Math funcs to desc (#769)

Author: shssf

dpnp/dpnp_algo/dpnp_algo_mathematical.pyx

@@ -80,7 +80,7 @@ ctypedef void(*ftpr_custom_trapz_2in_1out_with_2size_t)(void *, void * , void *
 ctypedef void(*ftpr_custom_around_1in_1out_t)(const void * , void * , const size_t, const int)
 
 
-cpdef dparray dpnp_absolute(dparray input):
+cpdef dparray dpnp_absolute(utils.dpnp_descriptor input):
     cdef dparray_shape_type input_shape = input.shape
     cdef size_t input_shape_size = input.ndim
 
@@ -109,7 +109,7 @@ cpdef dparray dpnp_arctan2(object x1_obj, object x2_obj, object dtype=None, dpar
     return call_fptr_2in_1out(DPNP_FN_ARCTAN2, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef dpnp_around(dparray x1, int decimals):
+cpdef dpnp_around(utils.dpnp_descriptor x1, int decimals):
 
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)
 
@@ -166,7 +166,7 @@ cpdef dparray dpnp_cumsum(utils.dpnp_descriptor x1):
     return call_fptr_1in_1out(DPNP_FN_CUMSUM, x1, (x1.size,))
 
 
-cpdef dparray dpnp_diff(dparray input, int n):
+cpdef dparray dpnp_diff(object input, int n):
     if n == 0:
         return input
     if n < input.shape[-1]:
@@ -221,7 +221,7 @@ cpdef dparray dpnp_fmod(object x1_obj, object x2_obj, object dtype=None, dparray
     return call_fptr_2in_1out(DPNP_FN_FMOD, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef dparray dpnp_gradient(dparray y1, int dx=1):
+cpdef dparray dpnp_gradient(object y1, int dx=1):
 
     size = y1.size
 
@@ -277,7 +277,7 @@ cpdef dparray dpnp_multiply(object x1_obj, object x2_obj, object dtype=None, dpa
     return call_fptr_2in_1out(DPNP_FN_MULTIPLY, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef dparray dpnp_nancumprod(dparray x1):
+cpdef dparray dpnp_nancumprod(utils.dpnp_descriptor x1):
 
     cur_x1 = dpnp.copy(x1)
 
@@ -289,7 +289,7 @@ cpdef dparray dpnp_nancumprod(dparray x1):
     return dpnp_cumprod(x1_desc)
 
 
-cpdef dparray dpnp_nancumsum(dparray x1):
+cpdef dparray dpnp_nancumsum(utils.dpnp_descriptor x1):
 
     cur_x1 = dpnp.copy(x1)
 
@@ -301,7 +301,7 @@ cpdef dparray dpnp_nancumsum(dparray x1):
     return dpnp_cumsum(x1_desc)
 
 
-cpdef dpnp_nanprod(dparray x1):
+cpdef dpnp_nanprod(object x1):
     cdef dparray result = dparray(x1.shape, dtype=x1.dtype)
 
     for i in range(result.size):
@@ -312,10 +312,11 @@ cpdef dpnp_nanprod(dparray x1):
         else:
             result._setitem_scalar(i, input_elem)
 
-    return dpnp_prod(result)
+    result_desc = dpnp.get_dpnp_descriptor(result)  # TODO remove it later
+    return dpnp_prod(result_desc)
 
 
-cpdef dpnp_nansum(dparray x1):
+cpdef dpnp_nansum(object x1):
     cdef dparray result = dparray(x1.shape, dtype=x1.dtype)
 
     for i in range(result.size):
@@ -328,7 +329,9 @@ cpdef dpnp_nansum(dparray x1):
 
     # due to bug in dpnp_sum need this workaround
     # return dpnp_sum(result)
-    sum_result = dpnp_sum(result)
+
+    result_desc = dpnp.get_dpnp_descriptor(result)  # TODO remove it later
+    sum_result = dpnp_sum(result_desc)
     return x1.dtype.type(sum_result[0])
 
 
@@ -340,7 +343,7 @@ cpdef dparray dpnp_power(object x1_obj, object x2_obj, object dtype=None, dparra
     return call_fptr_2in_1out(DPNP_FN_POWER, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef dparray dpnp_prod(dparray input, object axis=None, object dtype=None, dparray out=None, cpp_bool keepdims=False, object initial=None, object where=True):
+cpdef dparray dpnp_prod(utils.dpnp_descriptor input, object axis=None, object dtype=None, dparray out=None, cpp_bool keepdims=False, object initial=None, object where=True):
     """
     input:float64   : outout:float64   : name:prod
     input:float32   : outout:float32   : name:prod
@@ -384,7 +387,7 @@ cpdef dparray dpnp_subtract(object x1_obj, object x2_obj, object dtype=None, dpa
     return call_fptr_2in_1out(DPNP_FN_SUBTRACT, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef dparray dpnp_sum(dparray input, object axis=None, object dtype=None, dparray out=None, cpp_bool keepdims=False, object initial=None, object where=True):
+cpdef dparray dpnp_sum(utils.dpnp_descriptor input, object axis=None, object dtype=None, dparray out=None, cpp_bool keepdims=False, object initial=None, object where=True):
 
     cdef dparray_shape_type input_shape = input.shape
     cdef DPNPFuncType input_c_type = dpnp_dtype_to_DPNPFuncType(input.dtype)
@@ -407,7 +410,7 @@ cpdef dparray dpnp_sum(dparray input, object axis=None, object dtype=None, dparr
     return result
 
