Reuse add(), multiply() and subtract() from dpctl

dpnp/dpnp_algo/dpnp_algo.pxd

@@ -36,8 +36,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName":  # need this na
     cdef enum DPNPFuncName "DPNPFuncName":
         DPNP_FN_ABSOLUTE
         DPNP_FN_ABSOLUTE_EXT
-        DPNP_FN_ADD
-        DPNP_FN_ADD_EXT
         DPNP_FN_ALL
         DPNP_FN_ALL_EXT
         DPNP_FN_ALLCLOSE
@@ -117,7 +115,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName":  # need this na
         DPNP_FN_DIAG_INDICES_EXT
         DPNP_FN_DIAGONAL
         DPNP_FN_DIAGONAL_EXT
-        DPNP_FN_DIVIDE
         DPNP_FN_DOT
         DPNP_FN_DOT_EXT
         DPNP_FN_EDIFF1D
@@ -203,8 +200,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName":  # need this na
         DPNP_FN_MINIMUM_EXT
         DPNP_FN_MODF
         DPNP_FN_MODF_EXT
-        DPNP_FN_MULTIPLY
-        DPNP_FN_MULTIPLY_EXT
         DPNP_FN_NANVAR
         DPNP_FN_NANVAR_EXT
         DPNP_FN_NEGATIVE
@@ -323,8 +318,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName":  # need this na
         DPNP_FN_SQUARE_EXT
         DPNP_FN_STD
         DPNP_FN_STD_EXT
-        DPNP_FN_SUBTRACT
-        DPNP_FN_SUBTRACT_EXT
         DPNP_FN_SUM
         DPNP_FN_SUM_EXT
         DPNP_FN_SVD
@@ -523,8 +516,6 @@ cpdef dpnp_descriptor dpnp_copy(dpnp_descriptor x1)
 """
 Mathematical functions
 """
-cpdef dpnp_descriptor dpnp_add(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
-                               dpnp_descriptor out=*, object where=*)
 cpdef dpnp_descriptor dpnp_arctan2(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
                                    dpnp_descriptor out=*, object where=*)
 cpdef dpnp_descriptor dpnp_hypot(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
@@ -533,15 +524,11 @@ 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_multiply(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=*)
 cpdef dpnp_descriptor dpnp_remainder(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
                                      dpnp_descriptor out=*, object where=*)
-cpdef dpnp_descriptor dpnp_subtract(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*,
-                                    dpnp_descriptor out=*, object where=*)
 
 
 """

dpnp/dpnp_algo/dpnp_algo_mathematical.pxi

@@ -37,7 +37,6 @@ and the rest of the library
 
 __all__ += [
     "dpnp_absolute",
-    "dpnp_add",
     "dpnp_arctan2",
     "dpnp_around",
     "dpnp_ceil",
@@ -57,7 +56,6 @@ __all__ += [
     "dpnp_maximum",
     "dpnp_minimum",
     "dpnp_modf",
-    "dpnp_multiply",
     "dpnp_nancumprod",
     "dpnp_nancumsum",
     "dpnp_nanprod",
@@ -67,7 +65,6 @@ __all__ += [
     "dpnp_prod",
     "dpnp_remainder",
     "dpnp_sign",
-    "dpnp_subtract",
     "dpnp_sum",
     "dpnp_trapz",
     "dpnp_trunc"
@@ -123,14 +120,6 @@ cpdef utils.dpnp_descriptor dpnp_absolute(utils.dpnp_descriptor x1):
     return result
 
 
-cpdef utils.dpnp_descriptor dpnp_add(utils.dpnp_descriptor x1_obj,
-                                     utils.dpnp_descriptor x2_obj,
-                                     object dtype=None,
-                                     utils.dpnp_descriptor out=None,
-                                     object where=True):
-    return call_fptr_2in_1out_strides(DPNP_FN_ADD_EXT, x1_obj, x2_obj, dtype, out, where)
-
-
 cpdef utils.dpnp_descriptor dpnp_arctan2(utils.dpnp_descriptor x1_obj,
                                          utils.dpnp_descriptor x2_obj,
                                          object dtype=None,
@@ -426,14 +415,6 @@ cpdef tuple dpnp_modf(utils.dpnp_descriptor x1):
     return (result1.get_pyobj(), result2.get_pyobj())
 
 
-cpdef utils.dpnp_descriptor dpnp_multiply(utils.dpnp_descriptor x1_obj,
-                                          utils.dpnp_descriptor x2_obj,
-                                          object dtype=None,
-                                          utils.dpnp_descriptor out=None,
-                                          object where=True):
-    return call_fptr_2in_1out_strides(DPNP_FN_MULTIPLY_EXT, x1_obj, x2_obj, dtype, out, where)
-
-
 cpdef utils.dpnp_descriptor dpnp_nancumprod(utils.dpnp_descriptor x1):
     cur_x1 = dpnp_copy(x1).get_pyobj()
 
