[API STD][LINALG] Standardize sort & linalg operators

python/mxnet/ndarray/numpy/_op.py

@@ -1590,12 +1590,15 @@ def any(a, axis=None, out=None, keepdims=False):
 
 
 @set_module('mxnet.ndarray.numpy')
-def argsort(a, axis=-1, kind=None, order=None):
+def argsort(a, axis=-1, descending=False, stable=True):
     """
-    Returns the indices that would sort an array.
-    Perform an indirect sort along the given axis using the algorithm specified
-    by the `kind` keyword. It returns an array of indices of the same shape as
-    `a` that index data along the given axis in sorted order.
+    Returns the indices that sort an array `x` along a specified axis.
+
+    Notes
+    -----
+    `argsort` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/sorting_functions.html#argsort-x-axis-1-descending-false-stable-true
+    instead of an official NumPy operator.
 
     Parameters
     ----------
@@ -1604,11 +1607,13 @@ def argsort(a, axis=-1, kind=None, order=None):
     axis : int or None, optional
         Axis along which to sort.  The default is -1 (the last axis). If None,
         the flattened array is used.
-    kind : string, optional
-        This argument can take any string, but it does not have any effect on the
-        final result.
-    order : str or list of str, optional
-        Not supported yet, will raise NotImplementedError if not None.
+    descending : bool, optional
+        sort order. If `True`, the returned indices sort x in descending order (by value).
+        If `False`, the returned indices sort x in ascending order (by value).Default: False.
+    stable : bool, optional
+        sort stability. If `True`, the returned indices must maintain the relative order
+        of x values which compare as equal. If `False`, the returned indices may or may not
+        maintain the relative order of x values which compare as equal. Default: True.
 
     Returns
     -------
@@ -1659,29 +1664,34 @@ def argsort(a, axis=-1, kind=None, order=None):
     >>> x[ind]  # same as np.sort(x, axis=None)
     array([0, 2, 2, 3])
     """
-    if order is not None:
-        raise NotImplementedError("order not supported here")
-
-    return _api_internal.argsort(a, axis, True, 'int64')
+    return _api_internal.argsort(a, axis, not descending, 'int64')
 
 
 @set_module('mxnet.ndarray.numpy')
-def sort(a, axis=-1, kind=None, order=None):
+def sort(a, axis=-1, descending=False, stable=True):
     """
     Return a sorted copy of an array.
 
+    Notes
+    -----
+    `sort` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/sorting_functions.html#sort-x-axis-1-descending-false-stable-true
+    instead of an official NumPy operator.
+
     Parameters
     ----------
     a : ndarray
-        Array to be sorted.
+        Array to sort.
     axis : int or None, optional
         Axis along which to sort.  The default is -1 (the last axis). If None,
         the flattened array is used.
-    kind : string, optional
-        This argument can take any string, but it does not have any effect on the
-        final result.
-    order : str or list of str, optional
-        Not supported yet, will raise NotImplementedError if not None.
+    descending : bool, optional
+        sort order. If `True`, the returned indices sort x in descending order (by value).
+        If `False`, the returned indices sort x in ascending order (by value).Default: False.
+    stable : bool, optional
+        sort stability. If `True`, the returned indices must maintain the relative order
+        of x values which compare as equal. If `False`, the returned indices may or may not
+        maintain the relative order of x values which compare as equal. Default: True.
 
     Returns
     -------
@@ -1704,9 +1714,7 @@ def sort(a, axis=-1, kind=None, order=None):
     array([[1, 1],
            [3, 4]])
     """
-    if order is not None:
-        raise NotImplementedError("order not supported here")
-    return _api_internal.sort(a, axis, True)
+    return _api_internal.sort(a, axis, not descending)
 
 @set_module('mxnet.ndarray.numpy')
 def dot(a, b, out=None):

python/mxnet/ndarray/numpy/linalg.py

@@ -450,10 +450,16 @@ def svd(a):
     return tuple(_api_internal.svd(a))
 
 
-def cholesky(a):
+def cholesky(a, upper=False):
     r"""
     Cholesky decomposition.
 
+    Notes
+    -----
+    `upper` param is requested by API standardization in
+    https://data-apis.org/array-api/latest/extensions/linear_algebra_functions.html#linalg-cholesky-x-upper-false
+    instead of parameter in official NumPy operator.
+
     Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
     where `L` is lower-triangular and .T is the transpose operator. `a` must be
     symmetric and positive-definite. Only `L` is actually returned. Complex-valued
@@ -463,6 +469,10 @@ def cholesky(a):
     ----------
     a : (..., M, M) ndarray
         Symmetric, positive-definite input matrix.
+    upper : bool
+        If `True`, the result must be the upper-triangular Cholesky factor.
+        If `False`, the result must be the lower-triangular Cholesky factor.
+        Default: `False`.
 
