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RubtsowaRubtsowa
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add abs kernel (#104)
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7 files changed

+148
-48
lines changed

7 files changed

+148
-48
lines changed

‎dpnp/backend.pxd

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@@ -31,6 +31,7 @@ from dpnp.dparray cimport dparray, dparray_shape_type
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cdef extern from "backend/backend_iface_fptr.hpp" namespace "DPNPFuncName": # need this namespace for Enum import
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cdef enum DPNPFuncName "DPNPFuncName":
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DPNP_FN_ABSOLUTE
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DPNP_FN_ADD
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DPNP_FN_ARCCOS
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DPNP_FN_ARCCOSH

‎dpnp/backend/backend_iface.hpp

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@@ -97,6 +97,26 @@ template <typename _DataType>
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INP_DLLEXPORT void
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custom_blas_gemm_c(void* array1, void* array2, void* result1, size_t size_m, size_t size_n, size_t size_k);
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/**
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* @ingroup BACKEND_API
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* @brief absolute function.
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*
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* @param [in] array1_in Input array.
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*
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* @param [in] input_shape Input shape.
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*
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* @param [out] result1 Output array.
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*
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* @param [in] size Number of elements in input arrays.
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*
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*/
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template <typename _DataType>
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INP_DLLEXPORT void custom_elemwise_absolute_c(void* array1_in,
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const std::vector<long>& input_shape,
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void* result1,
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size_t size);
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/**
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* @ingroup BACKEND_API
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* @brief Custom implementation of dot function

‎dpnp/backend/backend_iface_fptr.cpp

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@@ -143,6 +143,11 @@ static func_map_t func_map_init()
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const DPNPFuncType eft_DBL = DPNPFuncType::DPNP_FT_DOUBLE;
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func_map_t fmap;
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fmap[DPNPFuncName::DPNP_FN_ABSOLUTE][eft_INT][eft_INT] = {eft_INT, (void*)custom_elemwise_absolute_c<int>};
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fmap[DPNPFuncName::DPNP_FN_ABSOLUTE][eft_LNG][eft_LNG] = {eft_LNG, (void*)custom_elemwise_absolute_c<long>};
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fmap[DPNPFuncName::DPNP_FN_ABSOLUTE][eft_FLT][eft_FLT] = {eft_FLT, (void*)custom_elemwise_absolute_c<float>};
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fmap[DPNPFuncName::DPNP_FN_ABSOLUTE][eft_DBL][eft_DBL] = {eft_DBL, (void*)custom_elemwise_absolute_c<double>};
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fmap[DPNPFuncName::DPNP_FN_ADD][eft_INT][eft_INT] = {eft_INT, (void*)custom_elemwise_add_c<int, int, int>};
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fmap[DPNPFuncName::DPNP_FN_ADD][eft_INT][eft_LNG] = {eft_LNG, (void*)custom_elemwise_add_c<int, long, long>};
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fmap[DPNPFuncName::DPNP_FN_ADD][eft_INT][eft_FLT] = {eft_DBL, (void*)custom_elemwise_add_c<int, float, double>};

‎dpnp/backend/backend_iface_fptr.hpp

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@@ -59,7 +59,8 @@
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*/
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enum class DPNPFuncName : size_t
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{
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DPNP_FN_NONE, /**< Very first element of the enumeration */
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DPNP_FN_NONE, /**< Very first element of the enumeration */
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DPNP_FN_ABSOLUTE, /**< Used in numpy.absolute() implementation */
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DPNP_FN_ADD, /**< Used in numpy.add() implementation */
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DPNP_FN_ARCCOS, /**< Used in numpy.arccos() implementation */
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DPNP_FN_ARCCOSH, /**< Used in numpy.arccosh() implementation */

