# Data Parallel Control (dpctl)
#
# Copyright 2020-2024 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import dpctl.tensor as dpt
import dpctl.tensor._tensor_impl as ti
import dpctl.tensor._tensor_reductions_impl as tri
import dpctl.utils as du
from ._numpy_helper import normalize_axis_tuple
def _boolean_reduction(x, axis, keepdims, func):
if not isinstance(x, dpt.usm_ndarray):
raise TypeError(f"Expected dpctl.tensor.usm_ndarray, got {type(x)}")
nd = x.ndim
if axis is None:
red_nd = nd
# case of a scalar
if red_nd == 0:
return dpt.astype(x, dpt.bool)
x_tmp = x
res_shape = tuple()
perm = list(range(nd))
else:
if not isinstance(axis, (tuple, list)):
axis = (axis,)
axis = normalize_axis_tuple(axis, nd, "axis")
red_nd = len(axis)
# check for axis=()
if red_nd == 0:
return dpt.astype(x, dpt.bool)
perm = [i for i in range(nd) if i not in axis] + list(axis)
x_tmp = dpt.permute_dims(x, perm)
res_shape = x_tmp.shape[: nd - red_nd]
exec_q = x.sycl_queue
res_usm_type = x.usm_type
_manager = du.SequentialOrderManager[exec_q]
dep_evs = _manager.submitted_events
# always allocate the temporary as
# int32 and usm-device to ensure that atomic updates
# are supported
res_tmp = dpt.empty(
res_shape,
dtype=dpt.int32,
usm_type="device",
sycl_queue=exec_q,
)
hev0, ev0 = func(
src=x_tmp,
trailing_dims_to_reduce=red_nd,
dst=res_tmp,
sycl_queue=exec_q,
depends=dep_evs,
)
_manager.add_event_pair(hev0, ev0)
# copy to boolean result array
res = dpt.empty(
res_shape,
dtype=dpt.bool,
usm_type=res_usm_type,
sycl_queue=exec_q,
)
hev1, ev1 = ti._copy_usm_ndarray_into_usm_ndarray(
src=res_tmp, dst=res, sycl_queue=exec_q, depends=[ev0]
)
_manager.add_event_pair(hev1, ev1)
if keepdims:
res_shape = res_shape + (1,) * red_nd
inv_perm = sorted(range(nd), key=lambda d: perm[d])
res = dpt.permute_dims(dpt.reshape(res, res_shape), inv_perm)
return res
[docs]def all(x, /, *, axis=None, keepdims=False):
"""all(x, axis=None, keepdims=False)
Tests whether all input array elements evaluate to True along a given axis.
Args:
x (usm_ndarray): Input array.
axis (Optional[Union[int, Tuple[int,...]]]): Axis (or axes)
along which to perform a logical AND reduction.
When `axis` is `None`, a logical AND reduction
is performed over all dimensions of `x`.
If `axis` is negative, the axis is counted from
the last dimension to the first.
Default: `None`.
keepdims (bool, optional): If `True`, the reduced axes are included
in the result as singleton dimensions, and the result is
broadcastable to the input array shape.
If `False`, the reduced axes are not included in the result.
Default: `False`.
Returns:
usm_ndarray:
An array with a data type of `bool`
containing the results of the logical AND reduction.
"""
return _boolean_reduction(x, axis, keepdims, tri._all)
[docs]def any(x, /, *, axis=None, keepdims=False):
"""any(x, axis=None, keepdims=False)
Tests whether any input array elements evaluate to True along a given axis.
Args:
x (usm_ndarray): Input array.
axis (Optional[Union[int, Tuple[int,...]]]): Axis (or axes)
along which to perform a logical OR reduction.
When `axis` is `None`, a logical OR reduction
is performed over all dimensions of `x`.
If `axis` is negative, the axis is counted from
the last dimension to the first.
Default: `None`.
keepdims (bool, optional): If `True`, the reduced axes are included
in the result as singleton dimensions, and the result is
broadcastable to the input array shape.
If `False`, the reduced axes are not included in the result.
Default: `False`.
Returns:
usm_ndarray:
An array with a data type of `bool`
containing the results of the logical OR reduction.
"""
return _boolean_reduction(x, axis, keepdims, tri._any)