cavis/libnd4j/include/loops/cpu/pairwise_int.cpp
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

223 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
//
#include <loops/pairwise_int.h>
#include <types/types.h>
#include <helpers/LoopKind.h>
#include <helpers/OmpLaunchHelper.h>
#include <execution/Threads.h>
using namespace simdOps;
namespace functions {
namespace pairwise_transforms {
template <typename X>
void PairWiseIntTransform<X>::exec(const int opNum,
const void *x, Nd4jLong xEws,
const void *y, Nd4jLong yEws,
void *z, Nd4jLong zEws,
void *extraParams,
Nd4jLong n,
const uint64_t start, const uint64_t stop) {
DISPATCH_BY_OPNUM_T(exec, PARAMS(x,
xEws,
y,
yEws,
z,
zEws,
extraParams,
n, start, stop), PAIRWISE_INT_OPS);
};
template <typename X>
template <typename OpType>
void PairWiseIntTransform<X>::exec(const void *vx, Nd4jLong xEws,
const void *vy, Nd4jLong yEws,
void *vz, Nd4jLong zEws,
void *vextraParams,
const Nd4jLong n,
const uint64_t start,
const uint64_t stop) {
auto x = reinterpret_cast<const X *>(vx);
auto y = reinterpret_cast<const X *>(vy);
auto z = reinterpret_cast<X *>(vz);
auto extraParams = reinterpret_cast<X *>(vextraParams);
if (xEws == 1 && yEws == 1 && zEws == 1) {
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++)
z[i] = OpType::op(x[i], y[i], extraParams);
}
else {
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++)
z[i*zEws] = OpType::op(x[i*xEws], y[i*yEws], extraParams);
}
}
template <typename X>
void PairWiseIntTransform<X>::exec(const int opNum,
const void *x, const Nd4jLong *xShapeInfo,
const void *y, const Nd4jLong *yShapeInfo,
void *z, const Nd4jLong *zShapeInfo,
void *extraParams,
const uint64_t start, const uint64_t stop) {
DISPATCH_BY_OPNUM_T(exec, PARAMS(x,
xShapeInfo,
y,
yShapeInfo,
z,
zShapeInfo,
extraParams, start, stop),
PAIRWISE_INT_OPS);
};
template <typename X>
template <typename OpType>
void PairWiseIntTransform<X>::exec(const void *vx, const Nd4jLong* xShapeInfo,
const void *vy, const Nd4jLong* yShapeInfo,
void *vz, const Nd4jLong* zShapeInfo,
void *vextraParams,
const uint64_t start,
const uint64_t stop) {
auto x = reinterpret_cast<const X *>(vx);
auto y = reinterpret_cast<const X *>(vy);
auto z = reinterpret_cast<X *>(vz);
auto extraParams = reinterpret_cast<X *>(vextraParams);
auto n = shape::length(xShapeInfo);
auto xEws = shape::elementWiseStride(xShapeInfo);
auto yEws = shape::elementWiseStride(yShapeInfo);
auto zEws = shape::elementWiseStride(zShapeInfo);
if (shape::isScalar(yShapeInfo)) {
uint xShapeInfoCast[MAX_RANK];
const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
PRAGMA_OMP_SIMD
for(auto i = start; i < stop; i++) {
auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
z[offset] = OpType::op(x[offset], y[0], extraParams);
};
}
else {
uint zShapeInfoCast[MAX_RANK];
const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
PRAGMA_OMP_SIMD
for(auto i = start; i < stop; i++) {
auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ);
z[zOffset] = OpType::op(x[xOffset], y[0], extraParams);
};
}
return;
}
const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo);
const bool sameShapesXY = shape::shapeEquals(xShapeInfo, yShapeInfo);
if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && sameShapesXY) {
exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, n, start, stop);
}
else if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape
exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo), start, stop);
}
else {
if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
uint xShapeInfoCast[MAX_RANK];
const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++) {
auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
z[offset] = OpType::op(x[offset], y[offset], extraParams);
};
}
else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
uint xShapeInfoCast[MAX_RANK];
uint zShapeInfoCast[MAX_RANK];
const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++) {
auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ);
z[zOffset] = OpType::op(x[offset], y[offset], extraParams);
};
}
else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
uint xShapeInfoCast[MAX_RANK];
uint yShapeInfoCast[MAX_RANK];
const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++) {
auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
auto yOffset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY);
z[offset] = OpType::op(x[offset], y[yOffset], extraParams);
};
}
else if(shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo)) {
uint xShapeInfoCast[MAX_RANK];
uint yShapeInfoCast[MAX_RANK];
const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++) {
auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
auto offset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY);
z[offset] = OpType::op(x[xOffset], y[offset], extraParams);
};
}
else {
uint xShapeInfoCast[MAX_RANK];
uint yShapeInfoCast[MAX_RANK];
uint zShapeInfoCast[MAX_RANK];
const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
PRAGMA_OMP_SIMD
for (auto i = start; i < stop; i++) {
auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
auto yOffset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY);
auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ);
z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
};
}
}
}
BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT PairWiseIntTransform, , INTEGER_TYPES);
}
}