/*******************************************************************************
* 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
******************************************************************************/
//
// Created by george on 05.04.18.
//
#include <ops/declarable/helpers/dynamic.h>
#include <execution/Threads.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static void _dynamicPartitionFunctor(NDArray const* input, NDArray const* indices, std::vector<NDArray*>& outputList) {
std::vector<std::pair<NDArray *, int>> outputs(outputList.size());
int sourceDimsLen = input->rankOf() - indices->rankOf();
if (sourceDimsLen) {
std::vector<int> sourceDims(sourceDimsLen);
for (int i = sourceDimsLen; i > 0; i--)
sourceDims[sourceDimsLen - i] = input->rankOf() - i;
std::unique_ptr<ResultSet> listOfTensors(input->allTensorsAlongDimension(sourceDims));
unsigned int outSize = outputList.size();
//PRAGMA_OMP_PARALLEL_FOR_IF(outSize > Environment::getInstance()->tadThreshold())
for (unsigned int i = 0; i < outSize; i++) {
outputs[i].first = outputList[i];
std::vector<int> outDims(outputs[i].first->rankOf() - 1);
int r = outputs[i].first->rankOf();
for (int k = 1; k < r; k++)
outDims[k - 1] = k;
std::unique_ptr<ResultSet> listOutForCurrent(
outputs[i].first->allTensorsAlongDimension(outDims));
outputs[i].second = 0;
//PRAGMA_OMP_PARALLEL_FOR_IF(indices->lengthOf() > Environment::getInstance()->elementwiseThreshold())
for (int e = 0; e < indices->lengthOf(); ++e)
if ((*indices).e<Nd4jLong>(e) == i)
listOutForCurrent->at(outputs[i].second++)->assign(listOfTensors->at(e));
}
} else {
unsigned int outSize = outputList.size();
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
outputs[i].first = outputList[i];
outputs[i].second = 0;
for (int e = 0; e < indices->lengthOf(); ++e)
if (indices->e<Nd4jLong>(e) == i)
outputs[i].first->p(outputs[i].second++, input->e<T>(e));
}
};
samediff::Threads::parallel_tad(func, 0, outSize);
}
}
template <typename T>
static int _dynamicStitchFunctor(std::vector<NDArray*> const& inputs, std::vector<NDArray*> const& indices, NDArray* output){
int numOfData = inputs.size();
if (output->isVector()) {
for (int e = 0; e < numOfData; e++) {
auto data = inputs[e];
auto index = indices[e];
for (int i = 0; i < index->lengthOf(); i++) {
Nd4jLong pos = index->e<Nd4jLong>(i);
if (pos < 0) {
nd4j_printf("dynamic_stitch: Index value should be non-negative. But %i was given", pos);
return ND4J_STATUS_VALIDATION;
}
if (pos >= output->lengthOf()) {
nd4j_printf("dynamic_stitch: Index should be less than %i. But %i was given",
output->lengthOf(), pos);
return ND4J_STATUS_VALIDATION;
}
output->p<T>(pos, data->e<T>(i));
}
}
}
else {
std::vector<int> restDims(output->rankOf() - 1);
for (int i = restDims.size(); i > 0; i--)
restDims[restDims.size() - i] = output->rankOf() - i;
std::unique_ptr<ResultSet> listOfOutTensors(output->allTensorsAlongDimension(restDims));
for (int e = 0; e < numOfData; e++) {
auto data = inputs[e];
auto index = indices[e];
std::vector<int> sourceDims(data->rankOf() - index->rankOf());
for (int i = sourceDims.size(); i > 0; i--)
sourceDims[sourceDims.size() - i] = data->rankOf() - i;
std::unique_ptr<ResultSet> listOfTensors(data->allTensorsAlongDimension(sourceDims));
for (int i = 0; i < index->lengthOf(); i++) {
auto pos = index->e<Nd4jLong>(i);
if (pos < 0) {
nd4j_printf("dynamic_stitch: Index value should be non-negative. But %i was given", pos);
return ND4J_STATUS_VALIDATION;
}
if (pos >= output->lengthOf()) {
nd4j_printf("dynamic_stitch: Index should be less than %i. But %i was given",
output->lengthOf(), pos);
return ND4J_STATUS_VALIDATION;
}
listOfOutTensors->at(pos)->assign(listOfTensors->at(i));
}
}
}
return ND4J_STATUS_OK;
}
template <typename T>
