Support more cases of numba advanced indexing

pytensor/link/numba/dispatch/subtensor.py

@@ -150,7 +150,7 @@ def broadcasted_to(x_bcast: tuple[bool, ...], to_bcast: tuple[bool, ...]):
             for adv_idx in adv_idxs
         )
         # Must be consecutive
-        and not op.non_contiguous_adv_indexing(node)
+        and not op.non_consecutive_adv_indexing(node)
         # y in set/inc_subtensor cannot be broadcasted
         and (
             y is None

pytensor/tensor/rewriting/subtensor.py

@@ -2029,18 +2029,41 @@ def ravel_multidimensional_bool_idx(fgraph, node):
     return [copy_stack_trace(node.outputs[0], new_out)]
 
 
-@node_rewriter(tracks=[AdvancedSubtensor])
+@node_rewriter(tracks=[AdvancedSubtensor, AdvancedIncSubtensor])
 def ravel_multidimensional_int_idx(fgraph, node):
-    """Convert multidimensional integer indexing into equivalent vector integer index, supported by Numba
-
-    x[eye(3, dtype=int)] -> x[eye(3).ravel()].reshape((3, 3))
+    """Convert multidimensional integer indexing into equivalent consecutive vector integer index,
+    supported by Numba or by our specialized dispatchers
 
+        x[eye(3)] -> x[eye(3).ravel()].reshape((3, 3))
 
     NOTE: This is very similar to the rewrite `local_replace_AdvancedSubtensor` except it also handles non-full slices
 
-    x[eye(3, dtype=int), 2:] -> x[eye(3).ravel(), 2:].reshape((3, 3, ...)), where ... are the remaining output shapes
+        x[eye(3), 2:] -> x[eye(3).ravel(), 2:].reshape((3, 3, ...)), where ... are the remaining output shapes
+
+    It also handles multiple integer indices, but only if they don't broadcast
+
+        x[eye(3,), 2:, eye(3)] -> x[eye(3).ravel(), eye(3).ravel(), 2:].reshape((3, 3, ...)), where ... are the remaining output shapes
+
+    Also handles AdvancedIncSubtensor, but only if the advanced indices are consecutive and neither indices nor y broadcast
+
+        x[eye(3), 2:].set(y) -> x[eye(3).ravel(), 2:].set(y.reshape(-1, y.shape[1:]))
+
     """
-    x, *idxs = node.inputs
+    op = node.op
+    non_consecutive_adv_indexing = op.non_consecutive_adv_indexing(node)
+    is_inc_subtensor = isinstance(op, AdvancedIncSubtensor)
+
+    if is_inc_subtensor:
+        x, y, *idxs = node.inputs
+        # Inc/SetSubtensor is harder to reason about due to y
+        # We get out if it's broadcasting or if the advanced indices are non-consecutive
+        if non_consecutive_adv_indexing or (
+            y.type.broadcastable != x[tuple(idxs)].type.broadcastable
+        ):
+            return None
+
+    else:
+        x, *idxs = node.inputs
 
     if any(
         (
@@ -2049,50 +2072,103 @@ def ravel_multidimensional_int_idx(fgraph, node):
         )
         for idx in idxs
     ):
-        # Get out if there are any other advanced indexes or np.newaxis
+        # Get out if there are any other advanced indices or np.newaxis
         return None
 
-    int_idxs = [
+    int_idxs_and_pos = [
         (i, idx)
         for i, idx in enumerate(idxs)
         if (isinstance(idx.type, TensorType) and idx.dtype in integer_dtypes)
     ]
 
-    if len(int_idxs) != 1:
-        # Get out if there are no or multiple integer idxs
+    if not int_idxs_and_pos:
         return None
 
-    [(int_idx_pos, int_idx)] = int_idxs
-    if int_idx.type.ndim < 2:
-        # No need to do anything if it's a vector or scalar, as it's already supported by Numba
+    int_idxs_pos, int_idxs = zip(
+        *int_idxs_and_pos, strict=False
+    )  # strict=False because by definition it's true
+
+    first_int_idx_pos = int_idxs_pos[0]
+    first_int_idx = int_idxs[0]
+    first_int_idx_bcast = first_int_idx.type.broadcastable
+
+    if any(int_idx.type.broadcastable != first_int_idx_bcast for int_idx in int_idxs):
+        # We don't have a view-only broadcasting operation
+        # Explicitly broadcasting the indices can incur a memory / copy overhead
         return None
 
