Add missing tovec methods for NamedTuple and Tuple.

HISTORY.md

@@ -1,5 +1,10 @@
 # DynamicPPL Changelog
 
+## 0.36.9
+
+Fixed a failure when sampling from `ProductNamedTupleDistribution` due to
+missing `tovec` methods for `NamedTuple` and `Tuple`.
+
 ## 0.36.8
 
 Made `LogDensityFunction` a subtype of `AbstractMCMC.AbstractModel`.

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.36.8"
+version = "0.36.9"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/utils.jl

@@ -443,6 +443,8 @@ to_linked_vec_transform(x) = inverse(from_linked_vec_transform(x))
 # or fix `tovec` to flatten the full matrix instead of using `Bijectors.triu_to_vec`.
 tovec(x::Real) = [x]
 tovec(x::AbstractArray) = vec(x)
+tovec(t::Tuple) = mapreduce(tovec, vcat, t)
+tovec(nt::NamedTuple) = mapreduce(tovec, vcat, values(nt))
 tovec(C::Cholesky) = tovec(Matrix(C.UL))
 
 """

test/model.jl

@@ -617,4 +617,15 @@ const GDEMO_DEFAULT = DynamicPPL.TestUtils.demo_assume_observe_literal()
             ]) ≈ 1.0 * xs_test[2] rtol = 0.1
         end
     end
+
+    @testset "ProductNamedTupleDistribution sampling" begin
+        priors = (a=Normal(), b=Normal())
+        d = product_distribution(priors)
+        @model function sample_nt(priors_dist)
+            x ~ priors_dist
+            return x
+        end
+        model = sample_nt(d)
+        @test model() isa NamedTuple
+    end
 end

test/utils.jl

@@ -47,6 +47,12 @@
         dist = LKJCholesky(2, 1)
         x = rand(dist)
         @test DynamicPPL.tovec(x) == vec(x.UL)
+
+        nt = (a=[1, 2], b=3.0)
+        @test DynamicPPL.tovec(nt) == [1, 2, 3.0]
+
+        t = (2.0, [3.0, 4.0])
+        @test DynamicPPL.tovec(t) == [2.0, 3.0, 4.0]
     end
 
     @testset "unique_syms" begin