Re-add test_setval! from test/turing/model.jl

Author: penelopeysm

test/test_util.jl

@@ -43,40 +43,6 @@ function test_model_ad(model, logp_manual)
     @test back(1)[1] ≈ grad
 end
 
-"""
-    test_setval!(model, chain; sample_idx = 1, chain_idx = 1)
-
-Test `setval!` on `model` and `chain`.
-
-Worth noting that this only supports models containing symbols of the forms
-`m`, `m[1]`, `m[1, 2]`, not `m[1][1]`, etc.
-"""
-function test_setval!(model, chain; sample_idx=1, chain_idx=1)
-    var_info = VarInfo(model)
-    spl = SampleFromPrior()
-    θ_old = var_info[spl]
-    DynamicPPL.setval!(var_info, chain, sample_idx, chain_idx)
-    θ_new = var_info[spl]
-    @test θ_old != θ_new
-    vals = DynamicPPL.values_as(var_info, OrderedDict)
-    iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
-    for (n, v) in mapreduce(collect, vcat, iters)
-        n = string(n)
-        if Symbol(n) ∉ keys(chain)
-            # Assume it's a group
-            chain_val = vec(
-                MCMCChains.group(chain, Symbol(n)).value[sample_idx, :, chain_idx]
-            )
-            v_true = vec(v)
-        else
-            chain_val = chain[sample_idx, n, chain_idx]
-            v_true = v
-        end
-
-        @test v_true == chain_val
-    end
-end
-
 """
     short_varinfo_name(vi::AbstractVarInfo)
 

test/varinfo.jl

@@ -130,6 +130,7 @@ DynamicPPL.getspace(::DynamicPPL.Sampler{MySAlg}) = (:s,)
         test_base!!(SimpleVarInfo(Dict()))
         test_base!!(SimpleVarInfo(DynamicPPL.VarNamedVector()))
     end
+
     @testset "flags" begin
         # Test flag setting:
         #    is_flagged, set_flag!, unset_flag!
@@ -187,6 +188,7 @@ DynamicPPL.getspace(::DynamicPPL.Sampler{MySAlg}) = (:s,)
         setgid!(vi, gid2, vn)
         @test meta.x.gids[meta.x.idcs[vn]] == Set([gid1, gid2])
     end
+
     @testset "setval! & setval_and_resample!" begin
         @model function testmodel(x)
             n = length(x)
@@ -339,6 +341,52 @@ DynamicPPL.getspace(::DynamicPPL.Sampler{MySAlg}) = (:s,)
         @test vals_prev == vi.metadata.x.vals
     end
 
+    @testset "setval! on chain" begin
+        # Define a helper function
+        """
+            test_setval!(model, chain; sample_idx = 1, chain_idx = 1)
+
+        Test `setval!` on `model` and `chain`.
+
+        Worth noting that this only supports models containing symbols of the forms
+        `m`, `m[1]`, `m[1, 2]`, not `m[1][1]`, etc.
+        """
+        function test_setval!(model, chain; sample_idx=1, chain_idx=1)
+            var_info = VarInfo(model)
+            spl = SampleFromPrior()
+            θ_old = var_info[spl]
+            DynamicPPL.setval!(var_info, chain, sample_idx, chain_idx)
+            θ_new = var_info[spl]
+            @test θ_old != θ_new
+            vals = DynamicPPL.values_as(var_info, OrderedDict)
+            iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
+            for (n, v) in mapreduce(collect, vcat, iters)
+                n = string(n)
+                if Symbol(n) ∉ keys(chain)
+                    # Assume it's a group
+                    chain_val = vec(
+                        MCMCChains.group(chain, Symbol(n)).value[sample_idx, :, chain_idx]
+                    )
+                    v_true = vec(v)
+                else
+                    chain_val = chain[sample_idx, n, chain_idx]
+                    v_true = v
+                end
+
+                @test v_true == chain_val
+            end
+        end
+
+        @testset "$model" for model in DynamicPPL.TestUtils.DEMO_MODELS
+            chain = make_chain_from_prior(model, 10)
+            # A simple way of checking that the computation is determinstic: run twice and compare.
+            res1 = generated_quantities(model, MCMCChains.get_sections(chain, :parameters))
+            res2 = generated_quantities(model, MCMCChains.get_sections(chain, :parameters))
+            @test all(res1 .== res2)
+            test_setval!(model, MCMCChains.get_sections(chain, :parameters))
+        end
+    end
+
     @testset "istrans" begin
         @model demo_constrained() = x ~ truncated(Normal(), 0, Inf)
         model = demo_constrained()