 
-cpdef dpnp_trapz(dparray y1, dparray x1, double dx):
+cpdef dpnp_trapz(utils.dpnp_descriptor y1, dparray x1, double dx):
 
     cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(y1.dtype)
     cdef DPNPFuncType param2_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)

dpnp/dpnp_algo/dpnp_algo_statistics.pyx

@@ -82,7 +82,7 @@ cdef dparray call_fptr_custom_std_var_1in_1out(DPNPFuncName fptr_name, dparray a
     return result
 
 
-cpdef dpnp_average(dparray x1):
+cpdef dpnp_average(utils.dpnp_descriptor x1):
     array_sum = dpnp_sum(x1)
 
     """ Numpy interface inconsistency """

dpnp/dpnp_iface_indexing.py

@@ -42,12 +42,12 @@
 
 import collections
 
-import numpy
-
 from dpnp.dpnp_algo import *
 from dpnp.dparray import dparray
 from dpnp.dpnp_utils import *
+
 import dpnp
+import numpy
 
 
 __all__ = [

dpnp/dpnp_iface_mathematical.py

@@ -142,12 +142,14 @@ def absolute(x1, **kwargs):
 
     """
 
-    is_input_dparray = isinstance(x1, dparray)
-
-    if not use_origin_backend(x1) and is_input_dparray and x1.ndim != 0 and not kwargs:
-        result = dpnp_absolute(x1)
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc and not kwargs:
+        if not x1_desc.ndim:
+            pass
+        else:
+            result = dpnp_absolute(x1_desc)
 
-        return result
+            return result
 
     return call_origin(numpy.absolute, x1, **kwargs)
 
@@ -236,15 +238,14 @@ def around(x1, decimals=0, out=None):
 
     """
 
-    if not use_origin_backend(x1):
-        if not isinstance(x1, dparray):
-            pass
-        elif out is not None:
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
+        if out is not None:
             pass
         elif decimals != 0:
             pass
         else:
-            return dpnp_around(x1, decimals)
+            return dpnp_around(x1_desc, decimals)
 
     return call_origin(numpy.around, x1, decimals=decimals, out=out)
 
@@ -483,7 +484,7 @@ def cumsum(x1, **kwargs):
     return call_origin(numpy.cumsum, x1, **kwargs)
 
 
-def diff(input, n=1, axis=-1, prepend=None, append=None):
+def diff(x1, n=1, axis=-1, prepend=None, append=None):
     """
     Calculate the n-th discrete difference along the given axis.
 
@@ -496,10 +497,9 @@ def diff(input, n=1, axis=-1, prepend=None, append=None):
     Otherwise the function will be executed sequentially on CPU.
     """
 
-    if not use_origin_backend(input):
-        if not isinstance(input, dparray):
-            pass
-        elif not isinstance(n, int):
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
+        if not isinstance(n, int):
             pass
         elif n < 1:
             pass
@@ -510,9 +510,9 @@ def diff(input, n=1, axis=-1, prepend=None, append=None):
         elif append is not None:
             pass
         else:
-            return dpnp_diff(input, n)
+            return dpnp_diff(x1, n)
 
-    return call_origin(numpy.diff, input, n, axis, prepend, append)
+    return call_origin(numpy.diff, x1, n, axis, prepend, append)
 
 
 def divide(x1, x2, dtype=None, out=None, where=True, **kwargs):
@@ -848,7 +848,7 @@ def fmod(x1, x2, dtype=None, out=None, where=True, **kwargs):
     return call_origin(numpy.fmod, x1, x2, dtype=dtype, out=out, where=where, **kwargs)
 
 
-def gradient(y1, *varargs, **kwargs):
+def gradient(x1, *varargs, **kwargs):
     """
     Return the gradient of an array.
 
@@ -874,20 +874,20 @@ def gradient(y1, *varargs, **kwargs):
     [0.5, 0.75, 1.25, 1.75, 2.25, 2.5]
 
     """
-    if not use_origin_backend(y1) and not kwargs:
-        if not isinstance(y1, dparray):
-            pass
-        elif len(varargs) > 1:
+
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc and not kwargs:
+        if len(varargs) > 1:
             pass
         elif len(varargs) == 1 and not isinstance(varargs[0], int):
             pass
         else:
             if len(varargs) == 0:
-                return dpnp_gradient(y1)
+                return dpnp_gradient(x1)
 
-            return dpnp_gradient(y1, varargs[0])
+            return dpnp_gradient(x1, varargs[0])
 
