Infer shape of advanced boolean indexing

pytensor/tensor/extra_ops.py

@@ -21,14 +21,18 @@
 from pytensor.raise_op import Assert
 from pytensor.scalar import int32 as int_t
 from pytensor.scalar import upcast
+from pytensor.tensor import as_tensor_variable
 from pytensor.tensor import basic as at
 from pytensor.tensor import get_vector_length
 from pytensor.tensor.exceptions import NotScalarConstantError
-from pytensor.tensor.math import abs as at_abs
+from pytensor.tensor.math import abs as pt_abs
 from pytensor.tensor.math import all as pt_all
 from pytensor.tensor.math import eq as pt_eq
-from pytensor.tensor.math import ge, lt, maximum, minimum, prod
+from pytensor.tensor.math import ge, lt
+from pytensor.tensor.math import max as pt_max
+from pytensor.tensor.math import maximum, minimum, prod
 from pytensor.tensor.math import sum as at_sum
+from pytensor.tensor.math import switch
 from pytensor.tensor.subtensor import advanced_inc_subtensor1, set_subtensor
 from pytensor.tensor.type import TensorType, dvector, int_dtypes, integer_dtypes, vector
 from pytensor.tensor.var import TensorVariable
@@ -1063,7 +1067,7 @@ def grad(self, inp, cost_grad):
         # only valid for matrices
         wr_a = fill_diagonal_offset(grad, 0, offset)
 
-        offset_abs = at_abs(offset)
+        offset_abs = pt_abs(offset)
         pos_offset_flag = ge(offset, 0)
         neg_offset_flag = lt(offset, 0)
         min_wh = minimum(width, height)
@@ -1442,6 +1446,7 @@ def ravel_multi_index(multi_index, dims, mode="raise", order="C"):
     "axes that have a statically known length 1. Use `specify_broadcastable` to "
     "inform PyTensor of a known shape."
 )
+_runtime_broadcast_assert = Assert("Could not broadcast dimensions.")
 
 
 def broadcast_shape(*arrays, **kwargs) -> Tuple[aes.ScalarVariable, ...]:
@@ -1465,6 +1470,7 @@ def broadcast_shape(*arrays, **kwargs) -> Tuple[aes.ScalarVariable, ...]:
 def broadcast_shape_iter(
     arrays: Iterable[Union[TensorVariable, Tuple[TensorVariable, ...]]],
     arrays_are_shapes: bool = False,
+    allow_runtime_broadcast: bool = False,
 ) -> Tuple[aes.ScalarVariable, ...]:
     r"""Compute the shape resulting from broadcasting arrays.
 
@@ -1480,22 +1486,24 @@ def broadcast_shape_iter(
     arrays
         An iterable of tensors, or a tuple of shapes (as tuples),
         for which the broadcast shape is computed.
-    arrays_are_shapes
+    arrays_are_shapes: bool, default False
         Indicates whether or not the `arrays` contains shape tuples.
         If you use this approach, make sure that the broadcastable dimensions
         are (scalar) constants with the value ``1``--or simply the integer
-        ``1``.
+        ``1``. This is not revelant if `allow_runtime_broadcast` is True.
+    allow_runtime_broadcast: bool, default False
+        Whether to allow non-statically known broadcast on the shape computation.
 
     """
-    one_at = pytensor.scalar.ScalarConstant(pytensor.scalar.int64, 1)
+    one = pytensor.scalar.ScalarConstant(pytensor.scalar.int64, 1)
 
     if arrays_are_shapes:
         max_dims = max(len(a) for a in arrays)
 
         array_shapes = [
-            (one_at,) * (max_dims - len(a))
+            (one,) * (max_dims - len(a))
             + tuple(
-                one_at
+                one
                 if sh == 1 or isinstance(sh, Constant) and sh.value == 1
                 else (aes.as_scalar(sh) if not isinstance(sh, Variable) else sh)
                 for sh in a
@@ -1508,10 +1516,8 @@ def broadcast_shape_iter(
         _arrays = tuple(at.as_tensor_variable(a) for a in arrays)
 
         array_shapes = [
-            (one_at,) * (max_dims - a.ndim)
-            + tuple(
-                one_at if t_sh == 1 else sh for sh, t_sh in zip(a.shape, a.type.shape)
-            )
+            (one,) * (max_dims - a.ndim)
+            + tuple(one if t_sh == 1 else sh for sh, t_sh in zip(a.shape, a.type.shape))
             for a in _arrays
         ]
 
