661 lines
22 KiB
C++
661 lines
22 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com, created on 07.10.2017.
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <system/pointercast.h>
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#include <helpers/shape.h>
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#include <helpers/TAD.h>
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#include <ops/specials.h>
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#include <system/dll.h>
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#include <array/NDArray.h>
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#include <ops/declarable/CustomOperations.h>
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#include <types/types.h>
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#include <helpers/Loops.h>
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namespace sd {
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/**
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* Concatneate multi array of the same shape together
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* along a particular dimension
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*/
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// template <typename T>
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// void SpecialMethods<T>::concatCpuGeneric(const std::vector<NDArray*>& inArrs, NDArray& output, const int axis) {
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// const uint numOfArrs = inArrs.size();
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// int outDim;
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// const bool isOutputVector = output.isCommonVector(outDim);
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// if(isOutputVector || (axis == 0 && output.ordering() == 'c')) {
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// bool allVectorsOrScalars = true;
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// const uint outEws = isOutputVector ? output.stridesOf()[outDim] : output.ews();
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// std::vector<int> nonUnityDim(numOfArrs);
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// std::vector<Nd4jLong> zOffset(numOfArrs);
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// for(int i = 0; i < numOfArrs; i++) {
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// allVectorsOrScalars &= (inArrs[i]->lengthOf() == 1 || inArrs[i]->isCommonVector(nonUnityDim[i]));
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// if(!allVectorsOrScalars)
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// break;
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// if(i == 0) zOffset[0] = 0;
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// else zOffset[i] = zOffset[i - 1] + outEws * inArrs[i - 1]->lengthOf();
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// }
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// if(allVectorsOrScalars) {
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// T* outBuff = output.bufferAsT<T>();
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// auto func = PRAGMA_THREADS_FOR {
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// for (auto r = start; r < stop; r += increment) {
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// const Nd4jLong arrLen = inArrs[r]->lengthOf();
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// const uint xEws = (arrLen == 1) ? 1 : inArrs[r]->stridesOf()[nonUnityDim[r]];
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// T *z = outBuff + zOffset[r];
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// T *x = inArrs[r]->bufferAsT<T>();
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// if (outEws == 1 && xEws == 1)
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// for (Nd4jLong e = 0; e < arrLen; e++)
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// z[e] = x[e];
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// else
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// for (Nd4jLong e = 0; e < arrLen; e++)
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// z[e * outEws] = x[e * xEws];
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// }
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// };
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// samediff::Threads::parallel_tad(func, 0, numOfArrs);
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// return;
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// }
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// }
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// const int rank = inArrs[0]->rankOf();
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// const int rank2 = 2*rank;
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// std::vector<std::vector<Nd4jLong>> indices(numOfArrs, std::vector<Nd4jLong>(rank2,0));
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// // take into account indices for first array
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// indices[0][2 * axis + 1] = inArrs[0]->sizeAt(axis);
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// // loop through the rest of input arrays
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// for(int i = 1; i < numOfArrs; ++i) {
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// indices[i][2 * axis] = indices[i-1][2 * axis + 1]; // index start from
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// indices[i][2 * axis + 1] = indices[i-1][2 * axis + 1] + inArrs[i]->sizeAt(axis); // index end with (excluding)
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// }
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// auto func = PRAGMA_THREADS_FOR {
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// for (auto i = start; i < stop; i += increment) {
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// auto temp = output(indices[i], true);
