use dpctl.tensor.sign and dpctl.tensor.negative in dpnp

dpnp/backend/include/dpnp_iface_fptr.hpp

@@ -282,8 +282,6 @@ enum class DPNPFuncName : size_t
     DPNP_FN_NANVAR_EXT,    /**< Used in numpy.nanvar() impl, requires extra
                               parameters */
     DPNP_FN_NEGATIVE,      /**< Used in numpy.negative() impl  */
-    DPNP_FN_NEGATIVE_EXT,  /**< Used in numpy.negative() impl, requires extra
-                              parameters */
     DPNP_FN_NONZERO,       /**< Used in numpy.nonzero() impl  */
     DPNP_FN_NOT_EQUAL_EXT, /**< Used in numpy.not_equal() impl, requires extra
                               parameters */
@@ -441,10 +439,8 @@ enum class DPNPFuncName : size_t
     DPNP_FN_SEARCHSORTED_EXT, /**< Used in numpy.searchsorted() impl, requires
                                  extra parameters */
     DPNP_FN_SIGN,             /**< Used in numpy.sign() impl  */
-    DPNP_FN_SIGN_EXT, /**< Used in numpy.sign() impl, requires extra parameters
-                       */
-    DPNP_FN_SIN,      /**< Used in numpy.sin() impl  */
-    DPNP_FN_SINH,     /**< Used in numpy.sinh() impl  */
+    DPNP_FN_SIN,              /**< Used in numpy.sin() impl  */
+    DPNP_FN_SINH,             /**< Used in numpy.sinh() impl  */
     DPNP_FN_SINH_EXT, /**< Used in numpy.sinh() impl, requires extra parameters
                        */
     DPNP_FN_SORT,     /**< Used in numpy.sort() impl  */

dpnp/backend/kernels/dpnp_krnl_elemwise.cpp

@@ -1094,15 +1094,6 @@ static void func_map_init_elemwise_1arg_1type(func_map_t &fmap)
     fmap[DPNPFuncName::DPNP_FN_NEGATIVE][eft_DBL][eft_DBL] = {
         eft_DBL, (void *)dpnp_negative_c_default<double>};
 
-    fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_INT][eft_INT] = {
-        eft_INT, (void *)dpnp_negative_c_ext<int32_t>};
-    fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_LNG][eft_LNG] = {
-        eft_LNG, (void *)dpnp_negative_c_ext<int64_t>};
-    fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_FLT][eft_FLT] = {
-        eft_FLT, (void *)dpnp_negative_c_ext<float>};
-    fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_DBL][eft_DBL] = {
-        eft_DBL, (void *)dpnp_negative_c_ext<double>};
-
     fmap[DPNPFuncName::DPNP_FN_RECIP][eft_INT][eft_INT] = {
         eft_INT, (void *)dpnp_recip_c_default<int32_t>};
     fmap[DPNPFuncName::DPNP_FN_RECIP][eft_LNG][eft_LNG] = {
@@ -1130,15 +1121,6 @@ static void func_map_init_elemwise_1arg_1type(func_map_t &fmap)
     fmap[DPNPFuncName::DPNP_FN_SIGN][eft_DBL][eft_DBL] = {
         eft_DBL, (void *)dpnp_sign_c_default<double>};
 
-    fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_INT][eft_INT] = {
-        eft_INT, (void *)dpnp_sign_c_ext<int32_t>};
-    fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_LNG][eft_LNG] = {
-        eft_LNG, (void *)dpnp_sign_c_ext<int64_t>};
-    fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_FLT][eft_FLT] = {
-        eft_FLT, (void *)dpnp_sign_c_ext<float>};
-    fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_DBL][eft_DBL] = {
-        eft_DBL, (void *)dpnp_sign_c_ext<double>};
-
     fmap[DPNPFuncName::DPNP_FN_SQUARE][eft_INT][eft_INT] = {
         eft_INT, (void *)dpnp_square_c_default<int32_t>};
     fmap[DPNPFuncName::DPNP_FN_SQUARE][eft_LNG][eft_LNG] = {