-    return call_origin(numpy.gradient, y1, *varargs, **kwargs)
+    return call_origin(numpy.gradient, x1, *varargs, **kwargs)
 
 
 def maximum(x1, x2, dtype=None, out=None, where=True, **kwargs):
@@ -1136,11 +1136,9 @@ def nancumprod(x1, **kwargs):
 
     """
 
-    if not use_origin_backend(x1) and not kwargs:
-        if not isinstance(x1, dparray):
-            pass
-        else:
-            return dpnp_nancumprod(x1)
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc and not kwargs:
+        return dpnp_nancumprod(x1_desc)
 
     return call_origin(numpy.nancumprod, x1, **kwargs)
 
@@ -1174,11 +1172,9 @@ def nancumsum(x1, **kwargs):
 
     """
 
-    if not use_origin_backend(x1) and not kwargs:
-        if not isinstance(x1, dparray):
-            pass
-        else:
-            return dpnp_nancumsum(x1)
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc and not kwargs:
+        return dpnp_nancumsum(x1_desc)
 
     return call_origin(numpy.nancumsum, x1, **kwargs)
 
@@ -1206,9 +1202,8 @@ def nanprod(x1, **kwargs):
 
     """
 
-    is_x1_dparray = isinstance(x1, dparray)
-
-    if (not use_origin_backend(x1) and is_x1_dparray and not kwargs):
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc and not kwargs:
         return dpnp_nanprod(x1)
 
     return call_origin(numpy.nanprod, x1, **kwargs)
@@ -1237,9 +1232,8 @@ def nansum(x1, **kwargs):
 
     """
 
-    is_x1_dparray = isinstance(x1, dparray)
-
-    if (not use_origin_backend(x1) and is_x1_dparray and not kwargs):
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc and not kwargs:
         return dpnp_nansum(x1)
 
     return call_origin(numpy.nansum, x1, **kwargs)
@@ -1360,16 +1354,15 @@ def prod(x1, axis=None, dtype=None, out=None, keepdims=False, initial=None, wher
 
     """
 
-    if not use_origin_backend(x1):
-        if not isinstance(x1, dparray):
-            pass
-        elif out is not None and not isinstance(out, dparray):
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
+        if out is not None and not isinstance(out, dparray):
             pass
         elif where is not True:
             pass
         else:
-            result_obj = dpnp_prod(x1, axis, dtype, out, keepdims, initial, where)
-            result = dpnp.convert_single_elem_array_to_scalar(result_obj, keepdims) 
+            result_obj = dpnp_prod(x1_desc, axis, dtype, out, keepdims, initial, where)
+            result = dpnp.convert_single_elem_array_to_scalar(result_obj, keepdims)
 
             return result
 
@@ -1540,23 +1533,22 @@ def sum(x1, axis=None, dtype=None, out=None, keepdims=False, initial=None, where
 
     """
 
-    if not use_origin_backend(x1):
-        if not isinstance(x1, dparray):
-            pass
-        elif out is not None and not isinstance(out, dparray):
+    x1_desc = dpnp.get_dpnp_descriptor(x1)
+    if x1_desc:
+        if out is not None and not isinstance(out, dparray):
             pass
         elif where is not True:
             pass
         else:
-            result_obj = dpnp_sum(x1, axis, dtype, out, keepdims, initial, where)
+            result_obj = dpnp_sum(x1_desc, axis, dtype, out, keepdims, initial, where)
             result = dpnp.convert_single_elem_array_to_scalar(result_obj, keepdims)
 
             return result
 
     return call_origin(numpy.sum, x1, axis=axis, dtype=dtype, out=out, keepdims=keepdims, initial=initial, where=where)
 
 
-def trapz(y, x=None, dx=1.0, **kwargs):
+def trapz(y, x=None, dx=1.0, axis=-1):
     """
     Integrate along the given axis using the composite trapezoidal rule.
 
@@ -1583,25 +1575,23 @@ def trapz(y, x=None, dx=1.0, **kwargs):
 
     """
 
-    if not use_origin_backend(y):
-
-        if not isinstance(y, dparray):
-            pass
-        elif not isinstance(x, dparray) and x is not None:
+    y_desc = dpnp.get_dpnp_descriptor(y)
+    if y_desc:
+        if not isinstance(x, dparray) and x is not None:
             pass
-        elif x is not None and y.size != x.size:
+        elif x is not None and y_desc.size != x.size:
             pass
-        elif x is not None and y.shape != x.shape:
+        elif x is not None and y_desc.shape != x.shape:
             pass
-        elif y.ndim > 1:
+        elif y_desc.ndim > 1:
             pass
         else:
             if x is None:
-                x = dpnp.empty(0, dtype=y.dtype)
+                x = dpnp.empty(0, dtype=y_desc.dtype)
 
-            return dpnp_trapz(y, x, dx)
+            return dpnp_trapz(y_desc, x, dx)
 
-    return call_origin(numpy.trapz, y, x=x, dx=dx, **kwargs)
+    return call_origin(numpy.trapz, y, x, dx, axis)
 
 
 def true_divide(*args, **kwargs):