@@ -586,14 +567,6 @@ 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_subtract(utils.dpnp_descriptor x1_obj,
-                                          utils.dpnp_descriptor x2_obj,
-                                          object dtype=None,
-                                          utils.dpnp_descriptor out=None,
-                                          object where=True):
-    return call_fptr_2in_1out_strides(DPNP_FN_SUBTRACT_EXT, x1_obj, x2_obj, dtype, out, where)
-
-
 cpdef utils.dpnp_descriptor dpnp_sum(utils.dpnp_descriptor x1,
                                      object axis=None,
                                      object dtype=None,

dpnp/dpnp_algo/dpnp_elementwise_common.py

@@ -38,10 +38,55 @@
 
 
 __all__ = [
-    "dpnp_divide"
+    "dpnp_add",
+    "dpnp_divide",
+    "dpnp_multiply",
+    "dpnp_subtract"
 ]
 
 
+_add_docstring_ = """
+add(x1, x2, out=None, order='K')
+
+Calculates the sum for each element `x1_i` of the input array `x1` with
+the respective element `x2_i` of the input array `x2`.
+
+Args:
+    x1 (dpnp.ndarray):
+        First input array, expected to have numeric data type.
+    x2 (dpnp.ndarray):
+        Second input array, also 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", None, optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    dpnp.ndarray:
+        an array containing the result of element-wise division. The data type
+        of the returned array is determined by the Type Promotion Rules.
+"""
+
+def dpnp_add(x1, x2, out=None, order='K'):
+    """
+    Invokes add() from dpctl.tensor implementation for add() function.
+    TODO: add a pybind11 extension of add() from OneMKL VM where possible
+    and would be performance effective.
+
+    """
+
+    # dpctl.tensor only works with usm_ndarray or scalar
+    x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1)
+    x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2)
+    out_usm = None if out is None else dpnp.get_usm_ndarray(out)
+
+    func = BinaryElementwiseFunc("add", ti._add_result_type, ti._add,
+                                 _add_docstring_, ti._add_inplace)
+    res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order)
+    return dpnp_array._create_from_usm_ndarray(res_usm)
+
+
 _divide_docstring_ = """
 divide(x1, x2, out=None, order='K')
 
@@ -88,3 +133,87 @@ def _call_divide(src1, src2, dst, sycl_queue, depends=[]):
     func = BinaryElementwiseFunc("divide", ti._divide_result_type, _call_divide, _divide_docstring_)
     res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order)
     return dpnp_array._create_from_usm_ndarray(res_usm)
+
+
+_multiply_docstring_ = """
+multiply(x1, x2, out=None, order='K')
+
+Calculates the product for each element `x1_i` of the input array `x1`
+with the respective element `x2_i` of the input array `x2`.
+
+Args:
+    x1 (dpnp.ndarray):
+        First input array, expected to have numeric data type.
+    x2 (dpnp.ndarray):
+        Second input array, also 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", None, optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    dpnp.ndarray:
+        an array containing the result of element-wise division. The data type
+        of the returned array is determined by the Type Promotion Rules.
+"""
+
+def dpnp_multiply(x1, x2, out=None, order='K'):
+    """
+    Invokes multiply() from dpctl.tensor implementation for multiply() function.
+    TODO: add a pybind11 extension of mul() from OneMKL VM where possible
+    and would be performance effective.
+
+    """
+
+    # dpctl.tensor only works with usm_ndarray or scalar
+    x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1)
+    x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2)
+    out_usm = None if out is None else dpnp.get_usm_ndarray(out)
+
+    func = BinaryElementwiseFunc("multiply", ti._multiply_result_type, ti._multiply,
+                                 _multiply_docstring_, ti._multiply_inplace)
+    res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order)
+    return dpnp_array._create_from_usm_ndarray(res_usm)
+
+
+_subtract_docstring_ = """
+subtract(x1, x2, out=None, order='K')
+
+Calculates the difference bewteen each element `x1_i` of the input
+array `x1` and the respective element `x2_i` of the input array `x2`.
+
+Args:
+    x1 (dpnp.ndarray):
+        First input array, expected to have numeric data type.
+    x2 (dpnp.ndarray):
+        Second input array, also 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", None, optional):
+        Memory layout of the newly output array, if parameter `out` is `None`.
+        Default: "K".
+Returns:
+    dpnp.ndarray:
+        an array containing the result of element-wise division. The data type
+        of the returned array is determined by the Type Promotion Rules.
+"""
+
+def dpnp_subtract(x1, x2, out=None, order='K'):
+    """
+    Invokes subtract() from dpctl.tensor implementation for subtract() function.
+    TODO: add a pybind11 extension of sub() from OneMKL VM where possible
+    and would be performance effective.
+
+    """
+
+    # dpctl.tensor only works with usm_ndarray or scalar
+    x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1)
+    x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2)
+    out_usm = None if out is None else dpnp.get_usm_ndarray(out)
+
+    func = BinaryElementwiseFunc("subtract", ti._subtract_result_type, ti._subtract,
+                                 _subtract_docstring_, ti._subtract_inplace)
+    res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order)
+    return dpnp_array._create_from_usm_ndarray(res_usm)

dpnp/dpnp_array.py

@@ -249,9 +249,15 @@ def __irshift__(self, other):
         dpnp.right_shift(self, other, out=self)
         return self
 
- # '__isub__',
+    def __isub__(self, other):
+        dpnp.subtract(self, other, out=self)
+        return self
+
  # '__iter__',
- # '__itruediv__',
+
+    def __itruediv__(self, other):
+        dpnp.true_divide(self, other, out=self)
+        return self
 
     def __ixor__(self, other):
         dpnp.bitwise_xor(self, other, out=self)