-    raveled_int_idx = int_idx.ravel()
-    new_idxs = list(idxs)
-    new_idxs[int_idx_pos] = raveled_int_idx
-    raveled_subtensor = x[tuple(new_idxs)]
-
-    # Reshape into correct shape
-    # Because we only allow one advanced indexing, the output dimension corresponding to the raveled integer indexing
-    # must match the input position. If there were multiple advanced indexes, this could have been forcefully moved to the front
-    raveled_shape = raveled_subtensor.shape
-    unraveled_shape = (
-        *raveled_shape[:int_idx_pos],
-        *int_idx.shape,
-        *raveled_shape[int_idx_pos + 1 :],
-    )
-    new_out = raveled_subtensor.reshape(unraveled_shape)
+    int_idxs_ndim = len(first_int_idx_bcast)
+    if (
+        int_idxs_ndim == 0
+    ):  # This should be a basic indexing operation, rewrite elsewhere
+        return None
+
+    int_idxs_need_raveling = int_idxs_ndim > 1
+    if not (int_idxs_need_raveling or non_consecutive_adv_indexing):
+        # Numba or our dispatch natively supports consecutive vector indices, nothing needs to be done
+        return None
+
+    # Reorder non-consecutive indices
+    if non_consecutive_adv_indexing:
+        assert not is_inc_subtensor  # Sanity check that we got out if this was the case
+        # This case works as if all the advanced indices were on the front
+        transposition = list(int_idxs_pos) + [
+            i for i in range(len(idxs)) if i not in int_idxs_pos
+        ]
+        idxs = tuple(idxs[a] for a in transposition)
+        x = x.transpose(transposition)
+        first_int_idx_pos = 0
+        del int_idxs_pos  # Make sure they are not wrongly used
+
+    # Ravel multidimensional indices
+    if int_idxs_need_raveling:
+        idxs = list(idxs)
+        for idx_pos, int_idx in enumerate(int_idxs, start=first_int_idx_pos):
+            idxs[idx_pos] = int_idx.ravel()
+
+    # Index with reordered and/or raveled indices
+    new_subtensor = x[tuple(idxs)]
+
+    if is_inc_subtensor:
+        y_shape = tuple(y.shape)
+        y_raveled_shape = (
+            *y_shape[:first_int_idx_pos],
+            -1,
+            *y_shape[first_int_idx_pos + int_idxs_ndim :],
+        )
+        y_raveled = y.reshape(y_raveled_shape)
+
+        new_out = inc_subtensor(
+            new_subtensor,
+            y_raveled,
+            set_instead_of_inc=op.set_instead_of_inc,
+            ignore_duplicates=op.ignore_duplicates,
+            inplace=op.inplace,
+        )
+
+    else:
+        # Unravel advanced indexing dimensions
+        raveled_shape = tuple(new_subtensor.shape)
+        unraveled_shape = (
+            *raveled_shape[:first_int_idx_pos],
+            *first_int_idx.shape,
+            *raveled_shape[first_int_idx_pos + 1 :],
+        )
+        new_out = new_subtensor.reshape(unraveled_shape)
+
     return [copy_stack_trace(node.outputs[0], new_out)]
 
 
 optdb["specialize"].register(
     ravel_multidimensional_bool_idx.__name__,
     ravel_multidimensional_bool_idx,
     "numba",
+    use_db_name_as_tag=False,  # Not included if only "specialize" is requested
 )
 
 optdb["specialize"].register(
     ravel_multidimensional_int_idx.__name__,
     ravel_multidimensional_int_idx,
     "numba",
+    use_db_name_as_tag=False,  # Not included if only "specialize" is requested
 )

