Don't use numba objectmode with vector boolean inc_subtensor

pytensor/link/numba/dispatch/subtensor.py

@@ -130,7 +130,6 @@ def numba_funcify_AdvancedSubtensor(op, node, **kwargs):
         if isinstance(idx.type, TensorType)
     ]
 
-    # Special case for consecutive consecutive vector indices
     def broadcasted_to(x_bcast: tuple[bool, ...], to_bcast: tuple[bool, ...]):
         # Check that x is not broadcasted to y based on broadcastable info
         if len(x_bcast) < len(to_bcast):
@@ -176,7 +175,14 @@ def broadcasted_to(x_bcast: tuple[bool, ...], to_bcast: tuple[bool, ...]):
         or (
             isinstance(op, AdvancedIncSubtensor)
             and not op.set_instead_of_inc
-            and not op.ignore_duplicates
+            and not (
+                op.ignore_duplicates
+                # Only vector integer indices can have "duplicates", not scalars or boolean vectors
+                or all(
+                    adv_idx["ndim"] == 0 or adv_idx["dtype"] == "bool"
+                    for adv_idx in adv_idxs
+                )
+            )
         )
     ):
         return generate_fallback_impl(op, node, **kwargs)

tests/link/numba/test_subtensor.py

@@ -314,8 +314,16 @@ def test_AdvancedIncSubtensor1(x, y, indices):
             np.arange(3 * 4 * 5).reshape((3, 4, 5)),
             -np.arange(1 * 4 * 5).reshape(1, 4, 5),
             (np.array([True, False, False])),  # Broadcasted boolean index
+            False,  # It shouldn't matter what we set this to, boolean indices cannot be duplicate
             False,
             False,
+        ),
+        (
+            np.arange(3 * 4 * 5).reshape((3, 4, 5)),
+            -np.arange(1 * 4 * 5).reshape(1, 4, 5),
+            (np.array([True, False, False])),  # Broadcasted boolean index
+            True,  # It shouldn't matter what we set this to, boolean indices cannot be duplicate
+            False,
             False,
         ),
         (