| /* |
| * 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.common.Types; |
| import org.apache.sysds.lops.compile.Dag; |
| import org.apache.sysds.runtime.DMLRuntimeException; |
| import org.apache.sysds.runtime.controlprogram.caching.MatrixObject; |
| import org.apache.sysds.runtime.controlprogram.federated.FederatedData; |
| import org.apache.sysds.runtime.controlprogram.federated.FederatedRange; |
| import org.apache.sysds.runtime.controlprogram.federated.FederationMap; |
| import org.apache.sysds.runtime.controlprogram.parfor.stat.InfrastructureAnalyzer; |
| import org.apache.sysds.runtime.instructions.InstructionUtils; |
| import org.apache.sysds.runtime.matrix.data.MatrixBlock; |
| import org.apache.sysds.runtime.matrix.operators.AggregateBinaryOperator; |
| import org.apache.sysds.runtime.meta.MatrixCharacteristics; |
| import org.apache.sysds.runtime.meta.MetaDataFormat; |
| |
| import java.util.ArrayList; |
| import java.util.Collections; |
| import java.util.HashMap; |
| import java.util.List; |
| |
| public abstract class DataPartitionFederatedScheme { |
| |
| public static final class Result { |
| public final List<MatrixObject> _pFeatures; |
| public final List<MatrixObject> _pLabels; |
| public final int _workerNum; |
| public final BalanceMetrics _balanceMetrics; |
| |
| public Result(List<MatrixObject> pFeatures, List<MatrixObject> pLabels, int workerNum, BalanceMetrics balanceMetrics) { |
| this._pFeatures = pFeatures; |
| this._pLabels = pLabels; |
| this._workerNum = workerNum; |
| this._balanceMetrics = balanceMetrics; |
| } |
| } |
| |
| public abstract Result doPartitioning(MatrixObject features, MatrixObject labels); |
| |
| /** |
| * Takes a row federated Matrix and slices it into a matrix for each worker |
| * |
| * @param fedMatrix the federated input matrix |
| */ |
| static List<MatrixObject> sliceFederatedMatrix(MatrixObject fedMatrix) { |
| if (fedMatrix.isFederated(FederationMap.FType.ROW)) { |
| List<MatrixObject> slices = Collections.synchronizedList(new ArrayList<>()); |
| fedMatrix.getFedMapping().forEachParallel((range, data) -> { |
| // Create sliced matrix object |
| MatrixObject slice = new MatrixObject(fedMatrix.getValueType(), Dag.getNextUniqueVarname(Types.DataType.MATRIX)); |
| slice.setMetaData(new MetaDataFormat( |
| new MatrixCharacteristics(range.getSize(0), range.getSize(1)), |
| Types.FileFormat.BINARY) |
| ); |
| |
| // Create new federation map |
| HashMap<FederatedRange, FederatedData> newFedHashMap = new HashMap<>(); |
| newFedHashMap.put(range, data); |
| slice.setFedMapping(new FederationMap(fedMatrix.getFedMapping().getID(), newFedHashMap)); |
| slice.getFedMapping().setType(FederationMap.FType.ROW); |
| |
| slices.add(slice); |
| return null; |
| }); |
| |
| return slices; |
| } |
| else { |
| throw new DMLRuntimeException("Federated data partitioner: " + |
| "currently only supports row federated data"); |
| } |
| } |
| |
| static BalanceMetrics getBalanceMetrics(List<MatrixObject> slices) { |
| if (slices == null || slices.size() == 0) |
| return new BalanceMetrics(0, 0, 0); |
| |
| long minRows = slices.get(0).getNumRows(); |
| long maxRows = minRows; |
| long sum = 0; |
| |
| for (MatrixObject slice : slices) { |
| if (slice.getNumRows() < minRows) |
| minRows = slice.getNumRows(); |
| else if (slice.getNumRows() > maxRows) |
| maxRows = slice.getNumRows(); |
| |
| sum += slice.getNumRows(); |
| } |
| |
| return new BalanceMetrics(minRows, sum / slices.size(), maxRows); |
| } |
| |
| public static final class BalanceMetrics { |
| public final long _minRows; |
| public final long _avgRows; |
| public final long _maxRows; |
| |
| public BalanceMetrics(long minRows, long avgRows, long maxRows) { |
| this._minRows = minRows; |
| this._avgRows = avgRows; |
| this._maxRows = maxRows; |
| } |
| } |
| |
| /** |
| * Just a mat multiply used to shuffle with a provided shuffle matrixBlock |
| * |
| * @param m the input matrix object |
| * @param P the permutation matrix for shuffling |
| */ |
| static void shuffle(MatrixObject m, MatrixBlock P) { |
| int k = InfrastructureAnalyzer.getLocalParallelism(); |
| AggregateBinaryOperator mm = InstructionUtils.getMatMultOperator(k); |
| MatrixBlock out = P.aggregateBinaryOperations(P, m.acquireReadAndRelease(), new MatrixBlock(), mm); |
| m.acquireModify(out); |
| m.release(); |
| } |
| |
| /** |
| * Takes a MatrixObjects and extends it to the chosen number of rows by random replication |
| * |
| * @param m the input matrix object |
| * @param R the permutation matrix for replication |
| */ |
| static void replicateTo(MatrixObject m, MatrixBlock R) { |
| int k = InfrastructureAnalyzer.getLocalParallelism(); |
| AggregateBinaryOperator mm = InstructionUtils.getMatMultOperator(k); |
| MatrixBlock out = R.aggregateBinaryOperations(R, m.acquireReadAndRelease(), new MatrixBlock(), mm); |
| m.acquireModify(m.acquireReadAndRelease().append(out, new MatrixBlock(), false)); |
| m.release(); |
| } |
| |
| /** |
| * Just a mat multiply used to subsample with a provided subsample matrixBlock |
| * |
| * @param m the input matrix object |
| * @param R the permutation matrix for subsampling |
| */ |
| static void subsampleTo(MatrixObject m, MatrixBlock S) { |
| int k = InfrastructureAnalyzer.getLocalParallelism(); |
| AggregateBinaryOperator mm = InstructionUtils.getMatMultOperator(k); |
| MatrixBlock out = S.aggregateBinaryOperations(S, m.acquireReadAndRelease(), new MatrixBlock(), mm); |
| m.acquireModify(out); |
| m.release(); |
| } |
| } |