pytensor/tensor/subtensor.py

@@ -1,5 +1,6 @@
 import logging
 import sys
+import warnings
 from collections.abc import Callable, Iterable
 from itertools import chain, groupby
 from textwrap import dedent
@@ -59,6 +60,7 @@
     zscalar,
 )
 from pytensor.tensor.type_other import (
+    MakeSlice,
     NoneConst,
     NoneTypeT,
     SliceConstant,
@@ -527,11 +529,20 @@ def basic_shape(shape, indices):
         if isinstance(idx, slice):
             res_shape += (slice_len(idx, n),)
         elif isinstance(getattr(idx, "type", None), SliceType):
-            if idx.owner:
-                idx_inputs = idx.owner.inputs
+            if idx.owner is None:
+                if not isinstance(idx, Constant):
+                    # This is an input slice, we can't reason symbolically on it.
+                    # We don't even know if we will get None entries or integers
+                    res_shape += (None,)
+                    continue
+                else:
+                    sl: slice = idx.data
+                    slice_inputs = (sl.start, sl.stop, sl.step)
+            elif isinstance(idx.owner.op, MakeSlice):
+                slice_inputs = idx.owner.inputs
             else:
-                idx_inputs = (None,)
-            res_shape += (slice_len(slice(*idx_inputs), n),)
+                raise ValueError(f"Unexpected Slice producing Op {idx.owner.op}")
+            res_shape += (slice_len(slice(*slice_inputs), n),)
         elif idx is None:
             res_shape += (ps.ScalarConstant(ps.int64, 1),)
         elif isinstance(getattr(idx, "type", None), NoneTypeT):
@@ -570,8 +581,8 @@ def group_indices(indices):
     return idx_groups
 
 
-def _non_contiguous_adv_indexing(indices) -> bool:
-    """Check if the advanced indexing is non-contiguous (i.e., split by basic indexing)."""
+def _non_consecutive_adv_indexing(indices) -> bool:
+    """Check if the advanced indexing is non-consecutive (i.e., split by basic indexing)."""
     idx_groups = group_indices(indices)
     # This means that there are at least two groups of advanced indexing separated by basic indexing
     return len(idx_groups) > 3 or (len(idx_groups) == 3 and not idx_groups[0][0])
@@ -601,7 +612,7 @@ def indexed_result_shape(array_shape, indices, indices_are_shapes=False):
     remaining_dims = range(pytensor.tensor.basic.get_vector_length(array_shape))
     idx_groups = group_indices(indices)
 
-    if _non_contiguous_adv_indexing(indices):
+    if _non_consecutive_adv_indexing(indices):
         # In this case NumPy places the advanced index groups in the front of the array
         # https://numpy.org/devdocs/user/basics.indexing.html#combining-advanced-and-basic-indexing
         idx_groups = sorted(idx_groups, key=lambda x: x[0])
@@ -2728,6 +2739,11 @@ def is_bool_index(idx):
         res_shape = list(
             indexed_result_shape(ishapes[0], index_shapes, indices_are_shapes=True)
         )
+        for i, res_dim_length in enumerate(res_shape):
+            if res_dim_length is None:
+                # This can happen when we have a Slice provided by the user (not a constant nor the result of MakeSlice)
+                # We must compute the Op to find its shape
+                res_shape[i] = Shape_i(i)(node.out)
 
         adv_indices = [idx for idx in indices if not is_basic_idx(idx)]
         bool_indices = [idx for idx in adv_indices if is_bool_index(idx)]
@@ -2781,10 +2797,17 @@ def grad(self, inputs, grads):
 
     @staticmethod
     def non_contiguous_adv_indexing(node: Apply) -> bool:
+        warnings.warn(
+            "Method was renamed to `non_consecutive_adv_indexing`", FutureWarning
+        )
+        return AdvancedSubtensor.non_consecutive_adv_indexing(node)
+
+    @staticmethod
+    def non_consecutive_adv_indexing(node: Apply) -> bool:
         """
-        Check if the advanced indexing is non-contiguous (i.e. interrupted by basic indexing).
+        Check if the advanced indexing is non-consecutive (i.e. interrupted by basic indexing).
 
-        This function checks if the advanced indexing is non-contiguous,
+        This function checks if the advanced indexing is non-consecutive,
         in which case the advanced index dimensions are placed on the left of the
         output array, regardless of their opriginal position.
 