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// sd::TransformLoops<T, T, T>::template loopTransform<simdOps::Assign<T, T>>( inArrs[i]->bufferAsT<T>(), inArrs[i]->shapeInfo(), temp.bufferAsT<T>(), temp.shapeInfo(), nullptr, 0, 1);
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// }
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// };
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// samediff::Threads::parallel_tad(func, 0, numOfArrs);
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// }
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// static Nd4jLong strideOverContigAxis(const int axis, const Nd4jLong* inShapeInfo) {
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// Nd4jLong result = 9223372036854775807LL;
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// for(uint i = 0; i < shape::rank(inShapeInfo); ++i) {
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// const auto currentStride = shape::stride(inShapeInfo)[i];
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// if(i == axis || shape::shapeOf(inShapeInfo)[i] == 1)
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// continue;
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// if(result > currentStride)
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// result = currentStride;
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// }
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// return result == 9223372036854775807LL ? 1 : result;
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// }
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template <typename T>
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void SpecialMethods<T>::concatCpuGeneric(const std::vector<const NDArray*>& inArrs, NDArray& output, const int axis) {
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const int numOfInArrs = inArrs.size();
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const auto sizeofT = output.sizeOfT();
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T* zBuff = output.bufferAsT<T>();
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bool luckCase1 = ((axis == 0 && output.ordering() == 'c') || (axis == output.rankOf() - 1 && output.ordering() == 'f')) && output.ews() == 1;
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if(luckCase1) {
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for (uint i = 0; i < numOfInArrs; ++i) {
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luckCase1 &= inArrs[i]->ordering() == output.ordering() && inArrs[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) { // for example {1,10} + {2,10} + {3,10} = {6, 10} order c; or {10,1} + {10,2} + {10,3} = {10, 6} order f
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T* z = zBuff;
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for (uint i = 0; i < numOfInArrs; ++i) {
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const auto memAmountToCopy = inArrs[i]->lengthOf();
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memcpy(z, inArrs[i]->bufferAsT<T>(), memAmountToCopy * sizeofT);
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z += memAmountToCopy;
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}
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return;
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}
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// const bool isZcontin = output.strideAt(axis) == 1;
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// bool areInputsContin = true;
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// bool allSameOrder = true;
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// std::vector<Nd4jLong> strideOfContigStride(numOfInArrs);
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// if(isZcontin) {
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// for (uint i = 0; i < numOfInArrs; ++i) {
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// areInputsContin &= inArrs[i]->strideAt(axis) == 1;
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// allSameOrder &= inArrs[i]->ordering() == output.ordering();
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// if(!areInputsContin || !allSameOrder)
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// break;
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// strideOfContigStride[i] = strideOverContigAxis(axis, inArrs[i]->getShapeInfo());
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// }
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// }
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// const bool luckCase2 = isZcontin && areInputsContin && allSameOrder;
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// if(luckCase2) { // for example {2,1,3} + {2,5,3} + {2,10,3} = {2,16,3}, here axis 1 shoud have stride = 1 for all inputs arrays and output array
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// const auto zStep = strideOverContigAxis(axis, output.getShapeInfo());
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// for (uint i = 0; i < output.lengthOf() / output.sizeAt(axis); ++i) {
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// T* z = zBuff + zStep * i;
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// for (uint j = 0; j < inArrs.size(); ++j) {
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// const auto xDim = inArrs[j]->sizeAt(axis);
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// const T* x = inArrs[j]->bufferAsT<T>() + strideOfContigStride[j] * i;
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// memcpy(z, x, xDim * sizeofT);
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// z += xDim;
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// }
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// }
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// return;
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// }
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// general case
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auto func = PRAGMA_THREADS_FOR {
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int coords[MAX_RANK], temp;
