| /* |
| * 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.instructions.spark; |
| |
| |
| import org.apache.spark.api.java.JavaPairRDD; |
| import org.apache.spark.api.java.JavaRDD; |
| import org.apache.spark.api.java.function.Function; |
| import org.apache.spark.api.java.function.PairFlatMapFunction; |
| import org.apache.spark.api.java.function.PairFunction; |
| import org.apache.spark.broadcast.Broadcast; |
| import org.apache.sysds.hops.OptimizerUtils; |
| import org.apache.sysds.lops.MMTSJ.MMTSJType; |
| import org.apache.sysds.runtime.DMLRuntimeException; |
| import org.apache.sysds.runtime.controlprogram.context.ExecutionContext; |
| import org.apache.sysds.runtime.controlprogram.context.SparkExecutionContext; |
| import org.apache.sysds.runtime.functionobjects.Multiply; |
| import org.apache.sysds.runtime.functionobjects.Plus; |
| import org.apache.sysds.runtime.instructions.InstructionUtils; |
| import org.apache.sysds.runtime.instructions.cp.CPOperand; |
| import org.apache.sysds.runtime.instructions.spark.data.PartitionedBlock; |
| import org.apache.sysds.runtime.instructions.spark.functions.IsBlockInRange; |
| import org.apache.sysds.runtime.instructions.spark.utils.RDDAggregateUtils; |
| import org.apache.sysds.runtime.matrix.data.LibMatrixReorg; |
| import org.apache.sysds.runtime.matrix.data.MatrixBlock; |
| import org.apache.sysds.runtime.matrix.data.MatrixIndexes; |
| import org.apache.sysds.runtime.matrix.data.OperationsOnMatrixValues; |
| import org.apache.sysds.runtime.matrix.operators.AggregateBinaryOperator; |
| import org.apache.sysds.runtime.matrix.operators.AggregateOperator; |
| import org.apache.sysds.runtime.matrix.operators.Operator; |
| import org.apache.sysds.runtime.meta.DataCharacteristics; |
| import scala.Tuple2; |
| |
| import java.util.ArrayList; |
| import java.util.Iterator; |
| import java.util.List; |
| |
| public class Tsmm2SPInstruction extends UnarySPInstruction { |
| private MMTSJType _type = null; |
| |
| private Tsmm2SPInstruction(Operator op, CPOperand in1, CPOperand out, MMTSJType type, String opcode, String istr) { |
| super(SPType.TSMM2, op, in1, out, opcode, istr); |
| _type = type; |
| } |
| |
| public static Tsmm2SPInstruction parseInstruction( String str ) { |
| String parts[] = InstructionUtils.getInstructionPartsWithValueType(str); |
| String opcode = parts[0]; |
| //check supported opcode |
| if ( !opcode.equalsIgnoreCase("tsmm2") ) |
| throw new DMLRuntimeException("Tsmm2SPInstruction.parseInstruction():: Unknown opcode " + opcode); |
| CPOperand in1 = new CPOperand(parts[1]); |
| CPOperand out = new CPOperand(parts[2]); |
| MMTSJType type = MMTSJType.valueOf(parts[3]); |
| return new Tsmm2SPInstruction(null, in1, out, type, opcode, str); |
| } |
| |
| @Override |
| public void processInstruction(ExecutionContext ec) { |
| SparkExecutionContext sec = (SparkExecutionContext)ec; |
| |
| //get input |
| JavaPairRDD<MatrixIndexes,MatrixBlock> in = sec.getBinaryMatrixBlockRDDHandleForVariable( input1.getName() ); |
| DataCharacteristics mc = sec.getDataCharacteristics( input1.getName() ); |
| |
| //execute tsmm2 instruction |
| //step 1: first pass of X, filter-collect-broadcast excess blocks |
| JavaPairRDD<MatrixIndexes,MatrixBlock> tmp1 = |
| in.filter(new IsBlockInRange(_type.isLeft() ? 1 : mc.getBlocksize()+1, mc.getRows(), |
| _type.isLeft() ? mc.getBlocksize()+1 : 1, mc.getCols(), mc)) |
| .mapToPair(new ShiftTSMMIndexesFunction(_type)); |
| PartitionedBlock<MatrixBlock> pmb = SparkExecutionContext.toPartitionedMatrixBlock(tmp1, |
| (int)(_type.isLeft() ? mc.getRows() : mc.getRows() - mc.getBlocksize()), |
| (int)(_type.isLeft() ? mc.getCols()-mc.getBlocksize() : mc.getCols()), |
| mc.getBlocksize(), -1L); |