dpnp/dpnp_algo/dpnp_algo.pxd

@@ -162,8 +162,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName":  # need this na
         DPNP_FN_MODF_EXT
         DPNP_FN_NANVAR
         DPNP_FN_NANVAR_EXT
-        DPNP_FN_NEGATIVE
-        DPNP_FN_NEGATIVE_EXT
         DPNP_FN_NONZERO
         DPNP_FN_ONES
         DPNP_FN_ONES_LIKE
@@ -259,8 +257,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName":  # need this na
         DPNP_FN_RNG_ZIPF_EXT
         DPNP_FN_SEARCHSORTED
         DPNP_FN_SEARCHSORTED_EXT
-        DPNP_FN_SIGN
-        DPNP_FN_SIGN_EXT
         DPNP_FN_SINH
         DPNP_FN_SINH_EXT
         DPNP_FN_SORT
@@ -429,7 +425,6 @@ cpdef dpnp_descriptor dpnp_maximum(dpnp_descriptor x1_obj, dpnp_descriptor x2_ob
                                    dpnp_descriptor out=*, object where=*)
 cpdef dpnp_descriptor dpnp_minimum(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
                                    dpnp_descriptor out=*, object where=*)
-cpdef dpnp_descriptor dpnp_negative(dpnp_descriptor array1)
 cpdef dpnp_descriptor dpnp_power(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
                                  dpnp_descriptor out=*, object where=*)
 

dpnp/dpnp_algo/dpnp_algo_mathematical.pxi

@@ -56,10 +56,8 @@ __all__ += [
     "dpnp_nancumsum",
     "dpnp_nanprod",
     "dpnp_nansum",
-    "dpnp_negative",
     "dpnp_power",
     "dpnp_prod",
-    "dpnp_sign",
     "dpnp_sum",
     "dpnp_trapz",
 ]
@@ -455,10 +453,6 @@ cpdef utils.dpnp_descriptor dpnp_nansum(utils.dpnp_descriptor x1):
     return dpnp_sum(result)
 
 
-cpdef utils.dpnp_descriptor dpnp_negative(dpnp_descriptor x1):
-    return call_fptr_1in_1out_strides(DPNP_FN_NEGATIVE_EXT, x1)
-
-
 cpdef utils.dpnp_descriptor dpnp_power(utils.dpnp_descriptor x1_obj,
                                        utils.dpnp_descriptor x2_obj,
                                        object dtype=None,
@@ -529,10 +523,6 @@ cpdef utils.dpnp_descriptor dpnp_prod(utils.dpnp_descriptor x1,
     return result
 
 
-cpdef utils.dpnp_descriptor dpnp_sign(utils.dpnp_descriptor x1):
-    return call_fptr_1in_1out_strides(DPNP_FN_SIGN_EXT, x1)
-
-
 cpdef utils.dpnp_descriptor dpnp_sum(utils.dpnp_descriptor x1,
                                      object axis=None,
                                      object dtype=None,

dpnp/dpnp_algo/dpnp_elementwise_common.py

@@ -1390,6 +1390,42 @@ def dpnp_multiply(x1, x2, out=None, order="K"):
     return dpnp_array._create_from_usm_ndarray(res_usm)
 
 
+_negative_docstring = """
+negative(x, out=None, order="K")
+
+Computes the numerical negative for each element `x_i` of input array `x`.
+
+Args:
+    x (dpnp.ndarray):
+        Input array, expected to have numeric data type.
+    out ({None, dpnp.ndarray}, optional):
+        Output array to populate.
+        Array have the correct shape and the expected data type.
+    order ("C","F","A","K", optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    dpnp.ndarray:
+        An array containing the negative of `x`.
+"""
+
+
+negative_func = UnaryElementwiseFunc(
+    "negative", ti._negative_result_type, ti._negative, _negative_docstring
+)
+
+
+def dpnp_negative(x, out=None, order="K"):
+    """Invokes negative() from dpctl.tensor implementation for negative() function."""
+
+    # dpctl.tensor only works with usm_ndarray
+    x1_usm = dpnp.get_usm_ndarray(x)
+    out_usm = None if out is None else dpnp.get_usm_ndarray(out)
+
+    res_usm = negative_func(x1_usm, out=out_usm, order=order)
+    return dpnp_array._create_from_usm_ndarray(res_usm)
+
+
 _not_equal_docstring_ = """
 not_equal(x1, x2, out=None, order="K")
 
