Use desc in sort (#764)

Author: shssf

dpnp/dpnp_algo/dpnp_algo_sorting.pyx

@@ -50,7 +50,9 @@ cpdef dparray dpnp_argsort(utils.dpnp_descriptor x1):
     return call_fptr_1in_1out(DPNP_FN_ARGSORT, x1, x1.shape)
 
 
-cpdef dparray dpnp_partition(dparray arr, int kth, axis=-1, kind='introselect', order=None):
+cpdef dparray dpnp_partition(utils.dpnp_descriptor arr, int kth, axis=-1, kind='introselect', order=None):
+    cdef dparray_shape_type shape1 = arr.shape
+
     cdef size_t kth_ = kth if kth >= 0 else (arr.ndim + kth)
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(arr.dtype)
 
@@ -62,12 +64,12 @@ cpdef dparray dpnp_partition(dparray arr, int kth, axis=-1, kind='introselect',
 
     cdef fptr_dpnp_partition_t func = <fptr_dpnp_partition_t > kernel_data.ptr
 
-    func(arr.get_data(), arr2.get_data(), result.get_data(), kth_, < size_t * > arr._dparray_shape.data(), arr.ndim)
+    func(arr.get_data(), arr2.get_data(), result.get_data(), kth_, < size_t * > shape1.data(), arr.ndim)
 
     return result
 
 
-cpdef dparray dpnp_searchsorted(dparray arr, dparray v, side='left'):
+cpdef dparray dpnp_searchsorted(utils.dpnp_descriptor arr, utils.dpnp_descriptor v, side='left'):
     if side is 'left':
         side_ = True
     else:

dpnp/dpnp_iface_counting.py

@@ -41,7 +41,9 @@
 
 
 from dpnp.dpnp_algo.dpnp_algo import *  # TODO need to investigate why dpnp.dpnp_algo can not be used
-from dpnp.dparray import dparray
+
+import dpnp
+import numpy
 
 # full module name because dpnp_iface_counting loaded from cython too early
 from dpnp.dpnp_utils.dpnp_algo_utils import *
@@ -54,7 +56,7 @@
 ]
 
 
-def count_nonzero(in_array1, axis=None, *, keepdims=False):
+def count_nonzero(x1, axis=None, *, keepdims=False):
     """
     Counts the number of non-zero values in the array ``in_array1``.
 
@@ -77,17 +79,16 @@ def count_nonzero(in_array1, axis=None, *, keepdims=False):
 
     """
 
-    is_dparray1 = isinstance(in_array1, dparray)
-
-    if (not use_origin_backend(in_array1) and is_dparray1):
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
         if axis is not None:
-            checker_throw_value_error("count_nonzero", "axis", type(axis), None)
-        if keepdims is not False:
-            checker_throw_value_error("count_nonzero", "keepdims", keepdims, False)
-
-        result_obj = dpnp_count_nonzero(in_array1)
-        result = dpnp.convert_single_elem_array_to_scalar(result_obj)
+            pass
+        elif keepdims is not False:
+            pass
+        else:
+            result_obj = dpnp_count_nonzero(x1)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj)
 
-        return result
+            return result
 
-    return numpy.count_nonzero(in_array1, axis, keepdims=keepdims)
+    return numpy.count_nonzero(x1, axis, keepdims=keepdims)

dpnp/dpnp_iface_sorting.py

@@ -104,7 +104,7 @@ def argsort(in_array1, axis=-1, kind=None, order=None):
     return numpy.argsort(in_array1, axis, kind, order)
 
 
-def partition(arr, kth, axis=-1, kind='introselect', order=None):
+def partition(x1, kth, axis=-1, kind='introselect', order=None):
     """
     Return a partitioned copy of an array.
     For full documentation refer to :obj:`numpy.partition`.
@@ -115,12 +115,12 @@ def partition(arr, kth, axis=-1, kind='introselect', order=None):
     Input kth is supported as :obj:`int`.
     Parameters ``axis``, ``kind`` and ``order`` are supported only with default values.
     """
-    if not use_origin_backend():
-        if not isinstance(arr, dparray):
-            pass
-        elif not isinstance(kth, int):
+
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
+        if not isinstance(kth, int):
             pass
-        elif kth >= arr.shape[arr.ndim - 1] or arr.ndim + kth < 0:
+        elif kth >= x1_desc.shape[x1_desc.ndim - 1] or x1_desc.ndim + kth < 0:
             pass
         elif axis != -1:
             pass
@@ -129,12 +129,12 @@ def partition(arr, kth, axis=-1, kind='introselect', order=None):
         elif order is not None:
             pass
         else:
-            return dpnp_partition(arr, kth, axis, kind, order)
+            return dpnp_partition(x1_desc, kth, axis, kind, order)
 
-    return call_origin(numpy.partition, arr, kth, axis, kind, order)
+    return call_origin(numpy.partition, x1, kth, axis, kind, order)
 
 
-def searchsorted(arr, v, side='left', sorter=None):
+def searchsorted(x1, x2, side='left', sorter=None):
     """
     Find indices where elements should be inserted to maintain order.
     For full documentation refer to :obj:`numpy.searchsorted`.
@@ -146,27 +146,24 @@ def searchsorted(arr, v, side='left', sorter=None):
     Input side is supported only values ``left``, ``right``.
     Parameters ``sorter`` is supported only with default values.
     """
-    if not use_origin_backend():
-        if not isinstance(arr, dparray):
-            pass
-        elif not isinstance(v, dparray):
-            pass
-        elif arr.ndim != 1:
+
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    x2_desc = dpnp.get_dpnp_descriptor(x2)
+    if 0 and x1_desc and x2_desc:
+        if x1_desc.ndim != 1:
             pass
-        elif arr.dtype != v.dtype:
+        elif x1_desc.dtype != x2_desc.dtype:
             pass
         elif side not in ['left', 'right']:
             pass
         elif sorter is not None:
             pass
-        elif arr.size < 2:
-            pass
-        elif dpnp.sort(arr) != arr:
+        elif x1_desc.size < 2:
             pass
         else:
-            return dpnp_searchsorted(arr, v, side=side)
+            return dpnp_searchsorted(x1_desc, x2_desc, side=side)
 
-        return call_origin(numpy.searchsorted, arr, v, side=side, sorter=sorter)
+    return call_origin(numpy.searchsorted, x1, x2, side=side, sorter=sorter)
 
 
 def sort(x1, **kwargs):

dpnp/dpnp_iface_trigonometric.py

@@ -1061,10 +1061,8 @@ def unwrap(x1):
 
     """
 
-    if (use_origin_backend(x1)):
-        return call_origin(numpy.unwrap, x1, **kwargs)
-
-    if not isinstance(x1, dparray):
-        raise TypeError(f"DPNP unwrap(): Unsupported x1={type(x1)}")
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
+        return dpnp_unwrap(x1_desc)
 
-    return dpnp_unwrap(x1)
+    return call_origin(numpy.unwrap, x1, **kwargs)