| Broadcast<PartitionedBlock<MatrixBlock>> bpmb = sec.getSparkContext().broadcast(pmb); |
| |
| //step 2: second pass of X, compute tsmm/mapmm and aggregate result blocks |
| int outputDim = (int) (_type.isLeft() ? mc.getCols() : mc.getRows()); |
| if( OptimizerUtils.estimateSize(outputDim, outputDim) <= 32*1024*1024 ) { //default: <=32MB |
| //output large blocks and reduceAll to avoid skew on combineByKey |
| JavaRDD<MatrixBlock> tmp2 = in.map( |
| new RDDTSMM2ExtFunction(bpmb, _type, outputDim, mc.getBlocksize())); |
| MatrixBlock out = RDDAggregateUtils.sumStable(tmp2); |
| |
| //put output block into symbol table (no lineage because single block) |
| //this also includes implicit maintenance of matrix characteristics |
| sec.setMatrixOutput(output.getName(), out); |
| } |
| else { |
| //output individual output blocks and aggregate by key (no action) |
| JavaPairRDD<MatrixIndexes,MatrixBlock> tmp2 = in.flatMapToPair(new RDDTSMM2Function(bpmb, _type)); |
| JavaPairRDD<MatrixIndexes,MatrixBlock> out = RDDAggregateUtils.sumByKeyStable(tmp2, false); |
| |
| //put output RDD handle into symbol table |
| sec.getDataCharacteristics(output.getName()).set(outputDim, outputDim, mc.getBlocksize(), mc.getBlocksize()); |
| sec.setRDDHandleForVariable(output.getName(), out); |
| sec.addLineageRDD(output.getName(), input1.getName()); |
| } |
| } |
| |
| private static class RDDTSMM2Function implements PairFlatMapFunction<Tuple2<MatrixIndexes, MatrixBlock>, MatrixIndexes, MatrixBlock> |
| { |
| private static final long serialVersionUID = 2935770425858019666L; |
| |
| private Broadcast<PartitionedBlock<MatrixBlock>> _pb = null; |
| private MMTSJType _type = null; |
| private AggregateBinaryOperator _op = null; |
| |
| public RDDTSMM2Function( Broadcast<PartitionedBlock<MatrixBlock>> pb, MMTSJType type ) { |
| _pb = pb; |
| _type = type; |
| |
| //created operator for reuse |
| AggregateOperator agg = new AggregateOperator(0, Plus.getPlusFnObject()); |
| _op = new AggregateBinaryOperator(Multiply.getMultiplyFnObject(), agg); |
| } |
| |
| @Override |
| public Iterator<Tuple2<MatrixIndexes, MatrixBlock>> call(Tuple2<MatrixIndexes, MatrixBlock> arg0) |
| throws Exception |
| { |
| List<Tuple2<MatrixIndexes,MatrixBlock>> ret = new ArrayList<>(); |
| MatrixIndexes ixin = arg0._1(); |
| MatrixBlock mbin = arg0._2(); |
| |
| //execute block tsmm operation |
| MatrixBlock out1 = mbin.transposeSelfMatrixMultOperations(new MatrixBlock(), _type); |
| long ixout = _type.isLeft() ? ixin.getColumnIndex() : ixin.getRowIndex(); |
| ret.add(new Tuple2<>(new MatrixIndexes(ixout, ixout), out1)); |
| |
| if( _type.isLeft() ? ixin.getColumnIndex() == 1 : ixin.getRowIndex() == 1 ) { |
| //execute block mapmm operation for full block only (output two blocks, due to symmetry) |
| MatrixBlock mbin2 = _pb.getValue().getBlock( //lookup broadcast block |
| (int)(_type.isLeft()?ixin.getRowIndex():1), |
| (int)(_type.isLeft()?1:ixin.getColumnIndex())); |
| MatrixBlock mbin2t = transpose(mbin2, new MatrixBlock()); //prep for transpose rewrite mm |
| |
| MatrixBlock out2 = OperationsOnMatrixValues.matMult( //mm |
| _type.isLeft() ? mbin2t : mbin, _type.isLeft() ? mbin : mbin2t, new MatrixBlock(), _op); |
| MatrixIndexes ixout2 = _type.isLeft() ? new MatrixIndexes(2,1) : new MatrixIndexes(1,2); |
| ret.add(new Tuple2<>(ixout2, out2)); |
| |
| MatrixBlock out3 = transpose(out2, new MatrixBlock()); |
| MatrixIndexes ixout3 = _type.isLeft() ? new MatrixIndexes(1,2) : new MatrixIndexes(2,1); |
| ret.add(new Tuple2<>(ixout3, out3)); |
| } |
| |
| return ret.iterator(); |
| } |
| } |
| |
| /** |
| * Same semantics as RDDTSMM2Function but output single consolidated block. |
| * |
| */ |
| private static class RDDTSMM2ExtFunction implements Function<Tuple2<MatrixIndexes, MatrixBlock>, MatrixBlock> |
