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

127 lines
4.7 KiB
C++
Raw Normal View History

/*******************************************************************************
* Copyright (c) 2019-2020 Konduit K.K.
*
* 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 Oleh Semeniv (oleg.semeniv@gmail.com)
//
#include <ops/declarable/helpers/transforms.h>
#include <helpers/Loops.h>
namespace nd4j {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void split_(const NDArray& input, const std::vector<NDArray*>& outArrs, const int axis) {
uint numSplits = outArrs.size();
const auto sizeofT = input.sizeOfT();
T* xBuff = input.bufferAsT<T>();
bool luckCase1 = ((axis == 0 && input.ordering() == 'c') || (axis == input.rankOf() - 1 && input.ordering() == 'f')) && input.ews() == 1;
if (luckCase1) {
for (uint i = 0; i < numSplits; ++i) {
luckCase1 &= outArrs[i]->ordering() == input.ordering() && outArrs[i]->ews() == 1;
if (!luckCase1)
break;
}
}
if (luckCase1) {
T* x = const_cast<T*>(xBuff);
for (uint i = 0; i < numSplits; ++i) {
const auto memAmountToCopy = outArrs[i]->lengthOf();
memcpy(outArrs[i]->bufferAsT<T>(), x, memAmountToCopy * sizeofT);
x += memAmountToCopy;
}
return;
}
const bool isXcontin = input.strideAt(axis) == 1 && input.ordering() == 'c';
bool areOutsContin = true;
bool allSameOrder = true;
if (isXcontin) {
for (uint i = 0; i < numSplits; ++i) {
areOutsContin &= outArrs[i]->strideAt(axis) == 1;
allSameOrder &= outArrs[i]->ordering() == input.ordering();
if (!areOutsContin || !allSameOrder)
break;
}
}
const bool luckCase2 = isXcontin && areOutsContin && allSameOrder;
if (luckCase2) {
const auto xDim = input.sizeAt(axis);
for (Nd4jLong i = 0; i < input.lengthOf() / xDim; ++i) {
T* x = xBuff + xDim * i;
for (uint j = 0; j < numSplits; ++j) {
const auto zDim = outArrs[j]->sizeAt(axis);
T* z = outArrs[j]->bufferAsT<T>() + zDim * i;
memcpy(z, x, zDim * sizeofT);
z += zDim;
x += zDim;
}
}
return;
}
uint zDim = outArrs[0]->sizeAt(axis);
// general case
auto func = PRAGMA_THREADS_FOR{
Nd4jLong coords[MAX_RANK];
for (auto i = start; i < stop; i += increment) {
shape::index2coords(i, input.getShapeInfo(), coords);
const auto xOffset = shape::getOffset(input.getShapeInfo(), coords);
uint outArrIdx = 0;
while (coords[axis] >= zDim) {
coords[axis] -= zDim;
++outArrIdx;
}
T* z = outArrs[outArrIdx]->bufferAsT<T>();
const auto zOffset = shape::getOffset(outArrs[outArrIdx]->getShapeInfo(), coords);
z[zOffset] = xBuff[xOffset];
}
};
samediff::Threads::parallel_for(func, 0, input.lengthOf());
}
void split(nd4j::LaunchContext* context, const NDArray& input, std::vector<NDArray*>& outArrs, const int axis) {
BUILD_SINGLE_SELECTOR(input.dataType(), split_, (input, outArrs, axis), LIBND4J_TYPES);
}
}
}
}