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add numpy op full_like, c++ impl, fix zeros_like, ones_like type infe… #16804

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add numpy op full_like, c++ impl, fix zeros_like, ones_like type infe… #16804

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71 changes: 0 additions & 71 deletions python/mxnet/_numpy_op_doc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,77 +20,6 @@
"""Doc placeholder for numpy ops with prefix _np."""


def _np_ones_like(a):
"""
Return an array of ones with the same shape and type as a given array.

Parameters
----------
a : ndarray
The shape and data-type of `a` define these same attributes of
the returned array.

Returns
-------
out : ndarray
Array of ones with the same shape and type as `a`.

Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0., 1., 2.],
[3., 4., 5.]])
>>> np.ones_like(x)
array([[1., 1., 1.],
[1., 1., 1.]])

>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.], dtype=float64)
>>>
>>> np.ones_like(y)
array([1., 1., 1.], dtype=float64)
"""
pass


def _np_zeros_like(a):
"""
Return an array of zeros with the same shape and type as a given array.

Parameters
----------
a : ndarray
The shape and data-type of `a` define these same attributes of
the returned array.

Returns
-------
out : ndarray
Array of zeros with the same shape and type as `a`.

Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0., 1., 2.],
[3., 4., 5.]])
>>> np.zeros_like(x)
array([[0., 0., 0.],
[0., 0., 0.]])
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.], dtype=float64)
>>>
>>> np.zeros_like(y)
array([0., 0., 0.], dtype=float64)
"""
pass


def _np_cumsum(a, axis=None, dtype=None, out=None):
"""
Return the cumulative sum of the elements along a given axis.
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175 changes: 173 additions & 2 deletions python/mxnet/ndarray/numpy/_op.py
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Expand Up @@ -28,7 +28,8 @@
from . import _internal as _npi
from ..ndarray import NDArray

__all__ = ['shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
__all__ = ['shape', 'zeros', 'zeros_like', 'ones', 'ones_like', 'full', 'full_like',
'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
Expand Down Expand Up @@ -145,7 +146,119 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None): # pylint: disable=rede

# pylint: disable=too-many-arguments, redefined-outer-name
@set_module('mxnet.ndarray.numpy')
def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
def zeros_like(a, dtype=None, order='C', ctx=None, out=None):
"""
Return an array of zeros with the same shape and type as a given array.

Parameters
----------
a : ndarray
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
Temporarily do not support boolean type.
order : {'C'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. Currently only supports C order.
ctx: to specify the device, e.g. the i-th GPU.
out : ndarray or None, optional
A location into which the result is stored.
If provided, it must have the same shape and dtype as input ndarray.
If not provided or `None`, a freshly-allocated array is returned.

Returns
-------
out : ndarray
Array of zeros with the same shape and type as a.

See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full : Return a new array of given shape filled with value.

Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0., 1., 2.],
[3., 4., 5.]])
>>> np.zeros_like(x)
array([[0., 0., 0.],
[0., 0., 0.]])
>>> np.zeros_like(x, int)
array([[0, 0, 0],
[0, 0, 0]], dtype=int64)
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.], dtype=float64)
>>> np.zeros_like(y)
array([0., 0., 0.], dtype=float64)
"""
return _npi.full_like(a, fill_value=0, dtype=dtype, ctx=None, out=None)


@set_module('mxnet.ndarray.numpy')
def ones_like(a, dtype=None, order='C', ctx=None, out=None):
"""
Return an array of ones with the same shape and type as a given array.

Parameters
----------
a : ndarray
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
Temporarily do not support boolean type.
order : {'C'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. Currently only supports C order.
ctx: to specify the device, e.g. the i-th GPU.
out : ndarray or None, optional
A location into which the result is stored.
If provided, it must have the same shape and dtype as input ndarray.
If not provided or `None`, a freshly-allocated array is returned.

Returns
-------
out : ndarray
Array of ones with the same shape and type as a.

See Also
--------
empty_like : Return an empty array with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
ones : Return a new array setting values to one.

Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0., 1., 2.],
[3., 4., 5.]])
>>> np.ones_like(x)
array([[1., 1., 1.],
[1., 1., 1.]])
>>> np.ones_like(x, int)
array([[1, 1, 1],
[1, 1, 1]], dtype=int64)
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.], dtype=float64)
>>> np.ones_like(y)
array([1., 1., 1.], dtype=float64)
"""
return _npi.full_like(a, fill_value=1, dtype=dtype, ctx=None, out=None)


@set_module('mxnet.ndarray.numpy')
def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None): # pylint: disable=too-many-arguments
"""
Return a new array of given shape and type, filled with `fill_value`.
Parameters
Expand Down Expand Up @@ -200,6 +313,64 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
# pylint: enable=too-many-arguments, redefined-outer-name


@set_module('mxnet.ndarray.numpy')
def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pylint: disable=too-many-arguments
"""
Return a full array with the same shape and type as a given array.

Parameters
----------
a : ndarray
The shape and data-type of `a` define these same attributes of
the returned array.
fill_value : scalar
Fill value.
dtype : data-type, optional
Overrides the data type of the result.
Temporarily do not support boolean type.
order : {'C'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. Currently only supports C order.
ctx: to specify the device, e.g. the i-th GPU.
out : ndarray or None, optional
A location into which the result is stored.
If provided, it must have the same shape and dtype as input ndarray.
If not provided or `None`, a freshly-allocated array is returned.

Returns
-------
out : ndarray
Array of `fill_value` with the same shape and type as `a`.

See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full : Return a new array of given shape filled with value.

Examples
--------
>>> x = np.arange(6, dtype=int)
>>> np.full_like(x, 1)
array([1, 1, 1, 1, 1, 1], dtype=int64)
>>> np.full_like(x, 0.1)
array([0, 0, 0, 0, 0, 0], dtype=int64)
>>> np.full_like(x, 0.1, dtype=np.float64)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1], dtype=float64)
>>> np.full_like(x, np.nan, dtype=np.double)
array([nan, nan, nan, nan, nan, nan], dtype=float64)
>>> y = np.arange(6, dtype=np.float32)
>>> np.full_like(y, 0.1)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
"""
if order != 'C':
raise NotImplementedError
if ctx is None:
ctx = current_context()
return _npi.full_like(a, fill_value=fill_value, dtype=dtype, ctx=ctx, out=out)


@set_module('mxnet.ndarray.numpy')
def arange(start, stop=None, step=1, dtype=None, ctx=None):
"""Return evenly spaced values within a given interval.
Expand Down
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