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