@@ -1525,6 +1561,47 @@ def dpnp_right_shift(x1, x2, out=None, order="K"):
     return dpnp_array._create_from_usm_ndarray(res_usm)
 
 
+_sign_docstring = """
+sign(x, out=None, order="K")
+
+Computes an indication of the sign of each element `x_i` of input array `x`
+using the signum function.
+
+The signum function returns `-1` if `x_i` is less than `0`,
+`0` if `x_i` is equal to `0`, and `1` if `x_i` is greater than `0`.
+
+Args:
+    x (dpnp.ndarray):
+        Input array, expected to have numeric data type.
+    out ({None, dpnp.ndarray}, optional):
+        Output array to populate.
+        Array have the correct shape and the expected data type.
+    order ("C","F","A","K", optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    dpnp.ndarray:
+        An array containing the element-wise results. The data type of the
+        returned array is determined by the Type Promotion Rules.
+"""
+
+
+sign_func = UnaryElementwiseFunc(
+    "sign", ti._sign_result_type, ti._sign, _sign_docstring
+)
+
+
+def dpnp_sign(x, out=None, order="K"):
+    """Invokes sign() from dpctl.tensor implementation for sign() function."""
+
+    # dpctl.tensor only works with usm_ndarray
+    x1_usm = dpnp.get_usm_ndarray(x)
+    out_usm = None if out is None else dpnp.get_usm_ndarray(out)
+
+    res_usm = sign_func(x1_usm, out=out_usm, order=order)
+    return dpnp_array._create_from_usm_ndarray(res_usm)
+
+
 _sin_docstring = """
 sin(x, out=None, order='K')
 Computes sine for each element `x_i` of input array `x`.

dpnp/dpnp_iface_mathematical.py

@@ -57,7 +57,9 @@
     dpnp_floor,
     dpnp_floor_divide,
     dpnp_multiply,
+    dpnp_negative,
     dpnp_remainder,
+    dpnp_sign,
     dpnp_subtract,
     dpnp_trunc,
 )
@@ -1486,39 +1488,64 @@ def nansum(x1, **kwargs):
     return call_origin(numpy.nansum, x1, **kwargs)
 
 
-def negative(x1, **kwargs):
+def negative(
+    x,
+    /,
+    out=None,
+    *,
+    order="K",
+    where=True,
+    dtype=None,
+    subok=True,
+    **kwargs,
+):
     """
     Negative element-wise.
 
     For full documentation refer to :obj:`numpy.negative`.
 
+    Returns
+    -------
+    out : dpnp.ndarray
+        The numerical negative of each element of `x`.
+
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.ndarray`.
+    Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
+    Parameters `where`, `dtype` and `subok` are supported with their default values.
     Keyword arguments `kwargs` are currently unsupported.
     Otherwise the function will be executed sequentially on CPU.
     Input array data types are limited by supported DPNP :ref:`Data types`.
 
     See Also
     --------
-    :obj:`dpnp.copysign` : Change the sign of x1 to that of x2, element-wise.
+    :obj:`dpnp.copysign` : Change the sign of `x1` to that of `x2`, element-wise.
 
     Examples
     --------
     >>> import dpnp as np
-    >>> result = np.negative([1, -1])
-    >>> [x for x in result]
-    [-1, 1]
+    >>> np.negative(np.array([1, -1]))
+    array([-1, 1])
+
+    The ``-`` operator can be used as a shorthand for ``negative`` on
+    :class:`dpnp.ndarray`.
 
