Merge branch 'master' into drop_build_py_38

Author: antonwolfy

dpnp/dpnp_algo/dpnp_algo_manipulation.pxi

@@ -38,9 +38,7 @@ and the rest of the library
 __all__ += [
     "dpnp_atleast_2d",
     "dpnp_atleast_3d",
-    "dpnp_expand_dims",
     "dpnp_repeat",
-    "dpnp_reshape",
 ]
 
 
@@ -104,35 +102,6 @@ cpdef utils.dpnp_descriptor dpnp_atleast_3d(utils.dpnp_descriptor arr):
         return arr
 
 
-cpdef utils.dpnp_descriptor dpnp_expand_dims(utils.dpnp_descriptor in_array, axis):
-    axis_tuple = utils._object_to_tuple(axis)
-    result_ndim = len(axis_tuple) + in_array.ndim
-
-    if len(axis_tuple) == 0:
-        axis_ndim = 0
-    else:
-        axis_ndim = max(-min(0, min(axis_tuple)), max(0, max(axis_tuple))) + 1
-
-    axis_norm = utils._object_to_tuple(utils.normalize_axis(axis_tuple, result_ndim))
-
-    if axis_ndim - len(axis_norm) > in_array.ndim:
-        utils.checker_throw_axis_error("dpnp_expand_dims", "axis", axis, axis_ndim)
-
-    if len(axis_norm) > len(set(axis_norm)):
-        utils.checker_throw_value_error("dpnp_expand_dims", "axis", axis, "no repeated axis")
-
-    cdef shape_type_c shape_list
-    axis_idx = 0
-    for i in range(result_ndim):
-        if i in axis_norm:
-            shape_list.push_back(1)
-        else:
-            shape_list.push_back(in_array.shape[axis_idx])
-            axis_idx = axis_idx + 1
-
-    return dpnp_reshape(in_array, shape_list)
-
-
 cpdef utils.dpnp_descriptor dpnp_repeat(utils.dpnp_descriptor array1, repeats, axes=None):
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)
 
@@ -165,17 +134,3 @@ cpdef utils.dpnp_descriptor dpnp_repeat(utils.dpnp_descriptor array1, repeats, a
     c_dpctl.DPCTLEvent_Delete(event_ref)
 
     return result
-
-
-cpdef utils.dpnp_descriptor dpnp_reshape(utils.dpnp_descriptor array1, newshape, order="C"):
-    # return dpnp.get_dpnp_descriptor(dpctl.tensor.usm_ndarray(newshape, dtype=numpy.dtype(array1.dtype).name, buffer=array1.get_pyobj()))
-    # return dpnp.get_dpnp_descriptor(dpctl.tensor.reshape(array1.get_pyobj(), newshape))
-    array1_obj = array1.get_array()
-    array_obj = dpctl.tensor.reshape(array1_obj, newshape, order=order)
-    return dpnp.get_dpnp_descriptor(dpnp_array(array_obj.shape,
-                                               buffer=array_obj,
-                                               order=order,
-                                               device=array1_obj.sycl_device,
-                                               usm_type=array1_obj.usm_type,
-                                               sycl_queue=array1_obj.sycl_queue),
-                                    copy_when_nondefault_queue=False)

dpnp/dpnp_algo/dpnp_elementwise_common.py

@@ -27,8 +27,6 @@
 # *****************************************************************************
 
 
-import dpctl
-import dpctl.tensor as dpt
 import dpctl.tensor._tensor_impl as ti
 from dpctl.tensor._elementwise_common import (
     BinaryElementwiseFunc,
@@ -538,32 +536,11 @@ def _call_divide(src1, src2, dst, sycl_queue, depends=None):
     return ti._divide(src1, src2, dst, sycl_queue, depends)
 
 
-def _call_divide_inplace(lhs, rhs, sycl_queue, depends=None):
-    """In place workaround until dpctl.tensor provides the functionality."""
-
-    if depends is None:
-        depends = []
-
-    # allocate temporary memory for out array
-    out = dpt.empty_like(lhs, dtype=dpnp.result_type(lhs.dtype, rhs.dtype))
-
-    # call a general callback
-    div_ht_, div_ev_ = _call_divide(lhs, rhs, out, sycl_queue, depends)
-
-    # store the result into left input array and return events
-    cp_ht_, cp_ev_ = ti._copy_usm_ndarray_into_usm_ndarray(
-        src=out, dst=lhs, sycl_queue=sycl_queue, depends=[div_ev_]
-    )
-    dpctl.SyclEvent.wait_for([div_ht_])
-    return (cp_ht_, cp_ev_)
-
-
 divide_func = BinaryElementwiseFunc(
     "divide",
     ti._divide_result_type,
     _call_divide,
     _divide_docstring_,
-    _call_divide_inplace,
 )
 
 

dpnp/dpnp_array.py

@@ -188,7 +188,10 @@ def __eq__(self, other):
     def __float__(self):
         return self._array_obj.__float__()
 
-    # '__floordiv__',
+    def __floordiv__(self, other):
+        """Return self//value."""
+        return dpnp.floor_divide(self, other)
+
     # '__format__',
 
     def __ge__(self, other):
@@ -227,15 +230,22 @@ def __iand__(self, other):
         dpnp.bitwise_and(self, other, out=self)
         return self
 
-    # '__ifloordiv__',
+    def __ifloordiv__(self, other):
+        """Return self//=value."""
+        dpnp.floor_divide(self, other, out=self)
+        return self
 
     def __ilshift__(self, other):
         """Return self<<=value."""
         dpnp.left_shift(self, other, out=self)
         return self
 
     # '__imatmul__',
-    # '__imod__',
+
+    def __imod__(self, other):
+        """Return self%=value."""
+        dpnp.remainder(self, other, out=self)
+        return self
 
     def __imul__(self, other):
         """Return self*=value."""
@@ -345,7 +355,8 @@ def __rand__(self, other):
     def __repr__(self):
         return dpt.usm_ndarray_repr(self._array_obj, prefix="array")
 
-    # '__rfloordiv__',
+    def __rfloordiv__(self, other):
+        return dpnp.floor_divide(self, other)
 
     def __rlshift__(self, other):
         return dpnp.left_shift(other, self)
@@ -1059,7 +1070,14 @@ def sum(
             where=where,
         )
 
-    # 'swapaxes',
+    def swapaxes(self, axis1, axis2):
+        """
+        Interchange two axes of an array.
+
+        For full documentation refer to :obj:`numpy.swapaxes`.
+        """
+
+        return dpnp.swapaxes(self, axis1=axis1, axis2=axis2)
 
     def take(self, indices, /, *, axis=None, out=None, mode="wrap"):
         """