Refactoring split operation (#266)
* libnd4j moved split operation implementation to helpers before special case adding Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j minor fixes for general split operation move, merge master Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libndj4 split cpu implementation Signed-off-by: Oleg <oleg.semeniv@gmail.com> * - provide cuda helper for split op Signed-off-by: Yurii <iuriish@yahoo.com> * - minor correction Signed-off-by: Yurii <iuriish@yahoo.com> * - minor correction 2 Signed-off-by: Yurii <iuriish@yahoo.com> * libnd4j moved split implementation from specials to split.cpp Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j update loopkind selections for 3D, 4D and 5D cases Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j removed unnecessary BUILD_SINGLE_TEMPLATE Signed-off-by: Oleg <oleg.semeniv@gmail.com> Co-authored-by: Yurii Shyrma <iuriish@yahoo.com>master
Oleh
GitHub
parent
cf67c7165a
commit
b686368b82
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@ -107,12 +107,22 @@ LoopKind::Kind LoopKind::deduceKindOfLoopBroadcast(const Nd4jLong* xShapeInfo, c
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bool bNotCommonVectorCase = (countUnityDimsInY != yRank - 1) && (countUnityDimsInX != xRank - 1);
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if (3 == xRank && bNDLoopsRanks && bNotCommonVectorCase)
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return nd4j::LoopKind::BROADCAST_3D;
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if (4 == xRank && bNDLoopsRanks && bNotCommonVectorCase)
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return nd4j::LoopKind::BROADCAST_4D;
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if (5 == xRank && bNDLoopsRanks && bNotCommonVectorCase)
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return nd4j::LoopKind::BROADCAST_5D;
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if (bNDLoopsRanks && bNotCommonVectorCase) {
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// case x[3,4,5] * y[1,4,5] = z[3,4,5] or reverse x[1,4,5] + y[3,4,5] = z[3,4,5]
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if (nd4j::LoopKind::EWS1 == deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo)
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&& (1 == shape::sizeAt(yShapeInfo, 0) || 1 == shape::sizeAt(xShapeInfo, 0))) {
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return EWS1;
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}
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if (3 == xRank)
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return nd4j::LoopKind::BROADCAST_3D;
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if (4 == xRank)
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return nd4j::LoopKind::BROADCAST_4D;
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if (5 == xRank)
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return nd4j::LoopKind::BROADCAST_5D;
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}
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if (xRank == yRank && xRank == zRank && xOrder == 'c' && yOrder == 'c' && zOrder == 'c' && xEws == 1 && yEws == 1 && zEws == 1 && xRank >= 2) {
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@ -18,26 +18,109 @@
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// @author Oleh Semeniv (oleg.semeniv@gmail.com)
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//
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#include <ops/declarable/helpers/transforms.h>
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#include <ops/specials.h>
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#include <helpers/Loops.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void split_(const NDArray& input, const std::vector<NDArray*>& outArrs, const int axis) {
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nd4j::SpecialMethods<T>::splitCpuGeneric(input, outArrs, axis);
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}
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void split(nd4j::LaunchContext* context, const NDArray& input, std::vector<NDArray*>& outArrs, const int axis) {
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BUILD_SINGLE_SELECTOR(input.dataType(), split_, (input, outArrs, axis), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void split_(const NDArray& input, const std::vector<NDArray*>& outArrs, const int axis) {
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int numSplits = outArrs.size();
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BUILD_SINGLE_TEMPLATE(template void split_, (const NDArray& input, const std::vector<NDArray*>& outArrs, const int axis), LIBND4J_TYPES);
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const auto sizeofT = input.sizeOfT();
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T* xBuff = input.bufferAsT<T>();
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bool luckCase1 = ((axis == 0 && input.ordering() == 'c') || (axis == input.rankOf() - 1 && input.ordering() == 'f')) && input.ews() == 1;
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if (luckCase1) {
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for (uint i = 0; i < numSplits; ++i) {
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luckCase1 &= outArrs[i]->ordering() == input.ordering() && outArrs[i]->ews() == 1;
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if (!luckCase1)
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break;
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}
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}
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if (luckCase1) {
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T* x = const_cast<T*>(xBuff);
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for (uint i = 0; i < numSplits; ++i) {
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const auto memAmountToCopy = outArrs[i]->lengthOf();
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memcpy(outArrs[i]->bufferAsT<T>(), x, memAmountToCopy * sizeofT);
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x += memAmountToCopy;
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}
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return;
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}
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const bool isXcontin = input.strideAt(axis) == 1 && input.ordering() == 'c';
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bool areOutsContin = true;
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bool allSameOrder = true;
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if (isXcontin) {
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for (uint i = 0; i < numSplits; ++i) {
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areOutsContin &= outArrs[i]->strideAt(axis) == 1;
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allSameOrder &= outArrs[i]->ordering() == input.ordering();
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if (!areOutsContin || !allSameOrder)
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break;
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}
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}
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const bool luckCase2 = isXcontin && areOutsContin && allSameOrder;
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if (luckCase2) {
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const uint xDim = input.sizeAt(axis);
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for (uint i = 0; i < input.lengthOf() / xDim; ++i) {
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T* x = xBuff + xDim * i;
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for (uint j = 0; j < numSplits; ++j) {
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const auto zDim = outArrs[j]->sizeAt(axis);
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T* z = outArrs[j]->bufferAsT<T>() + zDim * i;
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memcpy(z, x, zDim * sizeofT);
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z += zDim;
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x += zDim;
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}
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}
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return;
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}
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uint zDim = outArrs[0]->sizeAt(axis);
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// general case
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auto func = PRAGMA_THREADS_FOR{
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Nd4jLong coords[MAX_RANK];
