# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 itertools
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import KeyField, StringField
from ...utils import has_unknown_shape
from ..array_utils import as_same_device, device
from ..datasource import tensor as astensor
from ..operands import TensorOperand, TensorOperandMixin
from ..utils import broadcast_shape, unify_chunks
from .broadcast_to import broadcast_to
class TensorCopyTo(TensorOperand, TensorOperandMixin):
_op_type_ = OperandDef.COPYTO
_src = KeyField("src")
_dst = KeyField("dest")
_casting = StringField("casting")
_where = KeyField("where")
def __init__(self, casting=None, **kw):
super().__init__(_casting=casting, **kw)
@property
def src(self):
return self._src
@property
def dst(self):
return self._dst
@property
def casting(self):
return self._casting
@property
def where(self):
return self._where
def check_inputs(self, inputs):
if not 2 <= len(inputs) <= 3:
raise ValueError("inputs' length must be 2 or 3")
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._src = self._inputs[0]
self._dst = self._inputs[1]
if len(self._inputs) > 2:
self._where = self._inputs[2]
@staticmethod
def _extract_inputs(inputs):
if len(inputs) == 2:
(src, dst), where = inputs, None
else:
src, dst, where = inputs
if where is True:
where = None
else:
where = astensor(where)
return src, dst, where
def __call__(self, *inputs):
from ..core import Tensor
src, dst, where = self._extract_inputs(inputs)
if not isinstance(dst, Tensor):
raise TypeError("dst has to be a Tensor")
self.dtype = dst.dtype
self.gpu = dst.op.gpu
self.sparse = dst.issparse()
if not np.can_cast(src.dtype, dst.dtype, casting=self.casting):
raise TypeError(
f"Cannot cast array from {src.dtype!r} to {dst.dtype!r} "
f"according to the rule {self.casting!s}"
)
try:
broadcast_to(src, dst.shape)
except ValueError:
raise ValueError(
"could not broadcast input array "
f"from shape {src.shape!r} into shape {dst.shape!r}"
)
if where:
try:
broadcast_to(where, dst.shape)
except ValueError:
raise ValueError(
"could not broadcast where mask "
f"from shape {src.shape!r} into shape {dst.shape!r}"
)
inps = [src, dst]
if where is not None:
inps.append(where)
ret = self.new_tensor(inps, dst.shape, order=dst.order)
dst.data = ret.data
@classmethod
def tile(cls, op):
if has_unknown_shape(*op.inputs):
yield
inputs = yield from unify_chunks(
*[(input, list(range(input.ndim))[::-1]) for input in op.inputs]
)
output = op.outputs[0]
chunk_shapes = [
t.chunk_shape if hasattr(t, "chunk_shape") else t for t in inputs
]
out_chunk_shape = broadcast_shape(*chunk_shapes)
out_chunks = []
nsplits = [[np.nan] * shape for shape in out_chunk_shape]
get_index = lambda idx, t: tuple(
0 if t.nsplits[i] == (1,) else ix for i, ix in enumerate(idx)
)
for out_idx in itertools.product(*(map(range, out_chunk_shape))):
in_chunks = [
t.cix[get_index(out_idx[-t.ndim :], t)] if t.ndim != 0 else t.chunks[0]
for t in inputs
]
out_chunk = (
op.copy()
.reset_key()
.new_chunk(
in_chunks,
shape=in_chunks[1].shape,
order=output.order,
index=out_idx,
)
)
out_chunks.append(out_chunk)
for i, idx, s in zip(itertools.count(0), out_idx, out_chunk.shape):
nsplits[i][idx] = s
new_op = op.copy()
return new_op.new_tensors(
op.inputs,
output.shape,
order=output.order,
chunks=out_chunks,
nsplits=nsplits,
)
@classmethod
def execute(cls, ctx, op):
inputs, device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
with device(device_id):
dst = inputs[1].copy()
src = inputs[0]
where = inputs[2] if len(inputs) > 2 else True
xp.copyto(dst, src, casting=op.casting, where=where)
ctx[op.outputs[0].key] = dst
[docs]def copyto(dst, src, casting="same_kind", where=True):
"""
Copies values from one array to another, broadcasting as necessary.
Raises a TypeError if the `casting` rule is violated, and if
`where` is provided, it selects which elements to copy.
Parameters
----------
dst : Tensor
The tensor into which values are copied.
src : array_like
The tensor from which values are copied.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when copying.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
where : array_like of bool, optional
A boolean tensor which is broadcasted to match the dimensions
of `dst`, and selects elements to copy from `src` to `dst`
wherever it contains the value True.
"""
op = TensorCopyTo(casting=casting)
return op(src, dst, where)