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Declarative Machine Learning
/*
* Licensed to the Apache Software Foundation (ASF) under one
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* 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
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.sysml.runtime.instructions.cp;
import org.apache.sysml.lops.UAggOuterChain;
import org.apache.sysml.lops.PartialAggregate.CorrectionLocationType;
import org.apache.sysml.runtime.DMLRuntimeException;
import org.apache.sysml.runtime.DMLUnsupportedOperationException;
import org.apache.sysml.runtime.controlprogram.context.ExecutionContext;
import org.apache.sysml.runtime.functionobjects.ReduceAll;
import org.apache.sysml.runtime.functionobjects.ReduceCol;
import org.apache.sysml.runtime.instructions.InstructionUtils;
import org.apache.sysml.runtime.matrix.data.LibMatrixOuterAgg;
import org.apache.sysml.runtime.matrix.data.MatrixBlock;
import org.apache.sysml.runtime.matrix.operators.AggregateOperator;
import org.apache.sysml.runtime.matrix.operators.AggregateUnaryOperator;
import org.apache.sysml.runtime.matrix.operators.BinaryOperator;
public class UaggOuterChainCPInstruction extends UnaryCPInstruction
{
//operators
private AggregateUnaryOperator _uaggOp = null;
private BinaryOperator _bOp = null;
public UaggOuterChainCPInstruction(BinaryOperator bop, AggregateUnaryOperator uaggop, AggregateOperator aggop, CPOperand in1, CPOperand in2, CPOperand out, String opcode, String istr )
{
super(bop, in1, in2, out, opcode, istr);
_cptype = CPINSTRUCTION_TYPE.UaggOuterChain;
_uaggOp = uaggop;
_bOp = bop;
instString = istr;
}
public static UaggOuterChainCPInstruction parseInstruction(String str)
throws DMLRuntimeException
{
String parts[] = InstructionUtils.getInstructionPartsWithValueType(str);
String opcode = parts[0];
if ( opcode.equalsIgnoreCase(UAggOuterChain.OPCODE)) {
AggregateUnaryOperator uaggop = InstructionUtils.parseBasicAggregateUnaryOperator(parts[1]);
BinaryOperator bop = InstructionUtils.parseBinaryOperator(parts[2]);
CPOperand in1 = new CPOperand(parts[3]);
CPOperand in2 = new CPOperand(parts[4]);
CPOperand out = new CPOperand(parts[5]);
//derive aggregation operator from unary operator
String aopcode = InstructionUtils.deriveAggregateOperatorOpcode(parts[1]);
CorrectionLocationType corrLoc = InstructionUtils.deriveAggregateOperatorCorrectionLocation(parts[1]);
String corrExists = (corrLoc != CorrectionLocationType.NONE) ? "true" : "false";
AggregateOperator aop = InstructionUtils.parseAggregateOperator(aopcode, corrExists, corrLoc.toString());
return new UaggOuterChainCPInstruction(bop, uaggop, aop, in1, in2, out, opcode, str);
}
else {
throw new DMLRuntimeException("UaggOuterChainCPInstruction.parseInstruction():: Unknown opcode " + opcode);
}
}
@Override
public void processInstruction(ExecutionContext ec)
throws DMLRuntimeException, DMLUnsupportedOperationException
{
boolean rightCached = (_uaggOp.indexFn instanceof ReduceCol || _uaggOp.indexFn instanceof ReduceAll
|| !LibMatrixOuterAgg.isSupportedUaggOp(_uaggOp, _bOp));
MatrixBlock mbLeft = null, mbRight = null, mbOut = null;
//get the main data input
if( rightCached ) {
mbLeft = ec.getMatrixInput(input1.getName());
mbRight = ec.getMatrixInput(input2.getName());
}
else {
mbLeft = ec.getMatrixInput(input2.getName());
mbRight = ec.getMatrixInput(input1.getName());
}
mbOut = mbLeft.uaggouterchainOperations(mbLeft, mbRight, mbOut, _bOp, _uaggOp);
//release locks
ec.releaseMatrixInput(input1.getName());
ec.releaseMatrixInput(input2.getName());
if( _uaggOp.aggOp.correctionExists )
mbOut.dropLastRowsOrColums(_uaggOp.aggOp.correctionLocation);
String output_name = output.getName();
//final aggregation if required
if(_uaggOp.indexFn instanceof ReduceAll ) //RC AGG (output is scalar)
{
//create and set output scalar
ScalarObject ret = null;
switch( output.getValueType() ) {
case DOUBLE: ret = new DoubleObject(output_name, mbOut.quickGetValue(0, 0)); break;
default:
throw new DMLRuntimeException("Invalid output value type: "+output.getValueType());
}
ec.setScalarOutput(output_name, ret);
}
else //R/C AGG (output is rdd)
{
//Additional memory requirement to convert from dense to sparse can be leveraged from released memory needed for input data above.
mbOut.examSparsity();
ec.setMatrixOutput(output_name, mbOut);
}
}
}