Fix broadcasting conditions in broadcast_shape_iter

aesara/tensor/extra_ops.py

@@ -1548,15 +1548,26 @@ def broadcast_shape_iter(
                 )
 
                 if len(nonconst_nb_shapes) > 0:
+                    # All the potential non-broadcast shapes need to either
+                    # be broadcastable or equal to the one non-broadcastable
+                    # constant `const_nt_shape_var`.
                     assert_dim = Assert("Could not broadcast dimensions")
                     assert_cond = reduce(
                         aes.and_,
-                        (aes.eq(nbv, const_nt_shape_var) for nbv in nonconst_nb_shapes),
+                        (
+                            aes.or_(
+                                aes.eq(nbv, one_at), aes.eq(nbv, const_nt_shape_var)
+                            )
+                            for nbv in nonconst_nb_shapes
+                        ),
                     )
                     bcast_dim = assert_dim(const_nt_shape_var, assert_cond)
                 else:
                     bcast_dim = const_nt_shape_var
             else:
+                # There are no constant, non-broadcastable shapes in this
+                # dimension.
+
                 all_dims_equal = all(
                     # TODO FIXME: This is a largely deficient, and expensive, means
                     # of comparing graphs (and especially shapes)

tests/tensor/test_extra_ops.py

@@ -1198,6 +1198,31 @@ def test_broadcast_shape_symbolic(s1_vals, s2_vals, exp_res):
     assert tuple(res.eval(eval_point)) == exp_res
 
 
+def test_broadcast_shape_symbolic_one_symbolic():
+    """Test case for a constant non-broadcast shape and a symbolic shape."""
+    one_at = at.as_tensor(1, dtype=np.int64)
+    three_at = at.as_tensor(3, dtype=np.int64)
+    int_div = one_at / one_at
+
+    assert int_div.owner.op == at.true_div
+
+    index_shapes = [
+        (one_at, one_at, three_at),
+        (one_at, int_div, one_at),
+        (one_at, one_at, int_div),
+    ]
+
+    res_shape = broadcast_shape(*index_shapes, arrays_are_shapes=True)
+
+    from aesara.graph.rewriting.utils import rewrite_graph
+
+    res_shape = rewrite_graph(res_shape)
+
+    assert res_shape[0].data == 1
+    assert res_shape[1].data == 1
+    assert res_shape[2].data == 3
+
+
 class TestBroadcastTo(utt.InferShapeTester):
     def setup_method(self):
         super().setup_method()