@@ -2799,10 +2822,10 @@ def non_contiguous_adv_indexing(node: Apply) -> bool:
         Returns
         -------
         bool
-            True if the advanced indexing is non-contiguous, False otherwise.
+            True if the advanced indexing is non-consecutive, False otherwise.
         """
         _, *idxs = node.inputs
-        return _non_contiguous_adv_indexing(idxs)
+        return _non_consecutive_adv_indexing(idxs)
 
 
 advanced_subtensor = AdvancedSubtensor()
@@ -2820,7 +2843,7 @@ def vectorize_advanced_subtensor(op: AdvancedSubtensor, node, *batch_inputs):
         if isinstance(batch_idx, TensorVariable)
     )
 
-    if idxs_are_batched or (x_is_batched and op.non_contiguous_adv_indexing(node)):
+    if idxs_are_batched or (x_is_batched and op.non_consecutive_adv_indexing(node)):
         # Fallback to Blockwise if idxs are batched or if we have non contiguous advanced indexing
         # which would put the indexed results to the left of the batch dimensions!
         # TODO: Not all cases must be handled by Blockwise, but the logic is complex
@@ -2829,7 +2852,7 @@ def vectorize_advanced_subtensor(op: AdvancedSubtensor, node, *batch_inputs):
         # TODO: Implement these internally, so Blockwise is always a safe fallback
         if any(not isinstance(idx, TensorVariable) for idx in idxs):
             raise NotImplementedError(
-                "Vectorized AdvancedSubtensor with batched indexes or non-contiguous advanced indexing "
+                "Vectorized AdvancedSubtensor with batched indexes or non-consecutive advanced indexing "
                 "and slices or newaxis is currently not supported."
             )
         else:
@@ -2939,10 +2962,17 @@ def grad(self, inpt, output_gradients):
 
     @staticmethod
     def non_contiguous_adv_indexing(node: Apply) -> bool:
+        warnings.warn(
+            "Method was renamed to `non_consecutive_adv_indexing`", FutureWarning
+        )
+        return AdvancedIncSubtensor.non_consecutive_adv_indexing(node)
+
+    @staticmethod
+    def non_consecutive_adv_indexing(node: Apply) -> bool:
         """
-        Check if the advanced indexing is non-contiguous (i.e. interrupted by basic indexing).
+        Check if the advanced indexing is non-consecutive (i.e. interrupted by basic indexing).
 
-        This function checks if the advanced indexing is non-contiguous,
+        This function checks if the advanced indexing is non-consecutive,
         in which case the advanced index dimensions are placed on the left of the
         output array, regardless of their opriginal position.
 
@@ -2957,10 +2987,10 @@ def non_contiguous_adv_indexing(node: Apply) -> bool:
         Returns
         -------
         bool
-            True if the advanced indexing is non-contiguous, False otherwise.
+            True if the advanced indexing is non-consecutive, False otherwise.
         """
         _, _, *idxs = node.inputs
-        return _non_contiguous_adv_indexing(idxs)
+        return _non_consecutive_adv_indexing(idxs)
 
 
 advanced_inc_subtensor = AdvancedIncSubtensor()

scripts/mypy-failing.txt

@@ -11,7 +11,6 @@ pytensor/link/numba/dispatch/scan.py
 pytensor/printing.py
 pytensor/raise_op.py
 pytensor/sparse/basic.py
-pytensor/sparse/type.py
 pytensor/tensor/basic.py
 pytensor/tensor/blas_c.py
 pytensor/tensor/blas_headers.py

scripts/run_mypy.py

@@ -142,7 +142,13 @@ def check_no_unexpected_results(mypy_lines: Iterable[str]):
         print(*missing, sep="\n")
         sys.exit(1)
     cp = subprocess.run(
-        ["mypy", "--show-error-codes", "pytensor"],
+        [
+            "mypy",
+            "--show-error-codes",
+            "--disable-error-code",
+            "annotation-unchecked",
+            "pytensor",
+        ],
         capture_output=True,
     )
     output = cp.stdout.decode()