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for (auto i = start; i < stop; i += increment) {
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shape::index2coordsCPU(start, i, output.shapeInfo(), coords);
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const auto zOffset = shape::getOffset(output.shapeInfo(), coords);
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uint inArrIdx = 0;
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uint xDim = inArrs[inArrIdx]->sizeAt(axis);
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temp = coords[axis];
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while (coords[axis] >= xDim) {
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coords[axis] -= xDim;
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xDim = inArrs[++inArrIdx]->sizeAt(axis);
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}
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const T* x = inArrs[inArrIdx]->bufferAsT<T>();
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const auto xOffset = shape::getOffset(inArrs[inArrIdx]->shapeInfo(), coords);
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zBuff[zOffset] = x[xOffset];
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coords[axis] = temp;
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}
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};
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samediff::Threads::parallel_for(func, 0, output.lengthOf());
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}
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/**
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* Concatneate multi array of the same shape together
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* along a particular dimension
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*/
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template <typename T>
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void SpecialMethods<T>::concatCpuGeneric(int dimension, int numArrays, Nd4jPointer *data, Nd4jPointer *inputShapeInfo, void *vresult, Nd4jLong const* resultShapeInfo) {
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auto result = reinterpret_cast<T *>(vresult);
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std::vector<const NDArray*> inputs(numArrays);
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NDArray output(static_cast<void*>(result), resultShapeInfo);
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for(int i = 0; i < numArrays; ++i)
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inputs[i] = new NDArray(static_cast<void *>(data[i]), static_cast<Nd4jLong*>(inputShapeInfo[i]));
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sd::SpecialMethods<T>::concatCpuGeneric(inputs, output, dimension);
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for(int i = 0; i < numArrays; ++i)
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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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auto 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;
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// bool areOutsContin = true;
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// bool allSameOrder = true;
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// std::vector<Nd4jLong> strideOfContigStride(numSplits);
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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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// strideOfContigStride[i] = shape::strideOverContigAxis(axis, outArrs[i]->shapeInfo());
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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 auto xStep = shape::strideOverContigAxis(axis, input.shapeInfo());
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// for (uint i = 0; i < input.lengthOf() / input.sizeAt(axis); ++i) {
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// T* x = xBuff + xStep * 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>() + strideOfContigStride[j] * i;
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// memcpy(z, x, zDim * sizeofT);
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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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int coords[MAX_RANK], temp;
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for (auto i = start; i < stop; i += increment) {
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shape::index2coordsCPU(start, i, input.shapeInfo(), coords);
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const auto xOffset = shape::getOffset(input.shapeInfo(), coords);
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uint outArrIdx = 0;
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temp = coords[axis];
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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]->shapeInfo(), coords);
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z[zOffset] = xBuff[xOffset];
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coords[axis] = temp;
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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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* @tparam T
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* @param x
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* @param z
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* @param n
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* @param length
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*/
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template<typename T>
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void SpecialMethods<T>::accumulateGeneric(void **vx, void *vz, Nd4jLong const* zShapeInfo, int n, const Nd4jLong length) {
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auto z = reinterpret_cast<T *>(vz);
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auto x = reinterpret_cast<T **>(vx);
