File size: 5,108 Bytes
995244d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import numpy
from cupy import cuda
from cupy._creation.basic import _new_like_order_and_strides
from cupy._core import internal
def _update_shape(a, shape):
if shape is None and a is not None:
shape = a.shape
elif isinstance(shape, int):
shape = (shape,)
else:
shape = tuple(shape)
return shape
def empty_pinned(shape, dtype=float, order='C'):
"""Returns a new, uninitialized NumPy array with the given shape
and dtype.
This is a convenience function which is just :func:`numpy.empty`,
except that the underlying memory is pinned/pagelocked.
Args:
shape (int or tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
order ({'C', 'F'}): Row-major (C-style) or column-major
(Fortran-style) order.
Returns:
numpy.ndarray: A new array with elements not initialized.
.. seealso:: :func:`numpy.empty`
"""
shape = _update_shape(None, shape)
nbytes = internal.prod(shape) * numpy.dtype(dtype).itemsize
mem = cuda.alloc_pinned_memory(nbytes)
out = numpy.ndarray(shape, dtype=dtype, buffer=mem, order=order)
return out
def empty_like_pinned(a, dtype=None, order='K', subok=None, shape=None):
"""Returns a new, uninitialized NumPy array with the same shape and dtype
as those of the given array.
This is a convenience function which is just :func:`numpy.empty_like`,
except that the underlying memory is pinned/pagelocked.
This function currently does not support ``subok`` option.
Args:
a (numpy.ndarray or cupy.ndarray): Base array.
dtype: Data type specifier. The data type of ``a`` is used by default.
order ({'C', 'F', 'A', or 'K'}): Overrides the memory layout of the
result. ``'C'`` means C-order, ``'F'`` means F-order, ``'A'`` means
``'F'`` if ``a`` is Fortran contiguous, ``'C'`` otherwise.
``'K'`` means match the layout of ``a`` as closely as possible.
subok: Not supported yet, must be None.
shape (int or tuple of ints): Overrides the shape of the result. If
``order='K'`` and the number of dimensions is unchanged, will try
to keep order, otherwise, ``order='C'`` is implied.
Returns:
numpy.ndarray: A new array with same shape and dtype of ``a`` with
elements not initialized.
.. seealso:: :func:`numpy.empty_like`
"""
# We're kinda duplicating the code here because order='K' needs special
# treatment: strides need to be computed
if subok is not None:
raise TypeError('subok is not supported yet')
if dtype is None:
dtype = a.dtype
shape = _update_shape(a, shape)
order, strides, _ = _new_like_order_and_strides(
a, dtype, order, shape, get_memptr=False)
nbytes = internal.prod(shape) * numpy.dtype(dtype).itemsize
mem = cuda.alloc_pinned_memory(nbytes)
out = numpy.ndarray(shape, dtype=dtype, buffer=mem,
strides=strides, order=order)
return out
def zeros_pinned(shape, dtype=float, order='C'):
"""Returns a new, zero-initialized NumPy array with the given shape
and dtype.
This is a convenience function which is just :func:`numpy.zeros`,
except that the underlying memory is pinned/pagelocked.
Args:
shape (int or tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
order ({'C', 'F'}): Row-major (C-style) or column-major
(Fortran-style) order.
Returns:
numpy.ndarray: An array filled with zeros.
.. seealso:: :func:`numpy.zeros`
"""
out = empty_pinned(shape, dtype, order)
numpy.copyto(out, 0, casting='unsafe')
return out
def zeros_like_pinned(a, dtype=None, order='K', subok=None, shape=None):
"""Returns a new, zero-initialized NumPy array with the same shape and dtype
as those of the given array.
This is a convenience function which is just :func:`numpy.zeros_like`,
except that the underlying memory is pinned/pagelocked.
This function currently does not support ``subok`` option.
Args:
a (numpy.ndarray or cupy.ndarray): Base array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
order ({'C', 'F', 'A', or 'K'}): Overrides the memory layout of the
result. ``'C'`` means C-order, ``'F'`` means F-order, ``'A'`` means
``'F'`` if ``a`` is Fortran contiguous, ``'C'`` otherwise.
``'K'`` means match the layout of ``a`` as closely as possible.
subok: Not supported yet, must be None.
shape (int or tuple of ints): Overrides the shape of the result. If
``order='K'`` and the number of dimensions is unchanged, will try
to keep order, otherwise, ``order='C'`` is implied.
Returns:
numpy.ndarray: An array filled with zeros.
.. seealso:: :func:`numpy.zeros_like`
""" # NOQA
out = empty_like_pinned(a, dtype, order, subok, shape)
numpy.copyto(out, 0, casting='unsafe')
return out
|