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for (auto i = start; i < stop; i += increment) {
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shape::index2coords(i, input.getShapeInfo(), coords);
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const auto xOffset = shape::getOffset(input.getShapeInfo(), coords);
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uint outArrIdx = 0;
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while (coords[axis] >= zDim) {
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coords[axis] -= zDim;
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++outArrIdx;
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}
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T* z = outArrs[outArrIdx]->bufferAsT<T>();
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const auto zOffset = shape::getOffset(outArrs[outArrIdx]->getShapeInfo(), coords);
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z[zOffset] = xBuff[xOffset];
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}
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};
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samediff::Threads::parallel_for(func, 0, input.lengthOf());
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}
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void split(nd4j::LaunchContext* context, const NDArray& input, std::vector<NDArray*>& outArrs, const int axis) {
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BUILD_SINGLE_SELECTOR(input.dataType(), split_, (input, outArrs, axis), LIBND4J_TYPES);
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}
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}
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}
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}
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}
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}
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@ -217,100 +217,6 @@ void SpecialMethods<T>::concatCpuGeneric(int dimension, int numArrays, Nd4jPoint
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delete inputs[i];
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}
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template <typename T>
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void SpecialMethods<T>::splitCpuGeneric(const NDArray& input, const std::vector<NDArray*>& outArrs, const int axis) {
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int numSplits = outArrs.size();
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const auto sizeofT = input.sizeOfT();
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T* xBuff = input.bufferAsT<T>();
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bool luckCase1 = ((axis == 0 && input.ordering() == 'c') || (axis == input.rankOf() - 1 && input.ordering() == 'f')) && input.ews() == 1;
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if (luckCase1) {
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for (uint i = 0; i < numSplits; ++i) {
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luckCase1 &= outArrs[i]->ordering() == input.ordering() && outArrs[i]->ews() == 1;
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if (!luckCase1)
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break;
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}
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}
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if (luckCase1) {
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T* x = const_cast<T*>(xBuff);
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for (uint i = 0; i < numSplits; ++i) {
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const auto memAmountToCopy = outArrs[i]->lengthOf();
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memcpy(outArrs[i]->bufferAsT<T>(), x, memAmountToCopy * sizeofT);
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x += memAmountToCopy;
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}
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return;
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}
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const bool isXcontin = input.strideAt(axis) == 1 && input.ordering() == 'c';
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bool areOutsContin = true;
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bool allSameOrder = true;
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if (isXcontin) {
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for (uint i = 0; i < numSplits; ++i) {
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areOutsContin &= outArrs[i]->strideAt(axis) == 1;
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allSameOrder &= outArrs[i]->ordering() == input.ordering();
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if (!areOutsContin || !allSameOrder)
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break;
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}
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}
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const bool luckCase2 = isXcontin && areOutsContin && allSameOrder;
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if (luckCase2) {
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const uint xDim = input.sizeAt(axis);
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for (uint i = 0; i < input.lengthOf() / xDim; ++i) {
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T* x = xBuff + xDim * i;
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for (uint j = 0; j < numSplits; ++j) {
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const auto zDim = outArrs[j]->sizeAt(axis);
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T* z = outArrs[j]->bufferAsT<T>() + zDim * i;
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memcpy(z, x, zDim * sizeofT);
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z += zDim;
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x += zDim;
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}
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}
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return;
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}
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uint zDim = outArrs[0]->sizeAt(axis);
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// general case
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auto func = PRAGMA_THREADS_FOR{
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Nd4jLong coords[MAX_RANK];
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for (auto i = start; i < stop; i += increment) {
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shape::index2coords(i, input.getShapeInfo(), coords);
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const auto xOffset = shape::getOffset(input.getShapeInfo(), coords);
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uint outArrIdx = 0;
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while (coords[axis] >= zDim) {
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coords[axis] -= zDim;
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++outArrIdx;
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}
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T* z = outArrs[outArrIdx]->bufferAsT<T>();
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const auto zOffset = shape::getOffset(outArrs[outArrIdx]->getShapeInfo(), coords);
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z[zOffset] = xBuff[xOffset];
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}
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};
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samediff::Threads::parallel_for(func, 0, input.lengthOf());
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}
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/**
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* This kernel accumulates X arrays, and stores result into Z
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*
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@ -68,7 +68,6 @@ namespace nd4j {
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static void decodeBitmapGeneric(void *dx, Nd4jLong N, void *dz, Nd4jLong *zShapeInfo);
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static Nd4jLong encodeBitmapGeneric(void *dx, Nd4jLong *zShapeInfo, Nd4jLong N, int *dz, float threshold);
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static void splitCpuGeneric(const NDArray& input, const std::vector<NDArray*>& outArrs, const int axis);
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};
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template <typename X, typename Y>
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