| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://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. |
| */ |
| |
| package org.apache.sysds.runtime.controlprogram.paramserv.dp; |
| |
| import org.apache.sysds.runtime.DMLRuntimeException; |
| import org.apache.sysds.runtime.controlprogram.caching.MatrixObject; |
| import org.apache.sysds.runtime.controlprogram.context.ExecutionContext; |
| import org.apache.sysds.runtime.controlprogram.federated.FederatedData; |
| import org.apache.sysds.runtime.controlprogram.federated.FederatedRequest; |
| import org.apache.sysds.runtime.controlprogram.federated.FederatedResponse; |
| import org.apache.sysds.runtime.controlprogram.federated.FederatedUDF; |
| import org.apache.sysds.runtime.controlprogram.paramserv.ParamservUtils; |
| import org.apache.sysds.runtime.instructions.cp.Data; |
| import org.apache.sysds.runtime.matrix.data.MatrixBlock; |
| import org.apache.sysds.runtime.meta.DataCharacteristics; |
| |
| import java.util.List; |
| import java.util.concurrent.Future; |
| |
| /** |
| * Subsample to Min Federated scheme |
| * |
| * When the parameter server runs in federated mode it cannot pull in the data which is already on the workers. |
| * Therefore, a UDF is sent to manipulate the data locally. In this case the global minimum number of examples is taken |
| * and the worker subsamples data to match that number of examples. The subsampling is done by multiplying with a |
| * Permutation Matrix with a global seed. |
| * |
| * Then all entries in the federation map of the input matrix are separated into MatrixObjects and returned as a list. |
| * Only supports row federated matrices atm. |
| */ |
| public class SubsampleToMinFederatedScheme extends DataPartitionFederatedScheme { |
| @Override |
| public Result partition(MatrixObject features, MatrixObject labels, int seed) { |
| List<MatrixObject> pFeatures = sliceFederatedMatrix(features); |
| List<MatrixObject> pLabels = sliceFederatedMatrix(labels); |
| List<Double> weighingFactors = getWeighingFactors(pFeatures, getBalanceMetrics(pFeatures)); |
| |
| int min_rows = Integer.MAX_VALUE; |
| for (MatrixObject pFeature : pFeatures) { |
| min_rows = (pFeature.getNumRows() < min_rows) ? Math.toIntExact(pFeature.getNumRows()) : min_rows; |
| } |
| |
| for(int i = 0; i < pFeatures.size(); i++) { |
| // Works, because the map contains a single entry |
| FederatedData featuresData = (FederatedData) pFeatures.get(i).getFedMapping().getMap().values().toArray()[0]; |
| FederatedData labelsData = (FederatedData) pLabels.get(i).getFedMapping().getMap().values().toArray()[0]; |
| |
| Future<FederatedResponse> udfResponse = featuresData.executeFederatedOperation(new FederatedRequest(FederatedRequest.RequestType.EXEC_UDF, |
| featuresData.getVarID(), new subsampleDataOnFederatedWorker(new long[]{featuresData.getVarID(), labelsData.getVarID()}, seed, min_rows))); |
| |
| try { |
| FederatedResponse response = udfResponse.get(); |
| if(!response.isSuccessful()) |
| throw new DMLRuntimeException("FederatedDataPartitioner SubsampleFederatedScheme: subsample UDF returned fail"); |
| } |
| catch(Exception e) { |
| throw new DMLRuntimeException("FederatedDataPartitioner SubsampleFederatedScheme: executing subsample UDF failed" + e.getMessage()); |
| } |
| |
| DataCharacteristics update = pFeatures.get(i).getDataCharacteristics().setRows(min_rows); |
| pFeatures.get(i).updateDataCharacteristics(update); |
| update = pLabels.get(i).getDataCharacteristics().setRows(min_rows); |
| pLabels.get(i).updateDataCharacteristics(update); |
| } |
| |
| return new Result(pFeatures, pLabels, pFeatures.size(), getBalanceMetrics(pFeatures), weighingFactors); |
| } |
| |
| /** |
| * Subsample UDF executed on the federated worker |
| */ |
| private static class subsampleDataOnFederatedWorker extends FederatedUDF { |
| private static final long serialVersionUID = 2213790859544004286L; |
| private final int _seed; |
| private final int _min_rows; |
| |
| protected subsampleDataOnFederatedWorker(long[] inIDs, int seed, int min_rows) { |
| super(inIDs); |
| _seed = seed; |
| _min_rows = min_rows; |
| } |
| |
| @Override |
| public FederatedResponse execute(ExecutionContext ec, Data... data) { |
| MatrixObject features = (MatrixObject) data[0]; |
| MatrixObject labels = (MatrixObject) data[1]; |
| |
| // subsample down to minimum |
| if(features.getNumRows() > _min_rows) { |
| // generate subsampling matrix |
| MatrixBlock subsampleMatrixBlock = ParamservUtils.generateSubsampleMatrix(_min_rows, Math.toIntExact(features.getNumRows()), _seed); |
| subsampleTo(features, subsampleMatrixBlock); |
| subsampleTo(labels, subsampleMatrixBlock); |
| } |
| |
| return new FederatedResponse(FederatedResponse.ResponseType.SUCCESS); |
| } |
| } |
| } |