Do not consider dims without coords volatile if length has not changed

pymc/sampling/forward.py

@@ -42,7 +42,8 @@
     RandomGeneratorSharedVariable,
     RandomStateSharedVariable,
 )
-from pytensor.tensor.sharedvar import SharedVariable
+from pytensor.tensor.sharedvar import SharedVariable, TensorSharedVariable
+from pytensor.tensor.variable import TensorConstant
 from rich.console import Console
 from rich.progress import BarColumn, TextColumn, TimeElapsedColumn, TimeRemainingColumn
 from rich.theme import Theme
@@ -76,6 +77,28 @@
 _log = logging.getLogger(__name__)
 
 
+def get_constant_coords(trace_coords: dict[str, np.ndarray], model: Model) -> set:
+    """Get the set of coords that have remained constant between the trace and model"""
+    constant_coords = set()
+    for dim, coord in trace_coords.items():
+        current_coord = model.coords.get(dim, None)
+        current_length = model.dim_lengths.get(dim, None)
+        if isinstance(current_length, TensorSharedVariable):
+            current_length = current_length.get_value()
+        elif isinstance(current_length, TensorConstant):
+            current_length = current_length.data
+        if (
+            current_coord is not None
+            and len(coord) == len(current_coord)
+            and np.all(coord == current_coord)
+        ) or (
+            # Coord was defined without values (only length)
+            current_coord is None and len(coord) == current_length
+        ):
+            constant_coords.add(dim)
+    return constant_coords
+
+
 def get_vars_in_point_list(trace, model):
     """Get the list of Variable instances in the model that have values stored in the trace."""
     if not isinstance(trace, MultiTrace):
@@ -792,15 +815,7 @@ def sample_posterior_predictive(
             stacklevel=2,
         )
 
-    constant_coords = set()
-    for dim, coord in trace_coords.items():
-        current_coord = model.coords.get(dim, None)
-        if (
-            current_coord is not None
-            and len(coord) == len(current_coord)
-            and np.all(coord == current_coord)
-        ):
-            constant_coords.add(dim)
+    constant_coords = get_constant_coords(trace_coords, model)
 
     if var_names is not None:
         vars_ = [model[x] for x in var_names]

tests/sampling/test_forward.py

@@ -35,6 +35,7 @@
 from pymc.pytensorf import compile_pymc
 from pymc.sampling.forward import (
     compile_forward_sampling_function,
+    get_constant_coords,
     get_vars_in_point_list,
     observed_dependent_deterministics,
 )
@@ -427,6 +428,36 @@ def test_mutable_coords_volatile(self):
             "offsets",
         }
 
+    def test_length_coords_volatile(self):
+        with pm.Model() as model:
+            model.add_coord("trial", length=3)
+            x = pm.Normal("x", dims="trial")
+            y = pm.Deterministic("y", x.mean())
+
+        # Same coord length -- `x` is not volatile
+        trace_same_len = az_from_dict(
+            posterior={"x": [[[np.pi] * 3]]},
+            coords={"trial": range(3)},
+            dims={"x": ["trial"]},
+        )
+        with model:
+            pp_same_len = pm.sample_posterior_predictive(
+                trace_same_len, var_names=["y"]
+            ).posterior_predictive
+        assert pp_same_len["y"] == np.pi
+
+        # Coord length changed -- `x` is volatile
+        trace_diff_len = az_from_dict(
+            posterior={"x": [[[np.pi] * 2]]},
+            coords={"trial": range(2)},
+            dims={"x": ["trial"]},
+        )
+        with model:
+            pp_diff_len = pm.sample_posterior_predictive(
+                trace_diff_len, var_names=["y"]
+            ).posterior_predictive
+        assert pp_diff_len["y"] != np.pi
+
 
 class TestSamplePPC:
     def test_normal_scalar(self):
@@ -1669,6 +1700,20 @@ def test_Triangular(
         assert prior["target"].shape == (prior_samples, *shape)
 
 
+def test_get_constant_coords():
+    with pm.Model() as model:
+        model.add_coord("length_coord", length=1)
+        model.add_coord("value_coord", values=(3,))
+
+    trace_coords_same = {"length_coord": np.array([0]), "value_coord": np.array([3])}
+    constant_coords_same = get_constant_coords(trace_coords_same, model)
+    assert constant_coords_same == {"length_coord", "value_coord"}
+
+    trace_coords_diff = {"length_coord": np.array([0, 1]), "value_coord": np.array([4])}
+    constant_coords_diff = get_constant_coords(trace_coords_diff, model)
+    assert constant_coords_diff == set()
+
+
 def test_get_vars_in_point_list():
     with pm.Model() as modelA:
         pm.Normal("a", 0, 1)