5270559627

pending completion

Build # 5270559627

Build Type

Pull #1431

github

Committed by

web-flow

Commit Message

Merge 41f269645 into c51b3ce2b

Pull Request Pull Request #1431: Implementation of dpnp.mean()

Run Details

5829 of 24618 branches covered (23.68%)

Branch coverage included in aggregate %.

26 of 30 new or added lines in 2 files covered. (86.67%)

2 existing lines in 2 files now uncovered.

11393 of 17515 relevant lines covered (65.05%)

39646.77 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

88.41

/dpnp/dpnp_algo/dpnp_algo_statistics.pxi

# cython: language_level=3
# cython: linetrace=True
# -*- coding: utf-8 -*-
# *****************************************************************************
# Copyright (c) 2016-2023, Intel Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# - Redistributions of source code must retain the above copyright notice,
#   this list of conditions and the following disclaimer.
# - Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
# THE POSSIBILITY OF SUCH DAMAGE.
# *****************************************************************************

"""Module Backend (Statistics part)

This module contains interface functions between C backend layer
and the rest of the library

"""

# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file

__all__ += [
    "dpnp_average",
    "dpnp_correlate",
    "dpnp_cov",
    "dpnp_max",
    "dpnp_median",
    "dpnp_min",
    "dpnp_nanvar",
    "dpnp_std",
    "dpnp_var",
]


# C function pointer to the C library template functions
ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_custom_cov_1in_1out_t)(c_dpctl.DPCTLSyclQueueRef,
                                                                void *, void * , size_t, size_t,
                                                                const c_dpctl.DPCTLEventVectorRef)
ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_custom_nanvar_t)(c_dpctl.DPCTLSyclQueueRef,
                                                          void *, void * , void * , size_t, size_t,
                                                          const c_dpctl.DPCTLEventVectorRef)
ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_custom_std_var_1in_1out_t)(c_dpctl.DPCTLSyclQueueRef,
                                                                    void *, void * , shape_elem_type * , size_t,
                                                                    shape_elem_type * , size_t, size_t,
                                                                    const c_dpctl.DPCTLEventVectorRef)

# C function pointer to the C library template functions
ctypedef c_dpctl.DPCTLSyclEventRef(*custom_statistic_1in_1out_func_ptr_t)(c_dpctl.DPCTLSyclQueueRef,
                                                                          void *, void * , shape_elem_type * , size_t,
                                                                          shape_elem_type * , size_t,
                                                                          const c_dpctl.DPCTLEventVectorRef)
ctypedef c_dpctl.DPCTLSyclEventRef(*custom_statistic_1in_1out_func_ptr_t_max)(c_dpctl.DPCTLSyclQueueRef,
                                                                              void *,
                                                                              void * ,
                                                                              const size_t,
                                                                              shape_elem_type * ,
                                                                              size_t,
                                                                              shape_elem_type * ,
                                                                              size_t,
                                                                              const c_dpctl.DPCTLEventVectorRef)


cdef utils.dpnp_descriptor call_fptr_custom_std_var_1in_1out(DPNPFuncName fptr_name, utils.dpnp_descriptor x1, ddof):
    cdef shape_type_c x1_shape = x1.shape

    """ Convert string type names (array.dtype) to C enum DPNPFuncType """
    cdef DPNPFuncType param_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)

    """ get the FPTR data structure """
    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(fptr_name, param_type, DPNP_FT_NONE)

    x1_obj = x1.get_array()

    # ceate result array with type given by FPTR data
    cdef shape_type_c result_shape = (1,)
    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=x1_obj.sycl_device,
                                                                       usm_type=x1_obj.usm_type,
                                                                       sycl_queue=x1_obj.sycl_queue)

    result_sycl_queue = result.get_array().sycl_queue

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef fptr_custom_std_var_1in_1out_t func = <fptr_custom_std_var_1in_1out_t > kernel_data.ptr