static void _dynamicPartitionFunctorBP(NDArray const* input, NDArray const* indices, std::vector<NDArray*> const& inputGradientList, std::vector<NDArray*>& outputList) {
std::vector<std::pair<NDArray *, int>> outputs(inputGradientList.size());
int sourceDimsLen = input->rankOf() - indices->rankOf();
if (sourceDimsLen) { // multidimensional case
std::vector<int> sourceDims(sourceDimsLen);
for (int i = sourceDimsLen; i > 0; i--)
sourceDims[sourceDimsLen - i] = input->rankOf() - i;
std::unique_ptr<ResultSet> listOfTensors(outputList[0]->allTensorsAlongDimension(sourceDims));
for (unsigned int i = 0; i < inputGradientList.size(); i++) {
outputs[i].first = inputGradientList[i];
if (outputs[i].first->rankOf() < 1) continue; // skip empty gradient outs
std::vector<int> outDims(outputs[i].first->rankOf() - 1);
for (int k = 1; k < outputs[i].first->rankOf(); k++)
outDims[k - 1] = k;
std::unique_ptr<ResultSet> listOutForCurrent(
outputs[i].first->allTensorsAlongDimension(outDims));
outputs[i].second = 0;
for (int e = 0; e < indices->lengthOf(); ++e)
if (indices->e<Nd4jLong>(e) == i)
listOfTensors->at(e)->assign(listOutForCurrent->at(outputs[i].second++));
}
}
else { // one-dimensional case
auto output = outputList[0];
unsigned int gradsSize = inputGradientList.size();
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
outputs[i].first = inputGradientList[i];
outputs[i].second = 0;
for (int e = 0; e < indices->lengthOf(); ++e)
if (indices->e<Nd4jLong>(e) == i)
output->p<T>(e, outputs[i].first->e<T>(outputs[i].second++));
}
};
samediff::Threads::parallel_tad(func, 0, gradsSize);
}
outputList[1]->assign(indices);
}
void dynamicPartitionFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector<NDArray*>& outputList) {
auto xType = input->dataType();
BUILD_SINGLE_SELECTOR(xType, _dynamicPartitionFunctor, (input, indices, outputList), LIBND4J_TYPES);
}
template <typename T>
static int _dynamicStitchFunctorBP(std::vector<NDArray*> const& inputs, std::vector<NDArray*> const& indices, NDArray const* gradInput, std::vector<NDArray*>& outputList){
throw std::runtime_error("Not umplemented yet");
}
int dynamicStitchFunctor(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, std::vector<NDArray*> const& indices, NDArray* output){
auto xType = inputs.at(0)->dataType();
BUILD_SINGLE_SELECTOR(xType, return _dynamicStitchFunctor, (inputs, indices, output), LIBND4J_TYPES);
}
int dynamicStitchFunctorBP(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, std::vector<NDArray*> const& indices, NDArray const* gradInput, std::vector<NDArray*>& outputList) {
auto xType = inputs.at(0)->dataType();
BUILD_SINGLE_SELECTOR(xType, return _dynamicStitchFunctorBP, (inputs, indices, gradInput, outputList), LIBND4J_TYPES);
}
void dynamicPartitionFunctorBP(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector<NDArray*> const& inputGradientList, std::vector<NDArray*>& outputList) {
auto xType = input->dataType();
BUILD_SINGLE_SELECTOR(xType, _dynamicPartitionFunctorBP, (input, indices, inputGradientList, outputList), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void _dynamicPartitionFunctorBP, (NDArray const* input, NDArray const* indices, std::vector<NDArray*> const& inputGradientList, std::vector<NDArray*>& outputList);, LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template int _dynamicStitchFunctorBP, (std::vector<NDArray*> const& inputs, std::vector<NDArray*> const& indices, NDArray const* gradInput, std::vector<NDArray*>& outputList);, LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void _dynamicPartitionFunctor, (NDArray const* input, NDArray const* indices, std::vector<NDArray*>& outputList);, LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template int _dynamicStitchFunctor, (std::vector<NDArray*> const& inputs, std::vector<NDArray*> const& indices, NDArray* output);, LIBND4J_TYPES);
}
}
}