cavis/libnd4j/include/ops/impl/specials_double.hpp
raver119 320924278d
Legacy API changes (#441)
* initial commit

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* another initial commit

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* another initial commit

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* one more initial commit

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* next step

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* next step

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* next step

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* next step

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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

271 lines
11 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com, created on 07.10.2017.
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <system/pointercast.h>
#include <helpers/shape.h>
#include <helpers/TAD.h>
#include <ops/specials.h>
#include <system/dll.h>
#include <array/NDArray.h>
#include <ops/declarable/CustomOperations.h>
#include <types/types.h>
#include <helpers/Loops.h>
namespace sd {
template<typename S, typename T>
void SpecialTypeConverter::convertGeneric(Nd4jPointer * extras, void *dx, Nd4jLong N, void *dz) {
auto x = reinterpret_cast<S *>(dx);
auto z = reinterpret_cast<T *>(dz);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
z[i] = static_cast<T>(x[i]);
}
};
samediff::Threads::parallel_for(func, 0, N);
};
template <typename X, typename Y>
void quickSort_parallel_internal_key(X* key, Nd4jLong const* xShapeInfo, Y* values, Nd4jLong const* yShapeInfo, int left, int right, int cutoff, bool descending) {
int i = left, j = right;
X ktmp;
X pivot = key[shape::getIndexOffset((left + right) / 2, xShapeInfo)];
Y vtmp;
{
/* PARTITION PART */
while (i <= j) {
if (descending) {
while (key[shape::getIndexOffset(i, xShapeInfo)] > pivot)
i++;
while (key[shape::getIndexOffset(j, xShapeInfo)] < pivot)
j--;
if (i <= j) {
ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
vtmp = values[shape::getIndexOffset(i, yShapeInfo)];
values[shape::getIndexOffset(i, yShapeInfo)] = values[shape::getIndexOffset(j, yShapeInfo)];
values[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
i++;
j--;
}
} else {
while (key[shape::getIndexOffset(i, xShapeInfo)] < pivot)
i++;
while (key[shape::getIndexOffset(j, xShapeInfo)] > pivot)
j--;
if (i <= j) {
ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
vtmp = values[shape::getIndexOffset(i, yShapeInfo)];
values[shape::getIndexOffset(i, yShapeInfo)] = values[shape::getIndexOffset(j, yShapeInfo)];
values[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
i++;
j--;
}
}
}
}
//
if ( ((right-left)<cutoff) ){
if (left < j){ quickSort_parallel_internal_key(key, xShapeInfo, values, yShapeInfo, left, j, cutoff, descending); }
if (i < right){ quickSort_parallel_internal_key(key, xShapeInfo, values, yShapeInfo, i, right, cutoff, descending); }
}else{
PRAGMA_OMP_TASK
{ quickSort_parallel_internal_key(key, xShapeInfo, values, yShapeInfo, left, j, cutoff, descending); }
PRAGMA_OMP_TASK
{ quickSort_parallel_internal_key(key, xShapeInfo, values, yShapeInfo, i, right, cutoff, descending); }
}
}
template <typename X, typename Y>
void quickSort_parallel_internal_value(X* key, Nd4jLong const* xShapeInfo, Y* value, Nd4jLong const* yShapeInfo, int left, int right, int cutoff, bool descending) {
int i = left, j = right;
X ktmp;
Y pivot = value[shape::getIndexOffset((left + right) / 2, yShapeInfo)];
Y vtmp;
{
/* PARTITION PART */
while (i <= j) {
if (descending) {
while (value[shape::getIndexOffset(i, yShapeInfo)] > pivot)
i++;
while (value[shape::getIndexOffset(j, yShapeInfo)] < pivot)
j--;
if (i <= j) {
ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
vtmp = value[shape::getIndexOffset(i, yShapeInfo)];
value[shape::getIndexOffset(i, yShapeInfo)] = value[shape::getIndexOffset(j, yShapeInfo)];
value[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
i++;
j--;
}
} else {
while (value[shape::getIndexOffset(i, yShapeInfo)] < pivot)
i++;
while (value[shape::getIndexOffset(j, yShapeInfo)] > pivot)
j--;
if (i <= j) {
ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
vtmp = value[shape::getIndexOffset(i, yShapeInfo)];
value[shape::getIndexOffset(i, yShapeInfo)] = value[shape::getIndexOffset(j, yShapeInfo)];
value[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
i++;
j--;
}
}
}
}
//
if ( ((right-left)<cutoff) ){
if (left < j){ quickSort_parallel_internal_value(key, xShapeInfo, value, yShapeInfo, left, j, cutoff, descending); }
if (i < right){ quickSort_parallel_internal_value(key, xShapeInfo, value, yShapeInfo, i, right, cutoff, descending); }
}else{
PRAGMA_OMP_TASK
{ quickSort_parallel_internal_value(key, xShapeInfo, value, yShapeInfo, left, j, cutoff, descending); }
PRAGMA_OMP_TASK
{ quickSort_parallel_internal_value(key, xShapeInfo, value, yShapeInfo, i, right, cutoff, descending); }
}
}
template <typename X, typename Y>
static void quickSort_parallel_key(void *varray, Nd4jLong const* xShapeInfo, void *yarray, Nd4jLong const* yShapeInfo, Nd4jLong lenArray, int numThreads, bool descending){
auto array = reinterpret_cast<X *>(varray);
auto values = reinterpret_cast<Y *>(yarray);
int cutoff = 1000;
PRAGMA_OMP_PARALLEL_THREADS(numThreads)
{
PRAGMA_OMP_SINGLE_ARGS(nowait)
{
quickSort_parallel_internal_key(array, xShapeInfo, values, yShapeInfo, 0, lenArray-1, cutoff, descending);
}
}
}
template <typename X, typename Y>
static void quickSort_parallel_value(void *varray, Nd4jLong const* xShapeInfo, void *yarray, Nd4jLong const* yShapeInfo, Nd4jLong lenArray, int numThreads, bool descending){
auto array = reinterpret_cast<X *>(varray);
auto values = reinterpret_cast<Y *>(yarray);
int cutoff = 1000;
PRAGMA_OMP_PARALLEL_THREADS(numThreads)
{
PRAGMA_OMP_SINGLE_ARGS(nowait)
{
quickSort_parallel_internal_value(array, xShapeInfo, values, yShapeInfo, 0, lenArray-1, cutoff, descending);
}
}
}
template <typename X, typename Y>
void DoubleMethods<X,Y>::sortByKey(void *vx, Nd4jLong const* xShapeInfo, void *vy, Nd4jLong const* yShapeInfo, bool descending) {
quickSort_parallel_key<X,Y>(vx, xShapeInfo, vy, yShapeInfo, shape::length(xShapeInfo), omp_get_max_threads(), descending);
}
template <typename X, typename Y>
void DoubleMethods<X,Y>::sortByValue(void *vx, Nd4jLong const* xShapeInfo, void *vy, Nd4jLong const* yShapeInfo, bool descending) {
quickSort_parallel_value<X,Y>(vx, xShapeInfo, vy, yShapeInfo, shape::length(xShapeInfo), omp_get_max_threads(), descending);
}
template <typename X, typename Y>
void DoubleMethods<X,Y>::sortTadByKey(void *vx, Nd4jLong const* xShapeInfo, void *vy, Nd4jLong const* yShapeInfo, int *dimension, int dimensionLength, bool descending) {
auto x = reinterpret_cast<X*>(vx);
auto y = reinterpret_cast<Y*>(vy);
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
auto packY = ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength);
auto xLength = shape::length(xShapeInfo);
auto xTadLength = shape::length(packX.primaryShapeInfo());
auto numTads = packX.numberOfTads();
auto func = PRAGMA_THREADS_FOR {
for (auto r = start; r < stop; r++) {
auto dx = x + packX.primaryOffsets()[r];
auto dy = y + packY.primaryOffsets()[r];
quickSort_parallel_key<X, Y>(dx, packX.primaryShapeInfo(), dy, packY.primaryShapeInfo(), xTadLength, 1, descending);
}
};
samediff::Threads::parallel_tad(func, 0, numTads);
}
template <typename X, typename Y>
void DoubleMethods<X,Y>::sortTadByValue(void *vx, Nd4jLong const* xShapeInfo, void *vy, Nd4jLong const* yShapeInfo, int *dimension, int dimensionLength, bool descending) {
auto x = reinterpret_cast<X*>(vx);
auto y = reinterpret_cast<Y*>(vy);
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
auto packY = ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength);
auto xLength = shape::length(xShapeInfo);
auto xTadLength = shape::length(packX.primaryShapeInfo());
auto numTads = packX.numberOfTads();
auto func = PRAGMA_THREADS_FOR {
for (auto r = start; r < stop; r++) {
auto dx = x + packX.primaryOffsets()[r];
auto dy = y + packY.primaryOffsets()[r];
quickSort_parallel_value<X, Y>(dx, packX.primaryShapeInfo(), dy, packY.primaryShapeInfo(), xTadLength, 1, descending);
}
};
samediff::Threads::parallel_tad(func, 0, numTads);
}
}