    # stub for interface support
    cdef shape_type_c axis
    cdef Py_ssize_t axis_size = 0

    """ Call FPTR function """
    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    x1.get_data(),
                                                    result.get_data(),
                                                    x1_shape.data(),
                                                    x1.ndim,
                                                    axis.data(),
                                                    axis_size,
                                                    ddof,
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)


    return result


cpdef dpnp_average(utils.dpnp_descriptor x1):
    array_sum = dpnp_sum(x1).get_pyobj()

    """ Numpy interface inconsistency """
    return_type = dpnp.float32 if (x1.dtype == dpnp.float32) else dpnp.float64

    return (return_type(array_sum / x1.size))


cpdef utils.dpnp_descriptor dpnp_correlate(utils.dpnp_descriptor x1, utils.dpnp_descriptor x2):
    cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)
    cdef DPNPFuncType param2_type = dpnp_dtype_to_DPNPFuncType(x2.dtype)

    cdef shape_type_c x1_shape = x1.shape
    cdef shape_type_c x2_shape = x2.shape

    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_CORRELATE_EXT, param1_type, param2_type)

    result_sycl_device, result_usm_type, result_sycl_queue = utils.get_common_usm_allocation(x1, x2)

    # ceate result array with type given by FPTR data
    cdef shape_type_c result_shape = (1,)
    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=result_sycl_device,
                                                                       usm_type=result_usm_type,
                                                                       sycl_queue=result_sycl_queue)

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef fptr_2in_1out_t func = <fptr_2in_1out_t > kernel_data.ptr

    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    result.get_data(),
                                                    x1.get_data(),
                                                    x1.size,
                                                    x1_shape.data(),
                                                    x1_shape.size(),
                                                    x2.get_data(),
                                                    x2.size,
                                                    x2_shape.data(),
                                                    x2_shape.size(),
                                                    NULL,
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)

    return result


cpdef utils.dpnp_descriptor dpnp_cov(utils.dpnp_descriptor array1):
    cdef shape_type_c input_shape = array1.shape

    if array1.ndim == 1:
        input_shape.insert(input_shape.begin(), 1)

    # convert string type names (array.dtype) to C enum DPNPFuncType
    cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)

    # get the FPTR data structure
    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_COV_EXT, param1_type, param1_type)

    array1_obj = array1.get_array()

    # ceate result array with type given by FPTR data
    cdef shape_type_c result_shape = (input_shape[0], input_shape[0])
    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=array1_obj.sycl_device,
                                                                       usm_type=array1_obj.usm_type,
                                                                       sycl_queue=array1_obj.sycl_queue)

    result_sycl_queue = result.get_array().sycl_queue

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef fptr_custom_cov_1in_1out_t func = <fptr_custom_cov_1in_1out_t > kernel_data.ptr
    # call FPTR function
    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    array1.get_data(),
                                                    result.get_data(),
                                                    input_shape[0],
                                                    input_shape[1],
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)

    return result


cdef utils.dpnp_descriptor _dpnp_max(utils.dpnp_descriptor x1, _axis_, shape_type_c result_shape):
    cdef shape_type_c x1_shape = x1.shape
    cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)

    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MAX_EXT, param1_type, param1_type)

    x1_obj = x1.get_array()

    # ceate result array with type given by FPTR data
    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=x1_obj.sycl_device,
                                                                       usm_type=x1_obj.usm_type,
                                                                       sycl_queue=x1_obj.sycl_queue)

    result_sycl_queue = result.get_array().sycl_queue

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef custom_statistic_1in_1out_func_ptr_t_max func = <custom_statistic_1in_1out_func_ptr_t_max > kernel_data.ptr
    cdef shape_type_c axis
    cdef Py_ssize_t axis_size = 0
    cdef shape_type_c axis_ = axis

    if _axis_ is not None:
        axis = _axis_
        axis_.reserve(len(axis))
        for shape_it in axis:
            axis_.push_back(shape_it)
        axis_size = len(axis)