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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for (auto ar = 0L; ar < n; ar++) {
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z[i] += x[ar][i];
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, length);
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}
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/**
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* This kernel averages X input arrays, and stores result to Z
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*
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* @tparam T
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* @param x
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* @param z
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* @param n
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* @param length
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* @param propagate
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*/
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template<typename T>
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void SpecialMethods<T>::averageGeneric(void **vx, void *vz, Nd4jLong const* zShapeInfo, int n, const Nd4jLong length, bool propagate) {
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auto z = reinterpret_cast<T *>(vz);
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auto x = reinterpret_cast<T **>(vx);
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if (z == nullptr) {
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//code branch for absent Z
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z = x[0];
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PRAGMA_OMP_SIMD
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for (uint64_t i = 0; i < length; i++) {
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z[i] /= static_cast<T>(n);
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}
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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for (Nd4jLong ar = 1; ar < n; ar++) {
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z[i] += x[ar][i] / static_cast<T>(n);
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, length);
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// instead of doing element-wise propagation, we just issue memcpy to propagate data
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for (Nd4jLong ar = 1; ar < n; ar++) {
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memcpy(x[ar], z, length * sizeof(T));
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}
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} else {
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// code branch for existing Z
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// memset before propagation
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memset(z, 0, length * sizeof(T));
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// aggregation step
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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for (Nd4jLong ar = 0; ar < n; ar++) {
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z[i] += x[ar][i] / static_cast<T>(n);
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, length);
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// instead of doing element-wise propagation, we just issue memcpy to propagate data
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for (Nd4jLong ar = 0; ar < n; ar++) {
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memcpy(x[ar], z, length * sizeof(T));
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}
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}
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}
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template <typename T>
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Nd4jLong SpecialMethods<T>::getPosition(Nd4jLong const* xShapeInfo, Nd4jLong index) {
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auto xEWS = shape::elementWiseStride(xShapeInfo);
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if (xEWS == 1)
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return index;
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else if (xEWS > 1)
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return index * xEWS;
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else
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return shape::getIndexOffset(index, xShapeInfo);
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}
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template<typename T>
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void SpecialMethods<T>::quickSort_parallel_internal(T* array, Nd4jLong const* xShapeInfo, int left, int right, int cutoff, bool descending) {
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int i = left, j = right;
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T tmp;
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T pivot = array[getPosition(xShapeInfo, (left + right) / 2)];
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{
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/* PARTITION PART */
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while (i <= j) {
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if (descending) {
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while (array[getPosition(xShapeInfo, i)] > pivot)
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i++;
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while (array[getPosition(xShapeInfo, j)] < pivot)
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j--;
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if (i <= j) {
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tmp = array[getPosition(xShapeInfo, i)];
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array[getPosition(xShapeInfo, i)] = array[getPosition(xShapeInfo, j)];
