132 lines
4.8 KiB
C++
132 lines
4.8 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019-2020 Konduit K.K.
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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 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 <helpers/Loops.h>
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namespace sd {
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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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uint 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 auto xDim = input.sizeAt(axis);
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for (Nd4jLong 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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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.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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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]->getShapeInfo(), 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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void split(sd::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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