Support multidimensional boolean set/inc_subtensor in Numba via rewrite

pytensor/tensor/rewriting/subtensor.py

@@ -249,7 +249,7 @@ def local_AdvancedIncSubtensor_to_AdvancedIncSubtensor1(fgraph, node):
     This is only done when there's a single vector index.
     """
 
-    if not isinstance(node.op, AdvancedIncSubtensor) or node.op.ignore_duplicates:
+    if node.op.ignore_duplicates:
         # `AdvancedIncSubtensor1` does not ignore duplicate index values
         return
 
@@ -1967,19 +1967,26 @@ def local_blockwise_advanced_inc_subtensor(fgraph, node):
     return new_out
 
 
-@node_rewriter(tracks=[AdvancedSubtensor])
+@node_rewriter(tracks=[AdvancedSubtensor, AdvancedIncSubtensor])
 def ravel_multidimensional_bool_idx(fgraph, node):
     """Convert multidimensional boolean indexing into equivalent vector boolean index, supported by Numba
 
     x[eye(3, dtype=bool)] -> x.ravel()[eye(3).ravel()]
+    x[eye(3, dtype=bool)].set(y) -> x.ravel()[eye(3).ravel()].set(y).reshape(x.shape)
     """
-    x, *idxs = node.inputs
+    if isinstance(node.op, AdvancedSubtensor):
+        x, *idxs = node.inputs
+    else:
+        x, y, *idxs = node.inputs
 
     if any(
-        isinstance(idx.type, TensorType) and idx.type.dtype.startswith("int")
+        (
+            (isinstance(idx.type, TensorType) and idx.type.dtype.startswith("int"))
+            or isinstance(idx.type, NoneTypeT)
+        )
         for idx in idxs
     ):
-        # Get out if there are any other advanced indexes
+        # Get out if there are any other advanced indexes or np.newaxis
         return None
 
     bool_idxs = [
@@ -2007,7 +2014,16 @@ def ravel_multidimensional_bool_idx(fgraph, node):
     new_idxs = list(idxs)
     new_idxs[bool_idx_pos] = raveled_bool_idx
 
-    return [raveled_x[tuple(new_idxs)]]
+    if isinstance(node.op, AdvancedSubtensor):
+        new_out = node.op(raveled_x, *new_idxs)
+    else:
+        # The dimensions of y that correspond to the boolean indices
+        # must already be raveled in the original graph, so we don't need to do anything to it
+        new_out = node.op(raveled_x, y, *new_idxs)
+        # But we must reshape the output to math the original shape
+        new_out = new_out.reshape(x_shape)
+
+    return [copy_stack_trace(node.outputs[0], new_out)]
 
 
 @node_rewriter(tracks=[AdvancedSubtensor])
@@ -2024,10 +2040,13 @@ def ravel_multidimensional_int_idx(fgraph, node):
     x, *idxs = node.inputs
 
     if any(
-        isinstance(idx.type, TensorType) and idx.type.dtype.startswith("bool")
+        (
+            (isinstance(idx.type, TensorType) and idx.type.dtype == "bool")
+            or isinstance(idx.type, NoneTypeT)
+        )
         for idx in idxs
     ):
-        # Get out if there are any other advanced indexes
+        # Get out if there are any other advanced indexes or np.newaxis
         return None
 
     int_idxs = [
@@ -2059,7 +2078,8 @@ def ravel_multidimensional_int_idx(fgraph, node):
         *int_idx.shape,
         *raveled_shape[int_idx_pos + 1 :],
     )
-    return [raveled_subtensor.reshape(unraveled_shape)]
+    new_out = raveled_subtensor.reshape(unraveled_shape)
+    return [copy_stack_trace(node.outputs[0], new_out)]
 
 
 optdb["specialize"].register(

pytensor/tensor/subtensor.py

@@ -1456,11 +1456,8 @@ def inc_subtensor(
         views; if they overlap, the result of this `Op` will generally be
         incorrect. This value has no effect if ``inplace=False``.
     ignore_duplicates
-        This determines whether or not ``x[indices] += y`` is used or
-        ``np.add.at(x, indices, y)``.  When the special duplicates handling of
-        ``np.add.at`` isn't required, setting this option to ``True``
-        (i.e. using ``x[indices] += y``) can resulting in faster compiled
-        graphs.
+        This determines whether ``x[indices] += y`` is used or
+        ``np.add.at(x, indices, y)``.
 
     Examples
     --------

tests/link/numba/test_subtensor.py

@@ -334,8 +334,19 @@ def test_AdvancedIncSubtensor1(x, y, indices):
             -np.arange(3),
             (np.eye(3).astype(bool)),  # Boolean index
             False,
-            True,
-            True,
+            False,
+            False,
+        ),
+        (
+            np.arange(3 * 3 * 5).reshape((3, 3, 5)),
+            rng.poisson(size=(3, 2)),
+            (
+                np.eye(3).astype(bool),
+                slice(-2, None),
+            ),  # Boolean index, mixed with basic index
+            False,
+            False,
+            False,
         ),
         (
             np.arange(3 * 4 * 5).reshape((3, 4, 5)),
@@ -394,10 +405,18 @@ def test_AdvancedIncSubtensor1(x, y, indices):
             rng.poisson(size=(2, 2)),
             ([[1, 2], [2, 3]]),  # matrix indices
             False,
+            False,  # Gets converted to AdvancedIncSubtensor1
+            True,  # This is actually supported with the default `ignore_duplicates=False`
+        ),
+        (
+            np.arange(3 * 5).reshape((3, 5)),
+            rng.poisson(size=(1, 2, 2)),
+            (slice(1, 3), [[1, 2], [2, 3]]),  # matrix indices, mixed with basic index
+            False,
             True,
             True,
         ),
-        pytest.param(
+        (
             np.arange(3 * 4 * 5).reshape((3, 4, 5)),
             rng.poisson(size=(2, 5)),
             ([1, 1], [2, 2]),  # Repeated indices
@@ -418,6 +437,9 @@ def test_AdvancedIncSubtensor(
     inc_requires_objmode,
     inplace,
 ):
+    # Need rewrite to support certain forms of advanced indexing without object mode
+    mode = numba_mode.including("specialize")
+
     x_pt = pt.as_tensor(x).type("x")
     y_pt = pt.as_tensor(y).type("y")
 
@@ -432,7 +454,7 @@ def test_AdvancedIncSubtensor(
         if set_requires_objmode
         else contextlib.nullcontext()
     ):
-        fn, _ = compare_numba_and_py(([x_pt, y_pt], [out_pt]), [x, y])
+        fn, _ = compare_numba_and_py(([x_pt, y_pt], [out_pt]), [x, y], numba_mode=mode)
 
     if inplace:
         # Test updates inplace
@@ -452,7 +474,7 @@ def test_AdvancedIncSubtensor(
         if inc_requires_objmode
         else contextlib.nullcontext()
     ):
-        fn, _ = compare_numba_and_py(([x_pt, y_pt], [out_pt]), [x, y])
+        fn, _ = compare_numba_and_py(([x_pt, y_pt], [out_pt]), [x, y], numba_mode=mode)
     if inplace:
         # Test updates inplace
         x_orig = x.copy()