    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    x1.get_data(),
                                                    result.get_data(),
                                                    result.size,
                                                    x1_shape.data(),
                                                    x1.ndim,
                                                    axis_.data(),
                                                    axis_size,
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)

    return result


cpdef utils.dpnp_descriptor dpnp_max(utils.dpnp_descriptor x1, axis):
    cdef shape_type_c x1_shape = x1.shape
    cdef shape_type_c output_shape

    if axis is None:
        axis_ = axis
        output_shape.push_back(1)
    else:
        if isinstance(axis, int):
            if axis < 0:
                axis_ = tuple([x1.ndim - axis])
            else:
                axis_ = tuple([axis])
        else:
            _axis_ = []
            for i in range(len(axis)):
                if axis[i] < 0:
                    _axis_.append(x1.ndim - axis[i])
                else:
                    _axis_.append(axis[i])
            axis_ = tuple(_axis_)

        output_shape.resize(len(x1_shape) - len(axis_), 0)
        ind = 0
        for id, shape_axis in enumerate(x1_shape):
            if id not in axis_:
                output_shape[ind] = shape_axis
                ind += 1

    return _dpnp_max(x1, axis_, output_shape)

cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1):
    cdef shape_type_c x1_shape = array1.shape
    cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)

    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MEDIAN_EXT, param1_type, param1_type)

    array1_obj = array1.get_array()

    cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,),
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=array1_obj.sycl_device,
                                                                       usm_type=array1_obj.usm_type,
                                                                       sycl_queue=array1_obj.sycl_queue)

    result_sycl_queue = result.get_array().sycl_queue

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef custom_statistic_1in_1out_func_ptr_t func = <custom_statistic_1in_1out_func_ptr_t > kernel_data.ptr

    # stub for interface support
    cdef shape_type_c axis
    cdef Py_ssize_t axis_size = 0

    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    array1.get_data(),
                                                    result.get_data(),
                                                    x1_shape.data(),
                                                    array1.ndim,
                                                    axis.data(),
                                                    axis_size,
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)

    return result


cpdef utils.dpnp_descriptor _dpnp_min(utils.dpnp_descriptor x1, _axis_, shape_type_c shape_output):
    cdef shape_type_c x1_shape = x1.shape
    cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)

    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MIN_EXT, param1_type, param1_type)

    x1_obj = x1.get_array()

    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(shape_output,
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=x1_obj.sycl_device,
                                                                       usm_type=x1_obj.usm_type,
                                                                       sycl_queue=x1_obj.sycl_queue)

    result_sycl_queue = result.get_array().sycl_queue

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef custom_statistic_1in_1out_func_ptr_t_max func = <custom_statistic_1in_1out_func_ptr_t_max > kernel_data.ptr
    cdef shape_type_c axis
    cdef Py_ssize_t axis_size = 0
    cdef shape_type_c axis_ = axis

    if _axis_ is not None:
        axis = _axis_
        axis_.reserve(len(axis))
        for shape_it in axis:
            if shape_it < 0:
                raise ValueError("DPNP algo::_dpnp_min(): Negative values in 'shape' are not allowed")
            axis_.push_back(shape_it)
        axis_size = len(axis)

    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    x1.get_data(),
                                                    result.get_data(),
                                                    result.size,
                                                    x1_shape.data(),
                                                    x1.ndim,
                                                    axis_.data(),
                                                    axis_size,
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)

    return result


cpdef utils.dpnp_descriptor dpnp_min(utils.dpnp_descriptor x1, axis):
    cdef shape_type_c x1_shape = x1.shape
    cdef shape_type_c shape_output

    if axis is None:
        axis_ = axis
        shape_output = (1,)
    else:
        if isinstance(axis, int):
            if axis < 0:
                axis_ = tuple([x1.ndim - axis])
            else:
                axis_ = tuple([axis])
        else:
            _axis_ = []
            for i in range(len(axis)):
                if axis[i] < 0:
                    _axis_.append(x1.ndim - axis[i])
                else:
                    _axis_.append(axis[i])
            axis_ = tuple(_axis_)