     Returns
     -------
@@ -506,7 +516,7 @@ def cholesky(a):
     array([[16.,  4.],
            [ 4., 10.]])
     """
-    return _api_internal.cholesky(a, True)
+    return _api_internal.cholesky(a, not upper)
 
 
 def qr(a, mode='reduced'):

python/mxnet/numpy/linalg.py

@@ -28,27 +28,28 @@
 __all__ += fallback_linalg.__all__
 
 
-def matrix_rank(M, tol=None, hermitian=False):
+@wrap_data_api_linalg_func
+def matrix_rank(M, rtol=None, hermitian=False):
     r"""
     Return matrix rank of array using SVD method
 
     Rank of the array is the number of singular values of the array that are
-    greater than `tol`.
+    greater than `rtol`.
 
     Notes
     -----
-    `matrix_rank` is an alias for `matrix_rank`. It is a standard API in
+    `rtol` param is requested in array-api-standard in
     https://data-apis.org/array-api/latest/extensions/linear_algebra_functions.html#linalg-matrix-rank-x-rtol-none
-    instead of an official NumPy operator.
+    instead of a parameter in official NumPy operator.
 
     Parameters
     ----------
     M : {(M,), (..., M, N)} ndarray
         Input vector or stack of matrices.
-    tol : (...) ndarray, float, optional
-        Threshold below which SVD values are considered zero. If `tol` is
+    rtol : (...) ndarray, float, optional
+        Threshold below which SVD values are considered zero. If `rtol` is
         None, and ``S`` is an array with singular values for `M`, and
-        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        ``eps`` is the epsilon value for datatype of ``S``, then `rtol` is
         set to ``S.max() * max(M.shape) * eps``.
     hermitian : bool, optional
         If True, `M` is assumed to be Hermitian (symmetric if real-valued),
@@ -73,7 +74,7 @@ def matrix_rank(M, tol=None, hermitian=False):
     >>> np.linalg.matrix_rank(np.zeros((4,)))
     0
     """
-    return _mx_nd_np.linalg.matrix_rank(M, tol, hermitian)
+    return _mx_nd_np.linalg.matrix_rank(M, rtol, hermitian)
 
 
 def matrix_transpose(a):
@@ -502,22 +503,29 @@ def lstsq(a, b, rcond='warn'):
     return _mx_nd_np.linalg.lstsq(a, b, rcond)
 
 
-def pinv(a, rcond=1e-15, hermitian=False):
+@wrap_data_api_linalg_func
+def pinv(a, rtol=None, hermitian=False):
     r"""
     Compute the (Moore-Penrose) pseudo-inverse of a matrix.
 
     Calculate the generalized inverse of a matrix using its
     singular-value decomposition (SVD) and including all
     *large* singular values.
 
+    Notes
+    -----
+    `rtol` param is requested in array-api-standard in
+    https://data-apis.org/array-api/latest/extensions/linear_algebra_functions.html#linalg-pinv-x-rtol-none
+    instead of a parameter in official NumPy operator.
+
     Parameters
     ----------
     a : (..., M, N) ndarray
         Matrix or stack of matrices to be pseudo-inverted.
-    rcond : (...) {float or ndarray of float}, optional
+    rtol : (...) {float or ndarray of float}, optional
         Cutoff for small singular values.
         Singular values less than or equal to
-        ``rcond * largest_singular_value`` are set to zero.
+        ``rtol * largest_singular_value`` are set to zero.
         Broadcasts against the stack of matrices.
     hermitian : bool, optional
         If True, `a` is assumed to be Hermitian (symmetric if real-valued),
@@ -567,7 +575,7 @@ def pinv(a, rcond=1e-15, hermitian=False):
     >>> (pinv_a - np.dot(pinv_a, np.dot(a, pinv_a))).sum()
     array(0.)
     """
-    return _mx_nd_np.linalg.pinv(a, rcond, hermitian)
+    return _mx_nd_np.linalg.pinv(a, rtol, hermitian)
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
@@ -732,10 +740,16 @@ def svdvals(a):
     return s
 
 
-def cholesky(a):
+def cholesky(a, upper=False):
     r"""
     Cholesky decomposition.
 
+    Notes
+    -----
+    `upper` param is requested by API standardization in
+    https://data-apis.org/array-api/latest/extensions/linear_algebra_functions.html#linalg-cholesky-x-upper-false
+    instead of parameter in official NumPy operator.
+
     Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
     where `L` is lower-triangular and .T is the transpose operator. `a` must be
     symmetric and positive-definite. Only `L` is actually returned. Complex-valued
@@ -745,6 +759,10 @@ def cholesky(a):
     ----------
     a : (..., M, M) ndarray
         Symmetric, positive-definite input matrix.
+    upper : bool
+        If `True`, the result must be the upper-triangular Cholesky factor.
+        If `False`, the result must be the lower-triangular Cholesky factor.
+        Default: `False`.
 
