Post-process when no sample sites present.

Current post-processing behaviour skips models with only deterministic variables. Applying this change will return consistent samples regardless of whether `sample` sites are present.

numpyro/infer/mcmc.py

@@ -195,9 +195,7 @@ def collect_and_postprocess(x):
         if collect_fields:
             fields = nested_attrgetter(*collect_fields)(x[0])
             fields = [fields] if len(collect_fields) == 1 else list(fields)
-            site_values = jax.tree.flatten(fields[0])[0]
-            if len(site_values) > 0:
-                fields[0] = postprocess_fn(fields[0], *x[1:])
+            fields[0] = postprocess_fn(fields[0], *x[1:])
 
             if remove_sites != ():
                 assert isinstance(fields[0], dict)

test/infer/test_mcmc.py

@@ -1208,3 +1208,27 @@ def model():
     mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10)
     mcmc.run(random.PRNGKey(0), extra_fields=("z.x",))
     assert_allclose(mcmc.get_samples()["x"], jnp.exp(mcmc.get_extra_fields()["z.x"]))
+
+def test_all_deterministic():
+    def model1():
+        numpyro.deterministic("x", 1.0)
+
+    def model2():
+        numpyro.deterministic("x", jnp.array([1.0, 2.0]))
+
+    num_samples = 10
+    shapes = {model1: (), model2: (2,)}
+
+    for model, shape in shapes.items():
+        mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=num_samples)
+        mcmc.run(random.PRNGKey(0))
+        assert mcmc.get_samples()["x"].shape == (num_samples,) + shape
+
+def test_empty_summary():
+    def model():
+        pass
+
+    mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10)
+    mcmc.run(random.PRNGKey(0))
+
+    mcmc.print_summary()