Hm/all deterministic (#1914)

* Skip printing summary if empty.

* 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/diagnostics.py

@@ -257,6 +257,8 @@ def summary(
 
     summary_dict = {}
     for name, value in samples.items():
+        if len(value) == 0:
+            continue
         value = device_get(value)
         value_flat = np.reshape(value, (-1,) + value.shape[2:])
         mean = value_flat.mean(axis=0)
@@ -307,6 +309,8 @@ def print_summary(
             "Param:{}".format(i): v for i, v in enumerate(jax.tree.flatten(samples)[0])
         }
     summary_dict = summary(samples, prob, group_by_chain=True)
+    if not summary_dict:
+        return
 
     row_names = {
         k: k + "[" + ",".join(map(lambda x: str(x - 1), v.shape[2:])) + "]"

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)
@@ -400,13 +398,27 @@ def _get_cached_fns(self):
             fns, key = None, None
         if fns is None:
 
+            def ensure_vmap(fn, batch_size=None):
+                def wrapper(x):
+                    x_arrays = jax.tree.flatten(x)[0]
+                    if len(x_arrays) > 0:
+                        return vmap(fn)(x)
+                    else:
+                        assert batch_size is not None
+                        return jax.tree.map(
+                            lambda x: jnp.broadcast_to(x, (batch_size,) + jnp.shape(x)),
+                            fn(x),
+                        )
+
+                return wrapper
+
             def _postprocess_fn(state, args, kwargs):
                 if self.postprocess_fn is None:
                     body_fn = self.sampler.postprocess_fn(args, kwargs)
                 else:
                     body_fn = self.postprocess_fn
                 if self.chain_method == "vectorized" and self.num_chains > 1:
-                    body_fn = vmap(body_fn)
+                    body_fn = ensure_vmap(body_fn, batch_size=self.num_chains)
 
                 return body_fn(state)
 

test/infer/test_mcmc.py

@@ -1208,3 +1208,29 @@ 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()