Re-introduce regression test

Author: penelopeysm

test/ad.jl

@@ -25,4 +25,40 @@
             end
         end
     end
+
+    @testset "Turing#2151: ReverseDiff compilation & eltype(vi, spl)" begin
+        # Failing model
+        t = 1:0.05:8
+        σ = 0.3
+        y = @. rand(sin(t) + Normal(0, σ))
+        @model function state_space(y, TT, ::Type{T}=Float64) where {T}
+            # Priors 
+            α ~ Normal(y[1], 0.001)
+            τ ~ Exponential(1)
+            η ~ filldist(Normal(0, 1), TT-1)
+            σ ~ Exponential(1)
+            # create latent variable
+            x = Vector{T}(undef, TT)
+            x[1] = α
+            for t in 2:TT
+                x[t] = x[t-1] + η[t-1] * τ
+            end
+            # measurement model
+            y ~ MvNormal(x, σ^2 * I)
+            return x
+        end
+        model = state_space(y, length(t))
+
+        # Dummy sampling algorithm for testing. The test case can only be replicated
+        # with a custom sampler, it doesn't work with SampleFromPrior()
+        struct MyAlg end
+        DynamicPPL.getspace(::DynamicPPL.Sampler{MyAlg}) = ()
+        DynamicPPL.assume(rng, ::DynamicPPL.Sampler{MyAlg}, dist, vn, vi) =
+            DynamicPPL.assume(dist, vn, vi)
+
+        # Compiling the ReverseDiff tape used to fail here spl = Sampler(MyAlg())
+        vi = VarInfo(model)
+        ldf = DynamicPPL.LogDensityFunction(vi, model, SamplingContext(spl))
+        @test LogDensityProblemsAD.ADgradient(AutoReverseDiff(; compile=true), ldf) isa Any
+    end
 end