@@ -1520,11 +1526,11 @@ def broadcast_shape_iter(
     for dim_shapes in zip(*array_shapes):
         # Get the shapes in this dimension that are not broadcastable
         # (i.e. not symbolically known to be broadcastable)
-        non_bcast_shapes = [shape for shape in dim_shapes if shape != one_at]
+        non_bcast_shapes = [shape for shape in dim_shapes if shape != one]
 
         if len(non_bcast_shapes) == 0:
             # Every shape was broadcastable in this dimension
-            result_dims.append(one_at)
+            result_dims.append(one)
         elif len(non_bcast_shapes) == 1:
             # Only one shape might not be broadcastable in this dimension
             result_dims.extend(non_bcast_shapes)
@@ -1554,9 +1560,26 @@ def broadcast_shape_iter(
                 result_dims.append(first_length)
                 continue
 
-            # Add assert that all remaining shapes are equal
-            condition = pt_all([pt_eq(first_length, other) for other in other_lengths])
-            result_dims.append(_broadcast_assert(first_length, condition))
+            if not allow_runtime_broadcast:
+                # Add assert that all remaining shapes are equal
+                condition = pt_all(
+                    [pt_eq(first_length, other) for other in other_lengths]
+                )
+                result_dims.append(_broadcast_assert(first_length, condition))
+            else:
+                lengths = as_tensor_variable((first_length, *other_lengths))
+                runtime_broadcastable = pt_eq(lengths, one)
+                result_dim = pt_abs(
+                    pt_max(switch(runtime_broadcastable, -one, lengths))
+                )
+                condition = pt_all(
+                    switch(
+                        ~runtime_broadcastable,
+                        pt_eq(lengths, result_dim),
+                        np.array(True),
+                    )
+                )
+                result_dims.append(_runtime_broadcast_assert(result_dim, condition))
 
     return tuple(result_dims)
 

pytensor/tensor/random/rewriting/basic.py

@@ -1,14 +1,15 @@
-from itertools import zip_longest
+from itertools import chain
 
 from pytensor.compile import optdb
 from pytensor.configdefaults import config
+from pytensor.graph import ancestors
 from pytensor.graph.op import compute_test_value
-from pytensor.graph.rewriting.basic import in2out, node_rewriter
+from pytensor.graph.rewriting.basic import copy_stack_trace, in2out, node_rewriter
+from pytensor.scalar import integer_types
 from pytensor.tensor import NoneConst
 from pytensor.tensor.basic import constant, get_vector_length
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.extra_ops import broadcast_to
-from pytensor.tensor.math import sum as at_sum
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.random.utils import broadcast_params
 from pytensor.tensor.shape import Shape, Shape_i, shape_padleft
@@ -18,7 +19,6 @@
     Subtensor,
     as_index_variable,
     get_idx_list,
-    indexed_result_shape,
 )
 from pytensor.tensor.type_other import SliceType
 
@@ -207,98 +207,148 @@ def local_subtensor_rv_lift(fgraph, node):
     ``mvnormal(mu, cov, size=(2,))[0, 0]``.
     """
 
-    st_op = node.op
+    def is_nd_advanced_idx(idx, dtype):
+        if isinstance(dtype, str):
+            return (getattr(idx.type, "dtype", None) == dtype) and (idx.type.ndim >= 1)
+        else:
+            return (getattr(idx.type, "dtype", None) in dtype) and (idx.type.ndim >= 1)
 
-    if not isinstance(st_op, (AdvancedSubtensor, AdvancedSubtensor1, Subtensor)):
-        return False
+    subtensor_op = node.op
 
+    old_subtensor = node.outputs[0]
     rv = node.inputs[0]
     rv_node = rv.owner
 
     if not (rv_node and isinstance(rv_node.op, RandomVariable)):
         return False
 
+    shape_feature = getattr(fgraph, "shape_feature", None)
+    if not shape_feature:
+        return None
+
+    # Use shape_feature to facilitate inferring final shape.
+    # Check that neither the RV nor the old Subtensor are in the shape graph.
+    output_shape = fgraph.shape_feature.shape_of.get(old_subtensor, None)
+    if output_shape is None or {old_subtensor, rv} & set(ancestors(output_shape)):
+        return None
+
     rv_op = rv_node.op
     rng, size, dtype, *dist_params = rv_node.inputs
 