     Returns
     -------
@@ -788,7 +806,7 @@ def cholesky(a):
     array([[16.,  4.],
            [ 4., 10.]])
     """
-    return _mx_nd_np.linalg.cholesky(a)
+    return _mx_nd_np.linalg.cholesky(a, upper)
 
 
 def qr(a, mode='reduced'):

python/mxnet/numpy/multiarray.py

@@ -45,7 +45,8 @@
 from ..runtime import Features
 from ..context import Context
 from ..util import set_module, wrap_np_unary_func, wrap_np_binary_func,\
-                   is_np_default_dtype, wrap_data_api_statical_func
+                   is_np_default_dtype, wrap_data_api_statical_func,\
+                   wrap_sort_functions
 from ..context import current_context
 from ..ndarray import numpy as _mx_nd_np
 from ..ndarray.numpy import _internal as _npi
@@ -1908,13 +1909,13 @@ def pick(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute pick')
 
-    def sort(self, axis=-1, kind=None, order=None):  # pylint: disable=arguments-differ
+    def sort(self, axis=-1, descending=False, stable=True):  # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`sort`.
 
         The arguments are the same as for :py:func:`sort`, with
         this array as data.
         """
-        raise sort(self, axis=axis, kind=kind, order=order)
+        return sort(self, axis=axis, descending=descending, stable=stable)
 
     def topk(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`topk`.
@@ -1924,13 +1925,13 @@ def topk(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute topk')
 
-    def argsort(self, axis=-1, kind=None, order=None):  # pylint: disable=arguments-differ
+    def argsort(self, axis=-1, descending=False, stable=True):  # pylint: disable=arguments-differ
         """Convenience fluent method for :py:func:`argsort`.
 
         The arguments are the same as for :py:func:`argsort`, with
         this array as data.
         """
-        return argsort(self, axis=axis, kind=kind, order=order)
+        return argsort(self, axis=axis, descending=descending, stable=stable)
 
     def argmax_channel(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`argmax_channel`.
@@ -5831,12 +5832,16 @@ def arctanh(x, out=None, **kwargs):
 
 
 @set_module('mxnet.numpy')
-def argsort(a, axis=-1, kind=None, order=None):
+@wrap_sort_functions
+def argsort(a, axis=-1, descending=False, stable=True):
     """
-    Returns the indices that would sort an array.
-    Perform an indirect sort along the given axis using the algorithm specified
-    by the `kind` keyword. It returns an array of indices of the same shape as
-    `a` that index data along the given axis in sorted order.
+    Returns the indices that sort an array `x` along a specified axis.
+
+    Notes
+    -----
+    `argsort` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/sorting_functions.html#argsort-x-axis-1-descending-false-stable-true
+    instead of an official NumPy operator.
 
     Parameters
     ----------
@@ -5845,11 +5850,13 @@ def argsort(a, axis=-1, kind=None, order=None):
     axis : int or None, optional
         Axis along which to sort.  The default is -1 (the last axis). If None,
         the flattened array is used.
-    kind : string, optional
-        This argument can take any string, but it does not have any effect on the
-        final result.
-    order : str or list of str, optional
-        Not supported yet, will raise NotImplementedError if not None.
+    descending : bool, optional
+        sort order. If `True`, the returned indices sort x in descending order (by value).
+        If `False`, the returned indices sort x in ascending order (by value).Default: False.
+    stable : bool, optional
+        sort stability. If `True`, the returned indices must maintain the relative order
+        of x values which compare as equal. If `False`, the returned indices may or may not
+        maintain the relative order of x values which compare as equal. Default: True.
 
     Returns
     -------
@@ -5900,26 +5907,37 @@ def argsort(a, axis=-1, kind=None, order=None):
     >>> x[ind]  # same as np.sort(x, axis=None)
     array([0, 2, 2, 3])
     """
-    return _mx_nd_np.argsort(a, axis=axis, kind=kind, order=order)
+    if stable:
+        warnings.warn("Currently, MXNet only support quicksort in backend, which is not stable")
+    return _mx_nd_np.argsort(a, axis=axis, descending=descending)
 
 
 @set_module('mxnet.numpy')
-def sort(a, axis=-1, kind=None, order=None):
+@wrap_sort_functions
+def sort(a, axis=-1, descending=False, stable=True):
     """
     Return a sorted copy of an array.
 
+    Notes
+    -----
+    `sort` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/sorting_functions.html#sort-x-axis-1-descending-false-stable-true
+    instead of an official NumPy operator.
+
     Parameters
     ----------
     a : ndarray
-        Array to be sorted.
+        Array to sort.
     axis : int or None, optional
         Axis along which to sort.  The default is -1 (the last axis). If None,
         the flattened array is used.
-    kind : string, optional
-        This argument can take any string, but it does not have any effect on the
-        final result.
-    order : str or list of str, optional
-        Not supported yet, will raise NotImplementedError if not None.
+    descending : bool, optional
+        sort order. If `True`, the returned indices sort x in descending order (by value).
+        If `False`, the returned indices sort x in ascending order (by value).Default: False.
+    stable : bool, optional
+        sort stability. If `True`, the returned indices must maintain the relative order
+        of x values which compare as equal. If `False`, the returned indices may or may not
+        maintain the relative order of x values which compare as equal. Default: True.
 
     Returns
     -------
@@ -5942,7 +5960,7 @@ def sort(a, axis=-1, kind=None, order=None):
     array([[1, 1],
            [3, 4]])
     """
-    return _mx_nd_np.sort(a, axis=axis, kind=kind, order=order)
+    return _mx_nd_np.sort(a, axis=axis, descending=descending)
 
 
 @set_module('mxnet.numpy')