        for id, shape_axis in enumerate(x1_shape):
            if id not in axis_:
                shape_output.push_back(shape_axis)

    return _dpnp_min(x1, axis_, shape_output)


cpdef utils.dpnp_descriptor dpnp_nanvar(utils.dpnp_descriptor arr, ddof):
    # dpnp_isnan does not support USM array as input in comparison to dpnp.isnan
    cdef utils.dpnp_descriptor mask_arr = dpnp.get_dpnp_descriptor(dpnp.isnan(arr.get_pyobj()),
                                                                   copy_when_nondefault_queue=False)
    n = dpnp.count_nonzero(mask_arr.get_pyobj())
    res_size = int(arr.size - n)
    cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(arr.dtype)

    cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_NANVAR_EXT, param1_type, param1_type)

    arr_obj = arr.get_array()

    # ceate result array with type given by FPTR data
    cdef shape_type_c result_shape = utils._object_to_tuple(res_size)
    cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,
                                                                       kernel_data.return_type,
                                                                       None,
                                                                       device=arr_obj.sycl_device,
                                                                       usm_type=arr_obj.usm_type,
                                                                       sycl_queue=arr_obj.sycl_queue)

    result_sycl_queue = result.get_array().sycl_queue

    cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue
    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()

    cdef fptr_custom_nanvar_t func = <fptr_custom_nanvar_t > kernel_data.ptr

    cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,
                                                    arr.get_data(),
                                                    mask_arr.get_data(),
                                                    result.get_data(),
                                                    result.size,
                                                    arr.size,
                                                    NULL)  # dep_events_ref

    with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)
    c_dpctl.DPCTLEvent_Delete(event_ref)

    return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR_EXT, result, ddof)


cpdef utils.dpnp_descriptor dpnp_std(utils.dpnp_descriptor a, size_t ddof):
    return call_fptr_custom_std_var_1in_1out(DPNP_FN_STD_EXT, a, ddof)


cpdef utils.dpnp_descriptor dpnp_var(utils.dpnp_descriptor a, size_t ddof):
    return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR_EXT, a, ddof)

