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Declarative Machine Learning
/*
* 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.sysml.runtime.instructions.mr;
import java.util.ArrayList;
import org.apache.sysml.lops.Lop;
import org.apache.sysml.parser.Expression.DataType;
import org.apache.sysml.runtime.DMLRuntimeException;
import org.apache.sysml.runtime.DMLUnsupportedOperationException;
import org.apache.sysml.runtime.instructions.InstructionUtils;
import org.apache.sysml.runtime.matrix.data.MatrixValue;
import org.apache.sysml.runtime.matrix.data.OperationsOnMatrixValues;
import org.apache.sysml.runtime.matrix.mapred.CachedValueMap;
import org.apache.sysml.runtime.matrix.mapred.IndexedMatrixValue;
import org.apache.sysml.runtime.matrix.operators.ScalarOperator;
public class ScalarInstruction extends UnaryMRInstructionBase
{
public ScalarInstruction(ScalarOperator op, byte in, byte out, String istr)
{
super(op, in, out);
mrtype = MRINSTRUCTION_TYPE.ArithmeticBinary;
instString = istr;
//value dependent safe-safeness (trigger re-evaluation sparse-safe)
op.setConstant(op.getConstant());
}
/**
*
* @param str
* @return
* @throws DMLRuntimeException
*/
public static ScalarInstruction parseInstruction ( String str )
throws DMLRuntimeException
{
InstructionUtils.checkNumFields ( str, 3 );
String[] parts = InstructionUtils.getInstructionParts ( str );
String opcode = parts[0];
boolean firstArgScalar = isFirstArgumentScalar(str);
double cst = Double.parseDouble( firstArgScalar ? parts[1] : parts[2]);
byte in = Byte.parseByte( firstArgScalar ? parts[2] : parts[1]);
byte out = Byte.parseByte(parts[3]);
ScalarOperator sop = InstructionUtils.parseScalarBinaryOperator(opcode, firstArgScalar, cst);
return new ScalarInstruction(sop, in, out, str);
}
public void processInstruction(Class valueClass, CachedValueMap cachedValues,
IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor)
throws DMLUnsupportedOperationException, DMLRuntimeException
{
ArrayList blkList = cachedValues.get(input);
if( blkList != null )
for( IndexedMatrixValue in : blkList )
{
if(in==null)
continue;
//allocate space for the output value
IndexedMatrixValue out;
if(input==output)
out=tempValue;
else
out=cachedValues.holdPlace(output, valueClass);
//process instruction
out.getIndexes().setIndexes(in.getIndexes());
OperationsOnMatrixValues.performScalarIgnoreIndexes(in.getValue(), out.getValue(), ((ScalarOperator)this.optr));
//put the output value in the cache
if(out==tempValue)
cachedValues.add(output, out);
}
}
/**
*
* @param inst
* @return
*/
private static boolean isFirstArgumentScalar(String inst)
{
//get first argument
String[] parts = InstructionUtils.getInstructionPartsWithValueType(inst);
String arg1 = parts[1];
//get data type of first argument
String[] subparts = arg1.split(Lop.VALUETYPE_PREFIX);
DataType dt = DataType.valueOf(subparts[1]);
return (dt == DataType.SCALAR);
}
}