‎dpnp/backend/custom_kernels_mathematical.cpp

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@@ -0,0 +1,97 @@
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//*****************************************************************************
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// Copyright (c) 2016-2020, Intel Corporation
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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// - Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// - Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
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// THE POSSIBILITY OF SUCH DAMAGE.
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//*****************************************************************************
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#include <cmath>
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#include <iostream>
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#include <mkl_blas_sycl.hpp>
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#include <vector>
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#include <backend_iface.hpp>
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#include "backend_utils.hpp"
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#include "queue_sycl.hpp"
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template <typename _KernelNameSpecialization>
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class custom_elemwise_absolute_c_kernel;
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template <typename _DataType>
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void custom_elemwise_absolute_c(void* array1_in,
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const std::vector<long>& input_shape,
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void* result1,
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size_t size)
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{
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if (!size)
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{
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return;
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}
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cl::sycl::event event;
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_DataType* array1 = reinterpret_cast<_DataType*>(array1_in);
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_DataType* result = reinterpret_cast<_DataType*>(result1);
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const size_t input_shape_size = input_shape.size();
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size_t* input_offset_shape = reinterpret_cast<size_t*>(dpnp_memory_alloc_c(input_shape_size * sizeof(long)));
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size_t* result_offset_shape = reinterpret_cast<size_t*>(dpnp_memory_alloc_c(input_shape_size * sizeof(long)));
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cl::sycl::range<1> gws(size);
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event = DPNP_QUEUE.submit([&](cl::sycl::handler& cgh) {
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cgh.parallel_for<class custom_elemwise_absolute_c_kernel<_DataType> >(
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gws,
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[=](cl::sycl::id<1> global_id)
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{
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const size_t idx = global_id[0];
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if (array1[idx] >= 0) {
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result[idx] = array1[idx];
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}
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else{
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result[idx] = -1 * array1[idx];
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}
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}); // parallel_for
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}); // queue.submit
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event.wait();
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free(input_offset_shape, DPNP_QUEUE);
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free(result_offset_shape, DPNP_QUEUE);
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}
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template void custom_elemwise_absolute_c<double>(void* array1_in,
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const std::vector<long>& input_shape,
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void* result1,
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size_t size);
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template void custom_elemwise_absolute_c<float>(void* array1_in,
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const std::vector<long>& input_shape,
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void* result1,
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size_t size);
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template void custom_elemwise_absolute_c<long>(void* array1_in,
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const std::vector<long>& input_shape,
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void* result1,
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size_t size);
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template void custom_elemwise_absolute_c<int>(void* array1_in,
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const std::vector<long>& input_shape,
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void* result1,
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size_t size);

‎dpnp/backend_mathematical.pyx

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@@ -63,54 +63,29 @@ __all__ += [
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]
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ctypedef void(*fptr_custom_elemwise_absolute_1in_1out_t)(void *, dparray_shape_type &,
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void *, size_t)
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cpdef dparray dpnp_absolute(dparray input):
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cdef dparray_shape_type shape_input = input.shape
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cdef long size_input = input.size
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output_shape = dparray(len(shape_input), dtype=numpy.int64)
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for id, shape_ in enumerate(shape_input):
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output_shape[id] = shape_
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cdef long prod = 1
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for i in range(len(output_shape)):
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if output_shape[i] != 0:
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prod *= output_shape[i]
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result_array = [None] * prod
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input_shape_offsets = [None] * len(shape_input)
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acc = 1
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for i in range(len(shape_input)):
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ind = len(shape_input) - 1 - i
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input_shape_offsets[ind] = acc
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acc *= shape_input[ind]
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output_shape_offsets = [None] * len(shape_input)
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acc = 1
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for i in range(len(output_shape)):
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ind = len(output_shape) - 1 - i
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output_shape_offsets[ind] = acc
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acc *= output_shape[ind]
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result_offsets = input_shape_offsets[:] # need copy. not a reference
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for source_idx in range(size_input):
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# reconstruct x,y,z from linear source_idx
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xyz = []
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remainder = source_idx
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for i in input_shape_offsets:
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quotient, remainder = divmod(remainder, i)
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xyz.append(quotient)
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result_indexes = []
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for idx, offset in enumerate(xyz):
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result_indexes.append(offset)
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result_offset = 0
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for i, result_indexes_val in enumerate(result_indexes):
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result_offset += (output_shape_offsets[i] * result_indexes_val)
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input_elem = input.item(source_idx)
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result_array[result_offset] = input_elem if input_elem >= 0 else -1 * input_elem
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dpnp_array = dpnp.array(result_array, dtype=input.dtype)
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dpnp_result_array = dpnp_array.reshape(output_shape)
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return dpnp_result_array
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cdef dparray_shape_type input_shape = input.shape
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cdef size_t input_shape_size = input.ndim
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# convert string type names (dparray.dtype) to C enum DPNPFuncType
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cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)
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# get the FPTR data structure
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cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_ABSOLUTE, param1_type, param1_type)
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result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type)
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# ceate result array with type given by FPTR data
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cdef dparray result = dparray(input_shape, dtype=result_type)
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cdef fptr_custom_elemwise_absolute_1in_1out_t func = <fptr_custom_elemwise_absolute_1in_1out_t > kernel_data.ptr
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# call FPTR function
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func(input.get_data(), input_shape, result.get_data(), input.size)
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return result
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cpdef dparray dpnp_add(dparray x1, dparray x2):

‎setup.py

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"dpnp/backend/custom_kernels.cpp",
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"dpnp/backend/custom_kernels_elemwise.cpp",
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"dpnp/backend/custom_kernels_manipulation.cpp",
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"dpnp/backend/custom_kernels_mathematical.cpp",
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"dpnp/backend/custom_kernels_reduction.cpp",
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"dpnp/backend/custom_kernels_searching.cpp",
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"dpnp/backend/custom_kernels_sorting.cpp",

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