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

181 lines
7.6 KiB
C++

/*******************************************************************************
* 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 (iuriish@yahoo.com), created on 07.03.2019
//
#include <ops/declarable/helpers/gather.h>
#include <numeric>
#include <execution/Threads.h>
#include <ShapeUtils.h>
#include <ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
////////////////////////////////////////////////////////////////////////
void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
int axis = intArgs.size() > 0 ? intArgs[0] : 0;
const int inputRank = input->rankOf();
if(axis < 0)
axis += inputRank;
const int numOfIntArgs = intArgs.size();
if (indices != nullptr) {
// first case: indices consist of only one scalar
if(indices->isScalar()) {
if(input->rankOf() <= 1){
//For scalar indices, rank 0 or 1 input: can't do tensor along dimension 0 as this is whole array... instead, we want to get a scalar
auto idx = indices->e<Nd4jLong>(0);
auto scalarNDArray = input->e(idx);
output->assign(scalarNDArray);
}
else {
NDArray inSubArr = (*input)(indices->e<Nd4jLong>(0), {axis});
output->assign(inSubArr);
}
}
else {
if(input->rankOf() == 1 && output->rankOf() == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++)
output->p(i, input->e(indices->e<Nd4jLong>(i)));
};
samediff::Threads::parallel_for(func, 0, output->lengthOf());
}
else {
std::vector<int> dimsOut;
for (int i = 0; i < axis; ++i)
dimsOut.push_back(i);
for (int i = axis+indices->rankOf(); i < output->rankOf(); ++i)
dimsOut.push_back(i);
std::vector<int> dimsIn = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
const Nd4jLong numOfSubArrs = indices->lengthOf();
auto inTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsIn);
auto outTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimsOut);
Nd4jLong* inTadShapeInfo = inTadPack.primaryShapeInfo();
Nd4jLong* outTadShapeInfo = outTadPack.primaryShapeInfo();
if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
else {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/,
outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/,
nullptr, nullptr, nullptr, false/*allowParallelism*/);
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
}
}
}
else {
// we only allow scalar/vector case here
if (numOfIntArgs == 2) { // scalar case
output->assign((*input)(intArgs[1], {axis}));
}
else { // vector case
const Nd4jLong numOfSubArrs = intArgs.size() - 1;
std::vector<int> dims = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
auto inTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dims);
auto outTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dims);
Nd4jLong* inTadShapeInfo = inTadPack.primaryShapeInfo();
Nd4jLong* outTadShapeInfo = outTadPack.primaryShapeInfo();
if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
std::memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
else {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/,
outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/,
nullptr, nullptr, nullptr, false/*allowParallelism*/);
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
}
}
}
}
}
}