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array[getPosition(xShapeInfo, j)] = tmp;
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i++;
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j--;
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}
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} else {
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while (array[getPosition(xShapeInfo, i)] < pivot)
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i++;
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while (array[getPosition(xShapeInfo, j)] > pivot)
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j--;
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if (i <= j) {
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tmp = array[getPosition(xShapeInfo, i)];
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array[getPosition(xShapeInfo, i)] = array[getPosition(xShapeInfo, j)];
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array[getPosition(xShapeInfo, j)] = tmp;
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i++;
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j--;
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}
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}
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}
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}
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//
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if ( ((right-left)<cutoff) ){
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if (left < j){ quickSort_parallel_internal(array, xShapeInfo, left, j, cutoff, descending); }
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if (i < right){ quickSort_parallel_internal(array, xShapeInfo, i, right, cutoff, descending); }
|
|
|
|
}else{
|
|
PRAGMA_OMP_TASK
|
|
{ quickSort_parallel_internal(array, xShapeInfo, left, j, cutoff, descending); }
|
|
PRAGMA_OMP_TASK
|
|
{ quickSort_parallel_internal(array, xShapeInfo, i, right, cutoff, descending); }
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
void SpecialMethods<T>::quickSort_parallel(void *varray, Nd4jLong const* xShapeInfo, Nd4jLong lenArray, int numThreads, bool descending){
|
|
auto array = reinterpret_cast<T *>(varray);
|
|
int cutoff = 1000;
|
|
|
|
PRAGMA_OMP_PARALLEL_THREADS(numThreads)
|
|
{
|
|
PRAGMA_OMP_SINGLE_ARGS(nowait)
|
|
{
|
|
quickSort_parallel_internal(array, xShapeInfo, 0, lenArray-1, cutoff, descending);
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
template <typename T>
|
|
int SpecialMethods<T>::nextPowerOf2(int number) {
|
|
int pos = 0;
|
|
|
|
while (number > 0) {
|
|
pos++;
|
|
number = number >> 1;
|
|
}
|
|
return (int) pow(2, pos);
|
|
}
|
|
|
|
template <typename T>
|
|
int SpecialMethods<T>::lastPowerOf2(int number) {
|
|
int p = 1;
|
|
while (p <= number)
|
|
p <<= 1;
|
|
|
|
p >>= 1;
|
|
return p;
|
|
}
|
|
|
|
|
|
template<typename T>
|
|
void SpecialMethods<T>::sortGeneric(void *vx, Nd4jLong const* xShapeInfo, bool descending) {
|
|
auto x = reinterpret_cast<T *>(vx);
|
|
|
|
quickSort_parallel(x, xShapeInfo, shape::length(xShapeInfo), omp_get_max_threads(), descending);
|
|
}
|
|
|
|
template<typename T>
|
|
void SpecialMethods<T>::sortTadGeneric(void *vx, Nd4jLong const* xShapeInfo, int *dimension, int dimensionLength, Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets, bool descending) {
|
|
auto x = reinterpret_cast<T *>(vx);
|
|
|
|
//quickSort_parallel(x, xShapeInfo, shape::length(xShapeInfo), omp_get_max_threads(), descending);
|
|
Nd4jLong xLength = shape::length(xShapeInfo);
|
|
Nd4jLong xTadLength = shape::tadLength(xShapeInfo, dimension, dimensionLength);
|
|
int numTads = xLength / xTadLength;
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto r = start; r < stop; r++) {
|
|
T *dx = x + tadOffsets[r];
|
|
|
|
quickSort_parallel(dx, tadShapeInfo, xTadLength, 1, descending);
|
|
}
|
|
};
|
|
samediff::Threads::parallel_tad(func, 0, numTads);
|
|
}
|
|
|
|
|
|
template<typename T>
|
|
void SpecialMethods<T>::decodeBitmapGeneric(const void *dx, Nd4jLong N, void *vz, Nd4jLong const* zShapeInfo) {
|
|
auto dz = reinterpret_cast<T *>(vz);
|
|
auto x = reinterpret_cast<const int *>(dx);
|
|
Nd4jLong lim = N / 16 + 5;
|
|
|
|
FloatBits2 fb;
|
|
fb.i_ = x[2];
|
|
float threshold = fb.f_;
|
|
|
|
auto pPos = -1;
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e++) {
|
|
const auto v = x[e];
|
|
for (int bitId = 0; bitId < 16; bitId++) {
|
|
bool hasBit = (v & 1 << (bitId)) != 0;
|
|
bool hasSign = (v & 1 << (bitId + 16)) != 0;
|
|
auto cPos = (e - 4) * 16 + bitId;
|
|
|
|
if (hasBit) {
|
|
if (hasSign)
|
|
dz[cPos] -= static_cast<T>(threshold);
|
|
else
|
|
dz[cPos] += static_cast<T>(threshold);
|
|
} else if (hasSign) {
|
|
dz[cPos] -= static_cast<T>(threshold / 2);
|
|
}
|
|
|
|
pPos = cPos;
|
|
}
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 4, lim);
|
|
}
|
|
|
|
template<typename T>
|
|
Nd4jLong SpecialMethods<T>::encodeBitmapGeneric(void *vx, Nd4jLong const* xShapeInfo, Nd4jLong N, int *dz, float threshold) {
|
|
auto dx = reinterpret_cast<T *>(vx);
|
|
const T two(2.0f);
|
|
const T zero(0.0f);
|
|
const T t(threshold);
|
|
const T thalf = t / two;
|
|
|
|
//auto func = PRAGMA_REDUCE_LONG {
|
|
Nd4jLong retVal = 0L;
|
|
|
|
PRAGMA_OMP_PARALLEL_FOR_REDUCTION(+:retVal)
|
|
for (auto x = 0; x < N; x += 16) {
|
|
int byte = 0;
|
|
int byteId = x / 16 + 4;
|
|
|
|
for (int f = 0; f < 16; f++) {
|
|
auto e = x + f;
|
|
|
|
if (e >= N)
|
|
continue;
|
|
|
|
T val = dx[e];
|
|
T abs = sd::math::nd4j_abs<T>(val);
|
|
|
|
int bitId = e % 16;
|
|
|
|
if (abs >= t) {
|
|
byte |= 1 << (bitId);
|
|
retVal++;
|
|
|
|
if (val < zero) {
|
|
byte |= 1 << (bitId + 16);
|
|
dx[e] += t;
|
|
} else {
|
|
dx[e] -= t;
|
|
}
|
|
} else if (abs >= thalf && val < zero) {
|
|
byte |= 1 << (bitId + 16);
|
|
dx[e] += thalf;
|
|
|
|
retVal++;
|
|
}
|
|
}
|
|
|
|
dz[byteId] = byte;
|
|
}
|
|
|
|
return retVal;
|
|
//};
|
|
|
|
//return samediff::Threads::parallel_long(func, LAMBDA_SUML, 0, N, 16);
|
|
}
|
|
}
|
|
|