1	# cython: language_level=3
2	# cython: linetrace=True
3	# -- coding: utf-8 --
4	# *****************************************************************************
5	# Copyright (c) 2016-2023, Intel Corporation
6	# All rights reserved.
7	#
8	# Redistribution and use in source and binary forms, with or without
9	# modification, are permitted provided that the following conditions are met:
10	# - Redistributions of source code must retain the above copyright notice,
11	# this list of conditions and the following disclaimer.
12	# - Redistributions in binary form must reproduce the above copyright notice,
13	# this list of conditions and the following disclaimer in the documentation
14	# and/or other materials provided with the distribution.
15	#
16	# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
17	# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
18	# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
19	# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
20	# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
21	# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
22	# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
23	# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
24	# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
25	# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
26	# THE POSSIBILITY OF SUCH DAMAGE.
27	# *****************************************************************************
28
29	"""Module Backend (Statistics part)
30
31	This module contains interface functions between C backend layer
32	and the rest of the library
33
34	"""
35
36	# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file
37
38	__all__ += [	×
39	"dpnp_average",
40	"dpnp_correlate",
41	"dpnp_cov",
42	"dpnp_max",
43	"dpnp_median",
44	"dpnp_min",
45	"dpnp_nanvar",
46	"dpnp_std",
47	"dpnp_var",
48	]
49
50
51	# C function pointer to the C library template functions
52	ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_custom_cov_1in_1out_t)(c_dpctl.DPCTLSyclQueueRef,
53	void , void , size_t, size_t,
54	const c_dpctl.DPCTLEventVectorRef)
55	ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_custom_nanvar_t)(c_dpctl.DPCTLSyclQueueRef,
56	void , void , void * , size_t, size_t,
57	const c_dpctl.DPCTLEventVectorRef)
58	ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_custom_std_var_1in_1out_t)(c_dpctl.DPCTLSyclQueueRef,
59	void , void , shape_elem_type * , size_t,
60	shape_elem_type * , size_t, size_t,
61	const c_dpctl.DPCTLEventVectorRef)
62
63	# C function pointer to the C library template functions
64	ctypedef c_dpctl.DPCTLSyclEventRef(*custom_statistic_1in_1out_func_ptr_t)(c_dpctl.DPCTLSyclQueueRef,
65	void , void , shape_elem_type * , size_t,
66	shape_elem_type * , size_t,
67	const c_dpctl.DPCTLEventVectorRef)
68	ctypedef c_dpctl.DPCTLSyclEventRef(*custom_statistic_1in_1out_func_ptr_t_max)(c_dpctl.DPCTLSyclQueueRef,
69	void *,
70	void * ,
71	const size_t,
72	shape_elem_type * ,
73	size_t,
74	shape_elem_type * ,
75	size_t,
76	const c_dpctl.DPCTLEventVectorRef)
77
78
79	cdef utils.dpnp_descriptor call_fptr_custom_std_var_1in_1out(DPNPFuncName fptr_name, utils.dpnp_descriptor x1, ddof):	×
80	cdef shape_type_c x1_shape = x1.shape	1✔
81
82	""" Convert string type names (array.dtype) to C enum DPNPFuncType """
83	cdef DPNPFuncType param_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)	1✔
84
85	""" get the FPTR data structure """
86	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(fptr_name, param_type, DPNP_FT_NONE)	1✔
87
88	x1_obj = x1.get_array()	1✔
89
90	# ceate result array with type given by FPTR data
91	cdef shape_type_c result_shape = (1,)	1✔
92	cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,	1✔
93	kernel_data.return_type,
94	None,
95	device=x1_obj.sycl_device,	1✔
96	usm_type=x1_obj.usm_type,	1✔
97	sycl_queue=x1_obj.sycl_queue)	1✔
98
99	result_sycl_queue = result.get_array().sycl_queue	1✔
100
101	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
102	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
