| | 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 |
| |
|