|
|
import itertools |
|
|
|
|
|
import numpy |
|
|
|
|
|
from cupy import _core |
|
|
from cupy._core import _fusion_interface |
|
|
from cupy._core import fusion |
|
|
from cupy._sorting import search |
|
|
from cupy_backends.cuda.api import runtime |
|
|
|
|
|
|
|
|
def copyto(dst, src, casting='same_kind', where=None): |
|
|
"""Copies values from one array to another with broadcasting. |
|
|
|
|
|
This function can be called for arrays on different devices. In this case, |
|
|
casting, ``where``, and broadcasting is not supported, and an exception is |
|
|
raised if these are used. |
|
|
|
|
|
Args: |
|
|
dst (cupy.ndarray): Target array. |
|
|
src (cupy.ndarray): Source array. |
|
|
casting (str): Casting rule. See :func:`numpy.can_cast` for detail. |
|
|
where (cupy.ndarray of bool): If specified, this array acts as a mask, |
|
|
and an element is copied only if the corresponding element of |
|
|
``where`` is True. |
|
|
|
|
|
.. seealso:: :func:`numpy.copyto` |
|
|
|
|
|
""" |
|
|
src_is_scalar = False |
|
|
src_type = type(src) |
|
|
|
|
|
if src_type in (bool, int, float, complex): |
|
|
dst_arr = numpy.empty((), dtype=dst.dtype) |
|
|
|
|
|
|
|
|
numpy.copyto(dst_arr, src, casting=casting) |
|
|
can_cast = True |
|
|
src_is_scalar = True |
|
|
elif src_type in (fusion._FusionVarScalar, _fusion_interface._ScalarProxy): |
|
|
src_dtype = src.dtype |
|
|
can_cast = numpy.can_cast(src_dtype, dst.dtype, casting) |
|
|
src_is_scalar = True |
|
|
elif isinstance(src, numpy.ndarray) or numpy.isscalar(src): |
|
|
if src.size != 1: |
|
|
raise ValueError( |
|
|
'non-scalar numpy.ndarray cannot be used for copyto') |
|
|
src_dtype = src.dtype |
|
|
can_cast = numpy.can_cast(src, dst.dtype, casting) |
|
|
src = src.item() |
|
|
src_is_scalar = True |
|
|
else: |
|
|
src_dtype = src.dtype |
|
|
can_cast = numpy.can_cast(src_dtype, dst.dtype, casting) |
|
|
|
|
|
if not can_cast: |
|
|
raise TypeError('Cannot cast %s to %s in %s casting mode' % |
|
|
(src_dtype, dst.dtype, casting)) |
|
|
|
|
|
if fusion._is_fusing(): |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if where is None: |
|
|
_core.elementwise_copy(src, dst) |
|
|
else: |
|
|
fusion._call_ufunc(search._where_ufunc, where, src, dst, dst) |
|
|
return |
|
|
|
|
|
if not src_is_scalar: |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if not all([ |
|
|
s in (d, 1) |
|
|
for s, d in itertools.zip_longest( |
|
|
reversed(src.shape), reversed(dst.shape), fillvalue=1) |
|
|
]): |
|
|
raise ValueError( |
|
|
"could not broadcast input array " |
|
|
f"from shape {src.shape} into shape {dst.shape}") |
|
|
squeeze_ndim = src.ndim - dst.ndim |
|
|
if squeeze_ndim > 0: |
|
|
|
|
|
src = src.squeeze(tuple(range(squeeze_ndim))) |
|
|
|
|
|
if where is not None: |
|
|
_core.elementwise_copy(src, dst, _where=where) |
|
|
return |
|
|
|
|
|
if dst.size == 0: |
|
|
return |
|
|
|
|
|
if src_is_scalar: |
|
|
_core.elementwise_copy(src, dst) |
|
|
return |
|
|
|
|
|
if _can_memcpy(dst, src): |
|
|
dst.data.copy_from_async(src.data, src.nbytes) |
|
|
return |
|
|
|
|
|
device = dst.device |
|
|
prev_device = runtime.getDevice() |
|
|
try: |
|
|
runtime.setDevice(device.id) |
|
|
if src.device != device: |
|
|
src = src.copy() |
|
|
_core.elementwise_copy(src, dst) |
|
|
finally: |
|
|
runtime.setDevice(prev_device) |
|
|
|
|
|
|
|
|
def _can_memcpy(dst, src): |
|
|
c_contiguous = dst.flags.c_contiguous and src.flags.c_contiguous |
|
|
f_contiguous = dst.flags.f_contiguous and src.flags.f_contiguous |
|
|
return (c_contiguous or f_contiguous) and dst.dtype == src.dtype and \ |
|
|
dst.size == src.size |
|
|
|