103
104	cdef fptr_custom_std_var_1in_1out_t func = <fptr_custom_std_var_1in_1out_t > kernel_data.ptr	1✔
105
106	# stub for interface support
107	cdef shape_type_c axis
108	cdef Py_ssize_t axis_size = 0	1✔
109
110	""" Call FPTR function """
111	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
112	x1.get_data(),
113	result.get_data(),
114	x1_shape.data(),
115	x1.ndim,	1✔
116	axis.data(),
117	axis_size,
118	ddof,	1✔
119	NULL) # dep_events_ref
120
121	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
122	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
123
124
125	return result	1✔
126
127
128	cpdef dpnp_average(utils.dpnp_descriptor x1):	×
129	array_sum = dpnp_sum(x1).get_pyobj()	1✔
130
131	""" Numpy interface inconsistency """
132	return_type = dpnp.float32 if (x1.dtype == dpnp.float32) else dpnp.float64	1✔
133
134	return (return_type(array_sum / x1.size))	1✔
135
136
137	cpdef utils.dpnp_descriptor dpnp_correlate(utils.dpnp_descriptor x1, utils.dpnp_descriptor x2):	×
138	cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)	1✔
139	cdef DPNPFuncType param2_type = dpnp_dtype_to_DPNPFuncType(x2.dtype)	1✔
140
141	cdef shape_type_c x1_shape = x1.shape	1✔
142	cdef shape_type_c x2_shape = x2.shape	1✔
143
144	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_CORRELATE_EXT, param1_type, param2_type)	1✔
145
146	result_sycl_device, result_usm_type, result_sycl_queue = utils.get_common_usm_allocation(x1, x2)	1✔
147
148	# ceate result array with type given by FPTR data
149	cdef shape_type_c result_shape = (1,)	1✔
150	cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,	1✔
151	kernel_data.return_type,
152	None,
153	device=result_sycl_device,
154	usm_type=result_usm_type,
155	sycl_queue=result_sycl_queue)
156
157	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
158	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
159
160	cdef fptr_2in_1out_t func = <fptr_2in_1out_t > kernel_data.ptr	1✔
161
162	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
163	result.get_data(),
164	x1.get_data(),
165	x1.size,	1✔
166	x1_shape.data(),
167	x1_shape.size(),
168	x2.get_data(),
169	x2.size,	1✔
170	x2_shape.data(),
171	x2_shape.size(),
172	NULL,
173	NULL) # dep_events_ref
174
175	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
176	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
177
178	return result	1✔
179
180
181	cpdef utils.dpnp_descriptor dpnp_cov(utils.dpnp_descriptor array1):	×
182	cdef shape_type_c input_shape = array1.shape	1✔
183
184	if array1.ndim == 1:	1✔
185	input_shape.insert(input_shape.begin(), 1)	×
186
187	# convert string type names (array.dtype) to C enum DPNPFuncType
188	cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)	1✔
189
190	# get the FPTR data structure
191	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_COV_EXT, param1_type, param1_type)	1✔
192
193	array1_obj = array1.get_array()	1✔
194
195	# ceate result array with type given by FPTR data
196	cdef shape_type_c result_shape = (input_shape[0], input_shape[0])	1✔
197	cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,	1✔
198	kernel_data.return_type,
199	None,
200	device=array1_obj.sycl_device,	1✔
201	usm_type=array1_obj.usm_type,	1✔
202	sycl_queue=array1_obj.sycl_queue)	1✔
203
204	result_sycl_queue = result.get_array().sycl_queue	1✔
205
206	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
207	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
208
209	cdef fptr_custom_cov_1in_1out_t func = <fptr_custom_cov_1in_1out_t > kernel_data.ptr	1✔
210	# call FPTR function
211	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
212	array1.get_data(),
213	result.get_data(),
214	input_shape[0],
215	input_shape[1],
216	NULL) # dep_events_ref
217
218	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
219	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
220
221	return result	1✔
222
223
224	cdef utils.dpnp_descriptor _dpnp_max(utils.dpnp_descriptor x1, _axis_, shape_type_c result_shape):	×
225	cdef shape_type_c x1_shape = x1.shape	1✔