+    >>> x = np.array([1., -1.])
+    >>> -x
+    array([-1.,  1.])
     """
 
-    x1_desc = dpnp.get_dpnp_descriptor(
-        x1, copy_when_strides=False, copy_when_nondefault_queue=False
+    return check_nd_call_func(
+        numpy.negative,
+        dpnp_negative,
+        x,
+        out=out,
+        where=where,
+        order=order,
+        dtype=dtype,
+        subok=subok,
+        **kwargs,
     )
-    if x1_desc and not kwargs:
-        return dpnp_negative(x1_desc).get_pyobj()
-
-    return call_origin(numpy.negative, x1, **kwargs)
 
 
 def power(x1, x2, /, out=None, *, where=True, dtype=None, subok=True, **kwargs):
@@ -1759,35 +1786,70 @@ def round_(a, decimals=0, out=None):
     return around(a, decimals, out)
 
 
-def sign(x1, **kwargs):
+def sign(
+    x,
+    /,
+    out=None,
+    *,
+    order="K",
+    where=True,
+    dtype=None,
+    subok=True,
+    **kwargs,
+):
     """
     Returns an element-wise indication of the sign of a number.
 
     For full documentation refer to :obj:`numpy.sign`.
 
+    Returns
+    -------
+    out : dpnp.ndarray
+        The indication of the sign of each element of `x`.
+
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.ndarray`.
-    Keyword arguments `kwargs` are currently unsupported.
+    Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
+    Parameters `where`, `dtype` and `subok` are supported with their default values.
+    Keyword argument `kwargs` is currently unsupported.
     Otherwise the function will be executed sequentially on CPU.
     Input array data types are limited by supported DPNP :ref:`Data types`.
 
     Examples
     --------
     >>> import dpnp as np
-    >>> result = np.sign(np.array([-5., 4.5]))
-    >>> [x for x in result]
-    [-1.0, 1.0]
-
-    """
-
-    x1_desc = dpnp.get_dpnp_descriptor(
-        x1, copy_when_strides=False, copy_when_nondefault_queue=False
-    )
-    if x1_desc and not kwargs:
-        return dpnp_sign(x1_desc).get_pyobj()
-
-    return call_origin(numpy.sign, x1, **kwargs)
+    >>> np.sign(np.array([-5., 4.5]))
+    array([-1.0, 1.0])
+    >>> np.sign(np.array(0))
+    array(0)
+    >>> np.sign(np.array(5-2j))
+    array([1+0j])
+
+    """
+
+    if numpy.iscomplexobj(x):
+        return call_origin(
+            numpy.sign,
+            x,
+            out=out,
+            where=where,
+            order=order,
+            dtype=dtype,
+            subok=subok,
+            **kwargs,
+        )
+    else:
+        return check_nd_call_func(
+            numpy.sign,
+            dpnp_sign,
+            x,
+            out=out,
+            where=where,
+            order=order,
+            dtype=dtype,
+            subok=subok,
+            **kwargs,
+        )
 
 
 def subtract(

dpnp/dpnp_iface_trigonometric.py

@@ -436,7 +436,7 @@ def cos(
     -----------
     Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
     Parameters `where`, `dtype` and `subok` are supported with their default values.
-    Keyword arguments `kwargs` are currently unsupported.
+    Keyword argument `kwargs` is currently unsupported.
     Otherwise the function will be executed sequentially on CPU.
     Input array data types are limited by supported DPNP :ref:`Data types`.
 
@@ -989,7 +989,7 @@ def sin(
     -----------
     Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
     Parameters `where`, `dtype` and `subok` are supported with their default values.
-    Keyword arguments `kwargs` are currently unsupported.
+    Keyword argument `kwargs` is currently unsupported.
     Otherwise the function will be executed sequentially on CPU.
     Input array data types are limited by supported DPNP :ref:`Data types`.