| { |
| private static final long serialVersionUID = 3284059592407517911L; |
| |
| private Broadcast<PartitionedBlock<MatrixBlock>> _pb = null; |
| private MMTSJType _type = null; |
| private AggregateBinaryOperator _op = null; |
| private int _outputDim = -1; |
| private int _blen = -1; |
| |
| public RDDTSMM2ExtFunction( Broadcast<PartitionedBlock<MatrixBlock>> pb, MMTSJType type, int outputDim, int blen ) { |
| _pb = pb; |
| _type = type; |
| _outputDim = outputDim; |
| _blen = blen; |
| |
| //created operator for reuse |
| AggregateOperator agg = new AggregateOperator(0, Plus.getPlusFnObject()); |
| _op = new AggregateBinaryOperator(Multiply.getMultiplyFnObject(), agg); |
| } |
| |
| @Override |
| public MatrixBlock call(Tuple2<MatrixIndexes, MatrixBlock> arg0) |
| throws Exception |
| { |
| MatrixIndexes ixin = arg0._1(); |
| MatrixBlock mbin = arg0._2(); |
| |
| boolean fullBlock = _type.isLeft() ? ixin.getColumnIndex() == 1 : ixin.getRowIndex() == 1; |
| MatrixBlock out = new MatrixBlock(_outputDim, _outputDim, !fullBlock).allocateBlock(); |
| |
| //execute block tsmm operation |
| MatrixBlock out1 = mbin.transposeSelfMatrixMultOperations(new MatrixBlock(), _type); |
| int ix = (int) ((_type.isLeft() ? ixin.getColumnIndex() : ixin.getRowIndex())-1) * _blen; |
| out.copy(ix, ix+out1.getNumRows()-1, ix, ix+out1.getNumColumns()-1, out1, true); |
| |
| if( fullBlock ) { |
| //execute block mapmm operation for full block only (output two blocks, due to symmetry) |
| MatrixBlock mbin2 = _pb.getValue().getBlock( //lookup broadcast block |
| (int)(_type.isLeft()?ixin.getRowIndex():1), |
| (int)(_type.isLeft()?1:ixin.getColumnIndex())); |
| MatrixBlock mbin2t = transpose(mbin2, new MatrixBlock()); //prep for transpose rewrite mm |
| |
| MatrixBlock out2 = OperationsOnMatrixValues.matMult( //mm |
| _type.isLeft() ? mbin2t : mbin, _type.isLeft() ? mbin : mbin2t, new MatrixBlock(), _op); |
| |
| MatrixIndexes ixout2 = _type.isLeft() ? new MatrixIndexes(2,1) : new MatrixIndexes(1,2); |
| out.copy((int)(ixout2.getRowIndex()-1)*_blen, (int)(ixout2.getRowIndex()-1)*_blen+out2.getNumRows()-1, |
| (int)(ixout2.getColumnIndex()-1)*_blen, (int)(ixout2.getColumnIndex()-1)*_blen+out2.getNumColumns()-1, out2, true); |
| MatrixBlock out3 = transpose(out2, new MatrixBlock()); |
| out.copy((int)(ixout2.getColumnIndex()-1)*_blen, (int)(ixout2.getColumnIndex()-1)*_blen+out3.getNumRows()-1, |
| (int)(ixout2.getRowIndex()-1)*_blen, (int)(ixout2.getRowIndex()-1)*_blen+out3.getNumColumns()-1, out3, true); |
| } |
| |
| return out; |
| } |
| } |
| |
| private static class ShiftTSMMIndexesFunction implements PairFunction<Tuple2<MatrixIndexes, MatrixBlock>, MatrixIndexes, MatrixBlock> |
| { |
| private static final long serialVersionUID = -3858454295795680100L; |
| |
| private MMTSJType _type = null; |
| |
| public ShiftTSMMIndexesFunction( MMTSJType type ) { |
| _type = type; |
| } |
| |
| @Override |
| public Tuple2<MatrixIndexes, MatrixBlock> call(Tuple2<MatrixIndexes, MatrixBlock> arg0) |
| throws Exception |
| { |
| if( _type.isLeft() ) |
| return new Tuple2<>(new MatrixIndexes(arg0._1().getRowIndex(), 1), arg0._2()); |
| else |
| return new Tuple2<>(new MatrixIndexes(1, arg0._1().getColumnIndex()), arg0._2()); |
| } |
| } |
| |
| /** |
| * Helper function to setup output dimensions. |
| * |
| * @param in input matrix block |
| * @param out output matrix block |
| * @return matrix block |
| */ |
| private static MatrixBlock transpose(MatrixBlock in, MatrixBlock out) { |
| if( out == null ) |
| out = new MatrixBlock(in.getNumColumns(), in.getNumRows(), in.getNonZeros()); |
| else |
| out.reset(in.getNumColumns(), in.getNumRows(), in.getNonZeros()); |
| |
| return LibMatrixReorg.transpose(in, out); |
| } |
| } |