226	cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)	1✔
227
228	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MAX_EXT, param1_type, param1_type)	1✔
229
230	x1_obj = x1.get_array()	1✔
231
232	# ceate result array with type given by FPTR data
233	cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,	1✔
234	kernel_data.return_type,
235	None,
236	device=x1_obj.sycl_device,	1✔
237	usm_type=x1_obj.usm_type,	1✔
238	sycl_queue=x1_obj.sycl_queue)	1✔
239
240	result_sycl_queue = result.get_array().sycl_queue	1✔
241
242	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
243	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
244
245	cdef custom_statistic_1in_1out_func_ptr_t_max func = <custom_statistic_1in_1out_func_ptr_t_max > kernel_data.ptr	1✔
246	cdef shape_type_c axis
247	cdef Py_ssize_t axis_size = 0	1✔
248	cdef shape_type_c axis_ = axis	1✔
249
250	if _axis_ is not None:	1✔
251	axis = _axis_	1✔
252	axis_.reserve(len(axis))	1✔
253	for shape_it in axis:	1✔
254	axis_.push_back(shape_it)	1✔
255	axis_size = len(axis)	1✔
256
257	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
258	x1.get_data(),
259	result.get_data(),
260	result.size,	1✔
261	x1_shape.data(),
262	x1.ndim,	1✔
263	axis_.data(),
264	axis_size,
265	NULL) # dep_events_ref
266
267	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
268	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
269
270	return result	1✔
271
272
273	cpdef utils.dpnp_descriptor dpnp_max(utils.dpnp_descriptor x1, axis):	×
274	cdef shape_type_c x1_shape = x1.shape	1✔
275	cdef shape_type_c output_shape
276
277	if axis is None:	1✔
278	axis_ = axis	1✔
279	output_shape.push_back(1)	1✔
280	else:
281	if isinstance(axis, int):	1✔
282	if axis < 0:	1✔
283	axis_ = tuple([x1.ndim - axis])	×
284	else:
285	axis_ = tuple([axis])	1✔
286	else:
287	_axis_ = []	1✔
288	for i in range(len(axis)):	1✔
289	if axis[i] < 0:	1✔
290	_axis_.append(x1.ndim - axis[i])	×
291	else:
292	_axis_.append(axis[i])	1✔
293	axis_ = tuple(_axis_)	1✔
294
295	output_shape.resize(len(x1_shape) - len(axis_), 0)	1✔
296	ind = 0	1✔
297	for id, shape_axis in enumerate(x1_shape):	1✔
298	if id not in axis_:	1✔
299	output_shape[ind] = shape_axis	1✔
300	ind += 1	1✔
301
302	return _dpnp_max(x1, axis_, output_shape)	1✔
303
UNCOV 304	cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1):	×
305	cdef shape_type_c x1_shape = array1.shape	1✔
306	cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)	1✔
307
308	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MEDIAN_EXT, param1_type, param1_type)	1✔
309
310	array1_obj = array1.get_array()	1✔
311
312	cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,),	1✔
313	kernel_data.return_type,
314	None,
315	device=array1_obj.sycl_device,	1✔
316	usm_type=array1_obj.usm_type,	1✔
317	sycl_queue=array1_obj.sycl_queue)	1✔
318
319	result_sycl_queue = result.get_array().sycl_queue	1✔
320
321	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
322	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
323
324	cdef custom_statistic_1in_1out_func_ptr_t func = <custom_statistic_1in_1out_func_ptr_t > kernel_data.ptr	1✔
325
326	# stub for interface support
327	cdef shape_type_c axis
328	cdef Py_ssize_t axis_size = 0	1✔
329
330	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
331	array1.get_data(),
332	result.get_data(),
333	x1_shape.data(),
334	array1.ndim,	1✔
335	axis.data(),
336	axis_size,
337	NULL) # dep_events_ref
338
339	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
340	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
341
342	return result	1✔
343
344
345	cpdef utils.dpnp_descriptor _dpnp_min(utils.dpnp_descriptor x1, _axis_, shape_type_c shape_output):	×
346	cdef shape_type_c x1_shape = x1.shape	1✔
347	cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype)	1✔
348
349	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MIN_EXT, param1_type, param1_type)	1✔
350
351	x1_obj = x1.get_array()	1✔
352
353	cdef utils.dpnp_descriptor result = utils.create_output_descriptor(shape_output,	1✔
354	kernel_data.return_type,
