cavis/libnd4j/include/ops/declarable/impl/LegacyReduce3Op.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

123 lines
5.7 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
******************************************************************************/
//
// Created by raver119 on 17.10.2017.
//
#include <ops/declarable/LegacyReduce3Op.h>
#include <helpers/ShapeUtils.h>
#include <helpers/TAD.h>
#include <helpers/ConstantTadHelper.h>
#include <array/DataTypeUtils.h>
namespace sd {
namespace ops {
Nd4jStatus LegacyReduce3Op::validateAndExecute(Context &block) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
NDArray::prepareSpecialUse({z}, {x, y});
int opNum = block.opNum() < 0 ? this->_opNum : block.opNum();
nd4j_debug("Executing LegacyReduce3Op: [%i]\n", opNum);
ExtraArguments extras(*block.getTArguments());
PointersManager manager(block.launchContext(), "LegacyReduce3Op");
if (x->isSameShape(y) && (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max<int>()))) {
// reduce3 to scalar
NativeOpExecutioner::execReduce3Scalar(block.launchContext(), opNum, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(),
extras.argumentsAsT(z->dataType()),
y->buffer(), y->shapeInfo(), y->specialBuffer(), y->specialShapeInfo(),
z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo());
} else {
std::vector<int> dims(*block.getAxis());
for (int e = 0; e < dims.size(); e++)
if (dims[e] < 0)
dims[e] += x->rankOf();
auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->shapeInfo(), dims);
auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z->shapeInfo(), dims);
REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions requuired for reduction!");
auto xTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tadX.tadOnlyShapeInfo, shape::shapeInfoByteLength(tadX.tadOnlyShapeInfo));
auto xTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tadX.tadOffsets, tadX.numTads * sizeof(Nd4jLong));
auto yTadShape = Environment::getInstance()->isCPU() ? packZ.primaryShapeInfo() : packZ.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tadY.tadOnlyShapeInfo, shape::shapeInfoByteLength(tadY.tadOnlyShapeInfo));
auto yTadOffsets = Environment::getInstance()->isCPU() ? packZ.primaryOffsets() : packZ.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tadY.tadOffsets, tadY.numTads * sizeof(Nd4jLong));
NativeOpExecutioner::execReduce3(block.launchContext(), opNum, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(),
extras.argumentsAsT(z->dataType()),
y->buffer(), y->shapeInfo(), y->specialBuffer(), y->specialShapeInfo(),
z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo(),
dims.data(), dims.size(), xTadShape, xTadOffsets, yTadShape, yTadOffsets);
}
manager.synchronize();
STORE_RESULT(*z);
return Status::OK();
}
LegacyReduce3Op::LegacyReduce3Op() : LegacyOp::LegacyOp(2) {
//
}
LegacyReduce3Op::LegacyReduce3Op(int opNum) : LegacyOp::LegacyOp(2, opNum) {
//
}
LegacyOp* LegacyReduce3Op::clone() {
return new LegacyReduce3Op(this->_opNum);
}
/**
* For all reductions rules are simple: either you return scalar, or you return reduced NDArray.
* It solely depends on input shape, and requested dimensions
*/
ShapeList *LegacyReduce3Op::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) {
auto xShape = inputShape->at(0);
auto yShape = inputShape->at(1);
Nd4jLong *zShape = nullptr;
if (shape::equalsSoft(xShape, yShape) && (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max<int>()))) {
// reduce3 to scalar case
ALLOCATE(zShape, block.getWorkspace(), shape::shapeInfoLength(2), Nd4jLong);
zShape[0] = 2;
zShape[1] = 1;
zShape[2] = 1;
zShape[3] = 1;
zShape[4] = 1;
zShape[5] = 0;
zShape[6] = 1;
zShape[7] = 99;
} else {
auto array = new NDArray(nullptr, xShape, block.launchContext());
xShape = ShapeUtils::evalReduceShapeInfo('c', *block.getIArguments(), *array, false, true);
delete array;
}
return SHAPELIST(zShape);
}
}
}