     # Parse indices
-    idx_list = getattr(st_op, "idx_list", None)
+    idx_list = getattr(subtensor_op, "idx_list", None)
     if idx_list:
-        cdata = get_idx_list(node.inputs, idx_list)
+        idx_vars = get_idx_list(node.inputs, idx_list)
     else:
-        cdata = node.inputs[1:]
-    st_indices, st_is_bool = zip(
-        *tuple(
-            (as_index_variable(i), getattr(i, "dtype", None) == "bool") for i in cdata
-        )
-    )
+        idx_vars = node.inputs[1:]
+    indices = tuple(as_index_variable(idx) for idx in idx_vars)
+
+    # The rewrite doesn't apply if advanced indexing could broadcast the samples (leading to duplicates)
+    # Note: For simplicity this also excludes subtensor-related expand_dims (np.newaxis).
+    #  If we wanted to support that we could rewrite it as subtensor + dimshuffle
+    #  and make use of the dimshuffle lift rewrite
+    integer_dtypes = {type.dtype for type in integer_types}
+    if any(
+        is_nd_advanced_idx(idx, integer_dtypes) or NoneConst.equals(idx)
+        for idx in indices
+    ):
+        return False
 
     # Check that indexing does not act on support dims
-    batched_ndims = rv.ndim - rv_op.ndim_supp
-    if len(st_indices) > batched_ndims:
-        # If the last indexes are just dummy `slice(None)` we discard them
-        st_is_bool = st_is_bool[:batched_ndims]
-        st_indices, supp_indices = (
-            st_indices[:batched_ndims],
-            st_indices[batched_ndims:],
+    batch_ndims = rv.ndim - rv_op.ndim_supp
+    # We decompose the boolean indexes, which makes it clear whether they act on support dims or not
+    non_bool_indices = tuple(
+        chain.from_iterable(
+            idx.nonzero() if is_nd_advanced_idx(idx, "bool") else (idx,)
+            for idx in indices
+        )
+    )
+    if len(non_bool_indices) > batch_ndims:
+        # If the last indexes are just dummy `slice(None)` we discard them instead of quitting
+        non_bool_indices, supp_indices = (
+            non_bool_indices[:batch_ndims],
+            non_bool_indices[batch_ndims:],
         )
-        for index in supp_indices:
+        for idx in supp_indices:
             if not (
-                isinstance(index.type, SliceType)
-                and all(NoneConst.equals(i) for i in index.owner.inputs)
+                isinstance(idx.type, SliceType)
+                and all(NoneConst.equals(i) for i in idx.owner.inputs)
             ):
                 return False
+        n_discarded_idxs = len(supp_indices)
+        indices = indices[:-n_discarded_idxs]
 
     # If no one else is using the underlying `RandomVariable`, then we can
     # do this; otherwise, the graph would be internally inconsistent.
     if is_rv_used_in_graph(rv, node, fgraph):
         return False
 
     # Update the size to reflect the indexed dimensions
-    # TODO: Could use `ShapeFeature` info.  We would need to be sure that
-    # `node` isn't in the results, though.
-    # if hasattr(fgraph, "shape_feature"):
-    #     output_shape = fgraph.shape_feature.shape_of(node.outputs[0])
-    # else:
-    output_shape_ignoring_bool = indexed_result_shape(rv.shape, st_indices)
-    new_size_ignoring_boolean = (
-        output_shape_ignoring_bool
-        if rv_op.ndim_supp == 0
-        else output_shape_ignoring_bool[: -rv_op.ndim_supp]
-    )
+    new_size = output_shape[: len(output_shape) - rv_op.ndim_supp]
 