355	None,
356	device=x1_obj.sycl_device,	1✔
357	usm_type=x1_obj.usm_type,	1✔
358	sycl_queue=x1_obj.sycl_queue)	1✔
359
360	result_sycl_queue = result.get_array().sycl_queue	1✔
361
362	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
363	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
364
365	cdef custom_statistic_1in_1out_func_ptr_t_max func = <custom_statistic_1in_1out_func_ptr_t_max > kernel_data.ptr	1✔
366	cdef shape_type_c axis
367	cdef Py_ssize_t axis_size = 0	1✔
368	cdef shape_type_c axis_ = axis	1✔
369
370	if _axis_ is not None:	1✔
371	axis = _axis_	1✔
372	axis_.reserve(len(axis))	1✔
373	for shape_it in axis:	1✔
374	if shape_it < 0:	1✔
375	raise ValueError("DPNP algo::_dpnp_min(): Negative values in 'shape' are not allowed")	×
376	axis_.push_back(shape_it)	1✔
377	axis_size = len(axis)	1✔
378
379	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
380	x1.get_data(),
381	result.get_data(),
382	result.size,	1✔
383	x1_shape.data(),
384	x1.ndim,	1✔
385	axis_.data(),
386	axis_size,
387	NULL) # dep_events_ref
388
389	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
390	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
391
392	return result	1✔
393
394
395	cpdef utils.dpnp_descriptor dpnp_min(utils.dpnp_descriptor x1, axis):	×
396	cdef shape_type_c x1_shape = x1.shape	1✔
397	cdef shape_type_c shape_output
398
399	if axis is None:	1✔
400	axis_ = axis	1✔
401	shape_output = (1,)	1✔
402	else:
403	if isinstance(axis, int):	1✔
404	if axis < 0:	1✔
405	axis_ = tuple([x1.ndim - axis])	×
406	else:
407	axis_ = tuple([axis])	1✔
408	else:
409	_axis_ = []	×
410	for i in range(len(axis)):	×
411	if axis[i] < 0:	×
412	_axis_.append(x1.ndim - axis[i])	×
413	else:
414	_axis_.append(axis[i])	×
415	axis_ = tuple(_axis_)	×
416
417	for id, shape_axis in enumerate(x1_shape):	1✔
418	if id not in axis_:	1✔
419	shape_output.push_back(shape_axis)	1✔
420
421	return _dpnp_min(x1, axis_, shape_output)	1✔
422
423
424	cpdef utils.dpnp_descriptor dpnp_nanvar(utils.dpnp_descriptor arr, ddof):	×
425	# dpnp_isnan does not support USM array as input in comparison to dpnp.isnan
426	cdef utils.dpnp_descriptor mask_arr = dpnp.get_dpnp_descriptor(dpnp.isnan(arr.get_pyobj()),	1✔
427	copy_when_nondefault_queue=False)	1✔
428	n = dpnp.count_nonzero(mask_arr.get_pyobj())	1✔
429	res_size = int(arr.size - n)	1✔
430	cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(arr.dtype)	1✔
431
432	cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_NANVAR_EXT, param1_type, param1_type)	1✔
433
434	arr_obj = arr.get_array()	1✔
435
436	# ceate result array with type given by FPTR data
437	cdef shape_type_c result_shape = utils._object_to_tuple(res_size)	1✔
438	cdef utils.dpnp_descriptor result = utils.create_output_descriptor(result_shape,	1✔
439	kernel_data.return_type,
440	None,
441	device=arr_obj.sycl_device,	1✔
442	usm_type=arr_obj.usm_type,	1✔
443	sycl_queue=arr_obj.sycl_queue)	1✔
444
445	result_sycl_queue = result.get_array().sycl_queue	1✔
446
447	cdef c_dpctl.SyclQueue q = <c_dpctl.SyclQueue> result_sycl_queue	1✔
448	cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()	1✔
449
450	cdef fptr_custom_nanvar_t func = <fptr_custom_nanvar_t > kernel_data.ptr	1✔
451
452	cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref,	1✔
453	arr.get_data(),
454	mask_arr.get_data(),
455	result.get_data(),
456	result.size,	1✔
457	arr.size,	1✔
458	NULL) # dep_events_ref
459
460	with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref)	1✔
461	c_dpctl.DPCTLEvent_Delete(event_ref)	1✔
462
463	return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR_EXT, result, ddof)	1✔
464
465
466	cpdef utils.dpnp_descriptor dpnp_std(utils.dpnp_descriptor a, size_t ddof):	×
467	return call_fptr_custom_std_var_1in_1out(DPNP_FN_STD_EXT, a, ddof)	1✔
468
469
470	cpdef utils.dpnp_descriptor dpnp_var(utils.dpnp_descriptor a, size_t ddof):	×
471	return call_fptr_custom_std_var_1in_1out(DPNP_FN_VAR_EXT, a, ddof)	1✔

IntelPython / dpnp / 5270559627

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous