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cavis/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp

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/*******************************************************************************
* 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 Yurii Shyrma, created on 16.04.2018
//
#include <ops/declarable/helpers/reverse.h>
#include <helpers/ShapeUtils.h>
#include <array/ResultSet.h>
#include <execution/Threads.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
inline void swap(T* arr, Nd4jLong from, Nd4jLong to) {
T tmp = arr[from];
arr[from] = arr[to];
arr[to] = tmp;
}
/////////////////////////////////////////////////////////////////////////////////////
// this legacy op is written by raver119@gmail.com
template<typename T>
static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *inShapeBuffer, void *voutArr, Nd4jLong *outShapeBuffer, int numOfElemsToReverse = 0) {
auto inArr = reinterpret_cast<T *>(vinArr);
auto outArr = reinterpret_cast<T *>(voutArr);
Nd4jLong inLength = shape::length(inShapeBuffer);
Nd4jLong outLength = shape::length(outShapeBuffer);
if(numOfElemsToReverse == 0)
numOfElemsToReverse = inLength;
int inEWS = shape::elementWiseStride(inShapeBuffer);
char inOrder = shape::order(inShapeBuffer);
auto sLength = numOfElemsToReverse - 1;
// two step phase here
if (inArr == outArr) {
if (inEWS == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment) {
auto idx = sLength - e;
swap(inArr, e, idx);
}
};
samediff::Threads::parallel_for(func, 0, numOfElemsToReverse / 2);
}
else if (inEWS > 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment) {
auto idx1 = (sLength - e) * inEWS;
Nd4jLong idx2 = e * inEWS;
swap(inArr, idx1, idx2);
}
};
samediff::Threads::parallel_for(func, 0, numOfElemsToReverse / 2);
}
else {
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment) {
auto inOffset = shape::getIndexOffset(e, inShapeBuffer);
auto outOffset = shape::getIndexOffset(sLength - e, inShapeBuffer);
swap(outArr, inOffset, outOffset);
}
};
samediff::Threads::parallel_for(func, 0, numOfElemsToReverse / 2);
}
}
else {
// single step phase here
auto outEWS = shape::elementWiseStride(outShapeBuffer);
char outOrder = shape::order(outShapeBuffer);
if (inEWS == 1 && outEWS == 1 && inOrder == outOrder) {
auto func = PRAGMA_THREADS_FOR {
for (Nd4jLong e = start; e < stop; e += increment)
outArr[sLength - e] = inArr[e];
};
samediff::Threads::parallel_for(func, 0, numOfElemsToReverse);
if(inLength != numOfElemsToReverse) {
auto f2 = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment)
outArr[e] = inArr[e];
};
samediff::Threads::parallel_for(f2, numOfElemsToReverse, inLength);
}
}
else if (inEWS >= 1 && outEWS >= 1 && inOrder == outOrder) {
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment)
outArr[(sLength - e) * outEWS] = inArr[e * inEWS];
};
samediff::Threads::parallel_for(func, 0, numOfElemsToReverse);
if(inLength != numOfElemsToReverse) {
auto f2 = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment)
outArr[e * outEWS] = inArr[e * inEWS];
};
samediff::Threads::parallel_for(f2, numOfElemsToReverse, inLength);
}
}
else {
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment) {
auto inOffset = shape::getIndexOffset(e, inShapeBuffer);
auto outOffset = shape::getIndexOffset(sLength - e, outShapeBuffer);
outArr[outOffset] = inArr[inOffset];
}
};
samediff::Threads::parallel_for(func, 0, numOfElemsToReverse);
if(inLength != numOfElemsToReverse) {
auto f2 = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e += increment) {
auto inOffset = shape::getIndexOffset(e, inShapeBuffer);
auto outOffset = shape::getIndexOffset(e, outShapeBuffer);
outArr[outOffset] = inArr[inOffset];
}
};
samediff::Threads::parallel_for(f2, numOfElemsToReverse, inLength);
}
}
}
}
///////////////////////////////////////////////////////////////////
template <typename T>
static void reverseSequence_(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim){
int posOfNonUnityDim = -1;
if(input->isVector() || shape::isLikeVector(input->getShapeInfo(), posOfNonUnityDim)) {
if((seqDim == 0 && input->sizeAt(0) == 1) || (batchDim == posOfNonUnityDim))
output->assign(input);
else
helpers::reverseArray<T>(context, const_cast<NDArray*>(input)->getBuffer(), const_cast<NDArray*>(input)->getShapeInfo(), output->getBuffer(), output->getShapeInfo(), seqLengths->e<int>(0));
}
else {
if(seqDim > batchDim)
--seqDim;
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {batchDim});
auto inSubArrsSet = input->allTensorsAlongDimension(dimensions);
auto outSubArrsSet = output->allTensorsAlongDimension(dimensions);
for(int i = 0; i < inSubArrsSet.size(); ++i) {
Nd4jLong numOfElemsToReverse = seqLengths->e<Nd4jLong>(i);
if(numOfElemsToReverse == 0 || numOfElemsToReverse == 1) {
outSubArrsSet.at(i)->assign(inSubArrsSet.at(i));
}
else {
auto inInnerSet = inSubArrsSet.at(i)->allTensorsAlongDimension({seqDim});
auto outInnerSet = outSubArrsSet.at(i)->allTensorsAlongDimension({seqDim});
for(int j = 0; j < inInnerSet.size(); ++j)
helpers::reverseArray<T>(context, inInnerSet.at(j)->getBuffer(), inInnerSet.at(j)->getShapeInfo(), outInnerSet.at(j)->getBuffer(), outInnerSet.at(j)->getShapeInfo(), numOfElemsToReverse);
}
}
}
}
void reverseSequence(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim) {
BUILD_SINGLE_SELECTOR(input->dataType(), reverseSequence_, (context, input, seqLengths, output, seqDim, batchDim), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
void reverse(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector<int>* intArgs, bool isBackProp) {
// we need to reverse axis only if that's new op
std::vector<int> dimensions = isBackProp ? ShapeUtils::evalDimsToExclude(input->rankOf(), *intArgs) : *intArgs;
auto listOut = output->allTensorsAlongDimension(dimensions);
auto listIn = input->allTensorsAlongDimension(dimensions);
NDArray *subArrIn, *subArrOut;
for(int i = 0; i < listIn.size(); ++i) { // listIn.size() = listOut.size()
subArrIn = listIn.at(i);
subArrOut = listOut.at(i);
BUILD_SINGLE_SELECTOR(input->dataType(), helpers::reverseArray, (context, subArrIn->getBuffer(), subArrIn->getShapeInfo(), subArrOut->getBuffer(), subArrOut->getShapeInfo()), LIBND4J_TYPES);
}
}
BUILD_SINGLE_TEMPLATE(template void reverseSequence_, (nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, void *inArr, Nd4jLong *inShapeBuffer, void *outArr, Nd4jLong *outShapeBuffer, int numOfElemsToReverse), LIBND4J_TYPES);
}
}
}
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