cavis/libnd4j/include/ops/declarable/impl/BroadcastableOp.cpp

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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 raver on 6/6/2018.
//
#include <op_boilerplate.h>
#include <pointercast.h>
#include <ops/declarable/BroadcastableOp.h>
#include <helpers/ShapeUtils.h>
namespace nd4j {
namespace ops {
BroadcastableOp::BroadcastableOp(const char *name, int numTArgs, int numIArgs) : DeclarableCustomOp::DeclarableCustomOp(2, 1, name, false, numTArgs, numIArgs) {
//
}
BroadcastableOp::~BroadcastableOp() {
// no-op
}
ShapeList *BroadcastableOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) {
auto shapeList = SHAPELIST();
auto x = inputShape->at(0);
auto y = inputShape->at(1);
auto outputs = _descriptor->getOutputTypesForOutput(0);
nd4j::DataType dtype = block.dataType(0);
if (block.dataType(0) != nd4j::DataType::BOOL && !(outputs.size() == 1 && outputs[0] == nd4j::DataType::BOOL)) {
if (Environment::getInstance()->isExperimentalBuild()) {
if (shape::length(y) > shape::length(x)) {
dtype = DataTypeUtils::pickPairwiseResultType(y, x);
} else {
dtype = DataTypeUtils::pickPairwiseResultType(x, y);
}
} else {
dtype = ArrayOptions::dataType(x);
}
} else
dtype = nd4j::DataType::BOOL;
if(shape::isEmpty(x) || shape::isEmpty(y)) {
Dev branch merge: dev_20190606 (#7904) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks
2019-06-15 13:34:34 +02:00
// this is edge case, [3, 4] + [] = []
if ((shape::isEmpty(x) && shape::rank(x) == 0) || (shape::isEmpty(y) && shape::rank(y) == 0)) {
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor::emptyDescriptor(dtype)));
return shapeList;
}
Nd4jLong *newshape = nullptr;
ShapeUtils::evalBroadcastShapeInfo(x, y, true, newshape, block.workspace());
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(newshape, dtype)));
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} else if (shape::isScalar(x) && shape::isScalar(y)) {
if (shape::rank(x) >= shape::rank(y)) {
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(x, dtype)));
} else {
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(y, dtype)));
}
} else if (shape::equalsSoft(x, y)) {
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(x, dtype)));
} else if (shape::isScalar(x) && !shape::isScalar(y)) {
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(y, dtype)));
} else if (!shape::isScalar(x) && shape::isScalar(y)) {
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(x, dtype)));
} else if (ShapeUtils::areShapesBroadcastable(x, y)) {
Nd4jLong *newshape = nullptr;
ShapeUtils::evalBroadcastShapeInfo(x, y, true, newshape, block.workspace());
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(newshape, dtype)));
} else {
// in this case we'll throw exception later
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(x, dtype)));
}
return shapeList;
}
}
}