Add multi-variate enumeration test case.

src/inference/enumerative.jl

@@ -107,7 +107,7 @@ function expand_grid_spec_to_values(
             end
             return vs
         end
-        return ((addr, v) for v in Iterators.product(vals...))
+        return ((addr, collect(v)) for v in Iterators.product(vals...))
     else
         error("Support must be :discrete or :continuous")
     end

test/inference/enumerative.jl

@@ -77,4 +77,60 @@
     @test map_trace[(:coeff, 1)] == 0.1
     @test map_trace[(:coeff, 2)] == -0.5
 
+    # 2D mixture of normals
+    @gen function mixture_model()
+        sign ~ bernoulli(0.5)
+        mu = sign ? fill(0.5, 2) : fill(-0.5, 2)
+        z ~ broadcasted_normal(mu, ones(2))
+    end
+
+    # test construction of grid with 2D random variable
+    grid = choice_vol_grid(
+        (:sign, [false, true]),
+        (:z, (-2.0:0.1:2.0, -2.0:0.1:2.0), :continuous, Val(2)),
+        anchor = :left
+    )
+
+    @test size(grid) == (2, 40, 40)
+    @test length(grid) == 3200
+
+    choices, log_vol = first(grid)
+    @test choices == choicemap((:sign, false), (:z, [-2.0, -2.0]))
+    @test log_vol ≈ log(0.1^2)
+
+    @test all(all([-2.0, -2.0] .<= choices[:z] .<= [2.0, 2.0]) for (choices, _) in grid)
+    @test all(log_vol ≈ log(0.1^2) for (_, log_vol) in grid)
+
+    # run enumerative inference over grid
+    traces, log_norm_weights, lml_est =
+        enumerative_inference(mixture_model, (), choicemap(), grid)
+
+    @test size(traces) == (2, 40, 40)
+    @test length(traces) == 3200
+    @test all(all([-2.0, -2.0] .<= tr[:z] .<= [2.0, 2.0]) for tr in traces)
+
+    # test that log-weights are as expected
+    function expected_logpdf(tr)
+        x, y = tr[:z]
+        mu = tr[:sign] ? 0.5 : -0.5
+        return log(0.5) + logpdf(normal, x, mu, 1.0) + logpdf(normal, y, mu, 1.0)
+    end
+
+    log_joint_weights = [expected_logpdf(tr) + log(0.1^2) for tr in traces]
+    lml_expected = logsumexp(log_joint_weights)
+    @test lml_est ≈ lml_expected
+    @test all((jw - lml_expected) ≈ w for (jw, w) in zip(log_joint_weights, log_norm_weights))
+
+    # test that maximal log-weights are at modes
+    max_log_weight = maximum(log_norm_weights)
+    max_idxs = findall(log_norm_weights .== max_log_weight)
+
+    max_trace_1 = traces[max_idxs[1]]
+    @test max_trace_1[:sign] == false
+    @test max_trace_1[:z] == [-0.5, -0.5]
+
+    max_trace_2 = traces[max_idxs[2]]
+    @test max_trace_2[:sign] == true
+    @test max_trace_2[:z] == [0.5, 0.5]
+
 end