Fix promote_batch_shape logic to take batch shapes of all parameters (#…

…1973)

* fix promote batch shape logic

* lint

numpyro/distributions/batch_util.py

@@ -504,12 +504,16 @@ def promote_batch_shape(d: Distribution):
 
 @promote_batch_shape.register
 def _default_promote_batch_shape(d: Distribution):
-    attr_name = list(d.arg_constraints.keys())[0]
-    attr_event_dim = d.arg_constraints[attr_name].event_dim
-    attr = getattr(d, attr_name)
-    resolved_batch_shape = attr.shape[
-        : max(0, attr.ndim - d.event_dim - attr_event_dim)
-    ]
+    attr_batch_shapes = [d.batch_shape]
+    for attr_name, constraint in d.arg_constraints.items():
+        try:
+            attr_event_dim = constraint.event_dim
+        except NotImplementedError:
+            continue
+        attr = getattr(d, attr_name)
+        attr_batch_ndim = max(0, jnp.ndim(attr) - attr_event_dim)
+        attr_batch_shapes.append(jnp.shape(attr)[:attr_batch_ndim])
+    resolved_batch_shape = jnp.broadcast_shapes(*attr_batch_shapes)
     new_self = copy.deepcopy(d)
     new_self._batch_shape = resolved_batch_shape
     return new_self

test/contrib/test_control_flow.py

@@ -272,3 +272,19 @@ def transition(x_prev, y_curr):
 
     xhat = results.params["x_auto_loc"]
     assert_allclose(xhat, tr["x"]["value"], rtol=0.1, atol=0.2)
+
+
+def test_scan_mvn():
+    def model():
+        def transition(c, a):
+            with numpyro.plate("foo", 5):
+                c2 = numpyro.sample(
+                    "val", dist.MultivariateNormal(c + a, scale_tril=jnp.eye(2))
+                )
+            return c2, c2
+
+        scan(transition, jnp.zeros((5, 2)), jnp.ones((4, 5, 2)))
+
+    with numpyro.handlers.seed(rng_seed=0), numpyro.handlers.trace() as tr:
+        model()
+    assert tr["val"]["fn"].batch_shape == (4, 5)