-    # Boolean indices can actually change the `size` value (compared to just *which* dimensions of `size` are used).
-    # The `indexed_result_shape` helper does not consider this
-    if any(st_is_bool):
-        new_size = tuple(
-            at_sum(idx) if is_bool else s
-            for s, is_bool, idx in zip_longest(
-                new_size_ignoring_boolean, st_is_bool, st_indices, fillvalue=False
-            )
-        )
-    else:
-        new_size = new_size_ignoring_boolean
-
-    # Update the parameters to reflect the indexed dimensions
+    # Propagate indexing to the parameters' batch dims.
+    # We try to avoid broadcasting the parameters together (and with size), by only indexing
+    # non-broadcastable (non-degenerate) parameter dims. These parameters and the new size
+    # should still correctly broadcast any degenerate parameter dims.
     new_dist_params = []
     for param, param_ndim_supp in zip(dist_params, rv_op.ndims_params):
-        # Apply indexing on the batched dimensions of the parameter
-        batched_param_dims_missing = batched_ndims - (param.ndim - param_ndim_supp)
-        batched_param = shape_padleft(param, batched_param_dims_missing)
-        batched_st_indices = []
-        for st_index, batched_param_shape in zip(st_indices, batched_param.type.shape):
-            # If we have a degenerate dimension indexing it should always do the job
-            if batched_param_shape == 1:
-                batched_st_indices.append(0)
+        # We first expand any missing parameter dims (and later index them away or keep them with none-slicing)
+        batch_param_dims_missing = batch_ndims - (param.ndim - param_ndim_supp)
+        batch_param = (
+            shape_padleft(param, batch_param_dims_missing)
+            if batch_param_dims_missing
+            else param
+        )
+        # Check which dims are actually broadcasted
+        bcast_batch_param_dims = tuple(
+            dim
+            for dim, (param_dim, output_dim) in enumerate(
+                zip(batch_param.type.shape, rv.type.shape)
+            )
+            if (param_dim == 1) and (output_dim != 1)
+        )
+        batch_indices = []
+        curr_dim = 0
+        for idx in indices:
+            # Advanced boolean indexing
+            if is_nd_advanced_idx(idx, "bool"):
+                # Check if any broadcasted dim overlaps with advanced boolean indexing.
+                # If not, we use that directly, instead of the more inefficient `nonzero` form
+                bool_dims = range(curr_dim, curr_dim + idx.type.ndim)
+                # There's an overlap, we have to decompose the boolean mask as a `nonzero`
+                if set(bool_dims) & set(bcast_batch_param_dims):
+                    int_indices = list(idx.nonzero())
+                    # Indexing by 0 drops the degenerate dims
+                    for bool_dim in bool_dims:
+                        if bool_dim in bcast_batch_param_dims:
+                            int_indices[bool_dim - curr_dim] = 0
+                    batch_indices.extend(int_indices)
+                # No overlap, use index as is
+                else:
+                    batch_indices.append(idx)
+                curr_dim += len(bool_dims)
+            # Basic-indexing (slice or integer)
             else:
-                batched_st_indices.append(st_index)
-        new_dist_params.append(batched_param[tuple(batched_st_indices)])
+                # Broadcasted dim
+                if curr_dim in bcast_batch_param_dims:
+                    # Slice indexing, keep degenerate dim by none-slicing
+                    if isinstance(idx.type, SliceType):
+                        batch_indices.append(slice(None))
+                    # Integer indexing, drop degenerate dim by 0-indexing
+                    else:
+                        batch_indices.append(0)
+                # Non-broadcasted dim
+                else:
+                    # Use index as is
+                    batch_indices.append(idx)
+                curr_dim += 1
+
+        new_dist_params.append(batch_param[tuple(batch_indices)])
 
     # Create new RV
     new_node = rv_op.make_node(rng, new_size, dtype, *new_dist_params)
     new_rv = new_node.default_output()
 
-    if config.compute_test_value != "off":
-        compute_test_value(new_node)
+    copy_stack_trace(rv, new_rv)
 
     return [new_rv]

pytensor/tensor/rewriting/shape.py

@@ -99,18 +99,6 @@ class ShapeFeature(Feature):
 
     Notes
     -----
-    Right now there is only the ConvOp that could really take
-    advantage of this shape inference, but it is worth it even
-    just for the ConvOp.  All that's necessary to do shape
-    inference is 1) to mark shared inputs as having a particular
-    shape, either via a .tag or some similar hacking; and 2) to
-    add an optional In() argument to promise that inputs will
-    have a certain shape (or even to have certain shapes in
-    certain dimensions).
-
-    We can't automatically infer the shape of shared variables as they can
-    change of shape during the execution by default.
-
     To use this shape information in rewrites, use the
     ``shape_of`` dictionary.