expl, DynamicPPL.SampleFromPrior())
+        end
     end
 end

test/distribution_wrappers.jl

@@ -1,13 +1,33 @@
 @testset "distribution_wrappers.jl" begin
-    d = Normal()
-    nd = DynamicPPL.NoDist(d)
+    @testset "univariate" begin
+        d = Normal()
+        nd = DynamicPPL.NoDist(d)
 
-    # Smoke test
-    rand(nd)
+        # Smoke test
+        rand(nd)
 
-    # Actual tests
-    @test minimum(nd) == -Inf
-    @test maximum(nd) == Inf
-    @test logpdf(nd, 15.0) == 0
-    @test Bijectors.logpdf_with_trans(nd, 30, true) == 0
+        # Actual tests
+        @test minimum(nd) == -Inf
+        @test maximum(nd) == Inf
+        @test logpdf(nd, 15.0) == 0
+        @test Bijectors.logpdf_with_trans(nd, 30, true) == 0
+        @test Bijectors.bijector(nd) == Bijectors.bijector(d)
+    end
+
+    @testset "multivariate" begin
+        d = Product([Normal(), Uniform()])
+        nd = DynamicPPL.NoDist(d)
+
+        # Smoke test
+        @test length(rand(nd)) == 2
+
+        # Actual tests
+        @test length(nd) == 2
+        @test size(nd) == (2,)
+        @test minimum(nd) == [-Inf, 0.0]
+        @test maximum(nd) == [Inf, 1.0]
+        @test logpdf(nd, [15.0, 0.5]) == 0
+        @test Bijectors.logpdf_with_trans(nd, [0, 1]) == 0
+        @test Bijectors.bijector(nd) == Bijectors.bijector(d)
+    end
 end