matrix_power, matmul with out param to desc (#812)

* matrix_power, matmul with out param to desc

Author: shssf

dpnp/dpnp_algo/dpnp_algo.pxd

@@ -273,7 +273,7 @@ cpdef dpnp_descriptor dpnp_not_equal(dpnp_descriptor input1, dpnp_descriptor inp
 Linear algebra
 """
 cpdef dparray dpnp_dot(dpnp_descriptor in_array1, dpnp_descriptor in_array2)
-cpdef dparray dpnp_matmul(dpnp_descriptor in_array1, dpnp_descriptor in_array2, dparray out=*)
+cpdef dpnp_descriptor dpnp_matmul(dpnp_descriptor in_array1, dpnp_descriptor in_array2, dpnp_descriptor out=*)
 
 
 """

dpnp/dpnp_algo/dpnp_algo_linearalgebra.pyx

@@ -197,7 +197,7 @@ cpdef dparray dpnp_kron(dpnp_descriptor in_array1, dpnp_descriptor in_array2):
     return result
 
 
-cpdef dparray dpnp_matmul(dpnp_descriptor in_array1, dpnp_descriptor in_array2, dparray out=None):
+cpdef utils.dpnp_descriptor dpnp_matmul(utils.dpnp_descriptor in_array1, utils.dpnp_descriptor in_array2, utils.dpnp_descriptor out=None):
 
     cdef dparray_shape_type shape_result
 
@@ -258,19 +258,8 @@ cpdef dparray dpnp_matmul(dpnp_descriptor in_array1, dpnp_descriptor in_array2,
     # get the FPTR data structure
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MATMUL, param1_type, param2_type)
 
-    result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)
-
-    cdef dparray result
-
-    if out is not None:
-        if out.dtype != result_type:
-            utils.checker_throw_value_error('matmul', 'out.dtype', out.dtype, result_type)
-        if out.shape != shape_result:
-            utils.checker_throw_value_error('matmul', 'out.shape', out.shape, shape_result)
-        result = out
-    else:
-        result = dparray(shape_result, dtype=result_type)
-
+    # ceate result array with type given by FPTR data
+    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(shape_result, kernel_data.return_type, out)
     if result.size == 0:
         return result
 

dpnp/dpnp_algo/dpnp_algo_mathematical.pyx

@@ -341,7 +341,13 @@ cpdef utils.dpnp_descriptor dpnp_power(utils.dpnp_descriptor x1_obj, utils.dpnp_
     return call_fptr_2in_1out(DPNP_FN_POWER, x1_obj, x2_obj, dtype=dtype, out=out, where=where, func_name="power")
 
 
-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):
+cpdef utils.dpnp_descriptor dpnp_prod(utils.dpnp_descriptor input,
+                                      object axis=None,
+                                      object dtype=None,
+                                      utils.dpnp_descriptor out=None,
+                                      cpp_bool keepdims=False,
+                                      object initial=None,
+                                      object where=True):
     """
     input:float64   : outout:float64   : name:prod
     input:float32   : outout:float32   : name:prod
@@ -364,7 +370,7 @@ cpdef dparray dpnp_prod(utils.dpnp_descriptor input, object axis=None, object dt
     cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_PROD, input_c_type, result_c_type)
 
     """ Create result array """
-    cdef dparray result = utils.create_output_array(result_shape, result_c_type, out)
+    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape, result_c_type, out)
     cdef dpnp_reduction_c_t func = <dpnp_reduction_c_t > kernel_data.ptr
 
     """ Call FPTR interface function """
@@ -385,7 +391,13 @@ cpdef utils.dpnp_descriptor dpnp_subtract(object x1_obj, object x2_obj, object d
     return call_fptr_2in_1out(DPNP_FN_SUBTRACT, x1_obj, x2_obj, dtype=dtype, out=out, where=where)
 
 
-cpdef utils.dpnp_descriptor 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):
+cpdef utils.dpnp_descriptor dpnp_sum(utils.dpnp_descriptor input,
+                                     object axis=None,
+                                     object dtype=None,
+                                     utils.dpnp_descriptor 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)

dpnp/dpnp_iface_linearalgebra.py

@@ -245,8 +245,7 @@ def matmul(x1, x2, out=None, **kwargs):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     x2_desc = dpnp.get_dpnp_descriptor(x2)
-    out_desc = dpnp.get_dpnp_descriptor(x2)
-    if x1_desc and x2_desc and out_desc and not kwargs:
+    if x1_desc and x2_desc and not kwargs:
         if x1_desc.size != x2_desc.size:
             pass
         elif not x1_desc.ndim:
@@ -276,7 +275,8 @@ def matmul(x1, x2, out=None, **kwargs):
                 if (dparray1_size > cost_size) and (dparray2_size > cost_size):
                     return dpnp_matmul(x1_desc, x2_desc, out)
             else:
-                return dpnp_matmul(x1_desc, x2_desc, out)
+                out_desc = dpnp.get_dpnp_descriptor(out) if out is not None else None
+                return dpnp_matmul(x1_desc, x2_desc, out_desc).get_pyobj()
 
     return call_origin(numpy.matmul, x1, x2, out=out, **kwargs)
 

dpnp/dpnp_iface_mathematical.py

@@ -1372,12 +1372,11 @@ def prod(x1, axis=None, dtype=None, out=None, keepdims=False, initial=None, wher
 
     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:
+        if where is not True:
             pass
         else:
-            result_obj = dpnp_prod(x1_desc, axis, dtype, out, keepdims, initial, where)
+            out_desc = dpnp.get_dpnp_descriptor(out) if out is not None else None
+            result_obj = dpnp_prod(x1_desc, axis, dtype, out_desc, keepdims, initial, where).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj, keepdims)
 
             return result
@@ -1499,7 +1498,6 @@ def subtract(x1, x2, dtype=None, out=None, where=True, **kwargs):
     x2_is_scalar = dpnp.isscalar(x2)
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     x2_desc = dpnp.get_dpnp_descriptor(x2)
-
     if x1_desc and x2_desc and not kwargs:
         if not x1_desc and not x1_is_scalar:
             pass
@@ -1522,7 +1520,8 @@ def subtract(x1, x2, dtype=None, out=None, where=True, **kwargs):
         elif not where:
             pass
         else:
-            return dpnp_subtract(x1_desc, x2_desc, dtype=dtype, out=out, where=where).get_pyobj()
+            out_desc = dpnp.get_dpnp_descriptor(out) if out is not None else None
+            return dpnp_subtract(x1_desc, x2_desc, dtype=dtype, out=out_desc, where=where).get_pyobj()
 
     return call_origin(numpy.subtract, x1, x2, dtype=dtype, out=out, where=where, **kwargs)
 
@@ -1551,12 +1550,11 @@ def sum(x1, axis=None, dtype=None, out=None, keepdims=False, initial=None, where
 
     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:
+        if where is not True:
             pass
         else:
-            result_obj = dpnp_sum(x1_desc, axis, dtype, out, keepdims, initial, where).get_pyobj()
+            out_desc = dpnp.get_dpnp_descriptor(out) if out is not None else None
+            result_obj = dpnp_sum(x1_desc, axis, dtype, out_desc, keepdims, initial, where).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj, keepdims)
 
             return result

dpnp/dpnp_utils/dpnp_algo_utils.pxd

@@ -156,12 +156,16 @@ cdef DPNPFuncType get_output_c_type(DPNPFuncName funcID,
 Calculate output array type by 'out' and 'dtype' cast parameters
 """
 
-cdef dparray create_output_array(dparray_shape_type output_shape, DPNPFuncType c_type, object requested_out)
+cdef dparray create_output_array(dparray_shape_type output_shape,
+                                 DPNPFuncType c_type,
+                                 object requested_out)
 """
 Create output array based on shape, type and 'out' parameters
 """
 
-cdef dpnp_descriptor create_output_descriptor(dparray_shape_type output_shape, DPNPFuncType c_type, object requested_out)
+cdef dpnp_descriptor create_output_descriptor(dparray_shape_type output_shape,
+                                              DPNPFuncType c_type,
+                                              dpnp_descriptor requested_out)
 """
 Same as "create_output_array" but output is "dpnp_descriptor"
 """

dpnp/dpnp_utils/dpnp_algo_utils.pyx

@@ -336,6 +336,10 @@ cdef DPNPFuncType get_output_c_type(DPNPFuncName funcID,
 
 
 cdef dparray create_output_array(dparray_shape_type output_shape, DPNPFuncType c_type, object requested_out):
+    """
+    TODO This function needs to be deleted. Replace with create_output_descriptor()
+    """
+
     cdef dparray result
 
     if requested_out is None:
@@ -349,10 +353,24 @@ cdef dparray create_output_array(dparray_shape_type output_shape, DPNPFuncType c
 
     return result
 
-cdef dpnp_descriptor create_output_descriptor(dparray_shape_type output_shape, DPNPFuncType c_type, object requested_out):
-    result = create_output_array(output_shape, c_type, requested_out)
+cdef dpnp_descriptor create_output_descriptor(dparray_shape_type output_shape,
+                                              DPNPFuncType c_type,
+                                              dpnp_descriptor requested_out):
+    cdef dpnp_descriptor result_desc
+
+    if requested_out is None:
+        """ Create DPNP array """
+        result = dparray(output_shape, dtype=dpnp_DPNPFuncType_to_dtype( < size_t > c_type))
+        result_desc = dpnp_descriptor(result)
+    else:
+        """ Based on 'out' parameter """
+        if (output_shape != requested_out.shape):
+            checker_throw_value_error("create_output_array", "out.shape", requested_out.shape, output_shape)
 
-    cdef dpnp_descriptor result_desc = dpnp_descriptor(result)
+        if isinstance(requested_out, dpnp_descriptor):
+            result_desc = requested_out
+        else:
+            result_desc = dpnp_descriptor(requested_out)
 
     return result_desc
 

dpnp/linalg/dpnp_iface_linalg.py

@@ -239,26 +239,14 @@ def matrix_power(input, count):
 
     """
 
-    is_input_dparray = isinstance(input, dparray)
-
-    if not use_origin_backend(input) and is_input_dparray and count > 0:
+    if not use_origin_backend() and count > 0:
         result = input
         for id in range(count - 1):
             result = dpnp.matmul(result, input)
 
         return result
 
-    input1 = dpnp.asnumpy(input) if is_input_dparray else input
-
-    # TODO need to put dparray memory into NumPy call
-    result_numpy = numpy.linalg.matrix_power(input1, count)
-    result = result_numpy
-    if isinstance(result, numpy.ndarray):
-        result = dparray(result_numpy.shape, dtype=result_numpy.dtype)
-        for i in range(result.size):
-            result._setitem_scalar(i, result_numpy.item(i))
-
-    return result
+    return call_origin(numpy.linalg.matrix_power, input, count)
 
 
 def matrix_rank(input, tol=None, hermitian=False):