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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.lops;
import org.apache.sysml.lops.LopProperties.ExecLocation;
import org.apache.sysml.lops.LopProperties.ExecType;
import org.apache.sysml.lops.compile.JobType;
import org.apache.sysml.parser.Expression.*;
/*
* Lop to perform transpose/vector to diag operations
* This lop can change the keys and hence break alignment.
*/
public class Transform extends Lop
{
public enum OperationTypes {
Transpose,
Diag,
Reshape,
Sort,
Rev
};
private boolean _bSortIndInMem = false;
private OperationTypes operation = null;
/**
* Constructor when we have one input.
* @param input
* @param op
*/
public Transform(Lop input, Transform.OperationTypes op, DataType dt, ValueType vt, ExecType et)
{
super(Lop.Type.Transform, dt, vt);
init(input, op, dt, vt, et);
}
public Transform(Lop input, Transform.OperationTypes op, DataType dt, ValueType vt)
{
super(Lop.Type.Transform, dt, vt);
init(input, op, dt, vt, ExecType.MR);
}
public Transform(Lop input, Transform.OperationTypes op, DataType dt, ValueType vt, ExecType et, boolean bSortIndInMem)
{
super(Lop.Type.Transform, dt, vt);
_bSortIndInMem = bSortIndInMem;
init(input, op, dt, vt, et);
}
private void init (Lop input, Transform.OperationTypes op, DataType dt, ValueType vt, ExecType et)
{
operation = op;
this.addInput(input);
input.addOutput(this);
boolean breaksAlignment = true;
boolean aligner = false;
boolean definesMRJob = false;
if ( et == ExecType.MR ) {
/*
* This lop CAN NOT be executed in PARTITION, SORT, STANDALONE
* MMCJ: only in mapper.
*/
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.addCompatibility(JobType.REBLOCK);
lps.addCompatibility(JobType.CSV_REBLOCK);
lps.addCompatibility(JobType.MMCJ);
lps.addCompatibility(JobType.MMRJ);
if( op == OperationTypes.Reshape )
//reshape should be executed in map because we have potentially large intermediate data and want to exploit the combiner.
this.lps.setProperties( inputs, et, ExecLocation.Map, breaksAlignment, aligner, definesMRJob );
else
this.lps.setProperties( inputs, et, ExecLocation.MapOrReduce, breaksAlignment, aligner, definesMRJob );
}
else //CP/SPARK
{
// breaksAlignment is not meaningful when Transform executes in CP.
breaksAlignment = false;
lps.addCompatibility(JobType.INVALID);
lps.setProperties( inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob );
}
}
@Override
public String toString() {
return " Operation: " + operation;
}
/**
* method to get operation type
* @return
*/
public OperationTypes getOperationType()
{
return operation;
}
private String getOpcode() {
switch(operation) {
case Transpose:
// Transpose a matrix
return "r'";
case Rev:
// Transpose a matrix
return "rev";
case Diag:
// Transform a vector into a diagonal matrix
return "rdiag";
case Reshape:
// Transform a vector into a diagonal matrix
return "rshape";
case Sort:
// Transform a matrix into a sorted matrix
return "rsort";
default:
throw new UnsupportedOperationException(this.printErrorLocation() + "Instruction is not defined for Transform operation " + operation);
}
}
//CP instructions
@Override
public String getInstructions(String input1, String output)
throws LopsException
{
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input1));
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output));
return sb.toString();
}
@Override
public String getInstructions(String input1, String input2, String input3, String input4, String output)
throws LopsException
{
//only used for reshape
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input1));
//rows, cols, byrow
String[] inputX = new String[]{input2,input3,input4};
for( int i=1; i<=(inputX.length); i++ ) {
Lop ltmp = getInputs().get(i);
sb.append( OPERAND_DELIMITOR );
sb.append( ltmp.prepScalarInputOperand(getExecType()));
}
//output
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output));
if( getExecType()==ExecType.SPARK && operation == OperationTypes.Sort ){
sb.append( OPERAND_DELIMITOR );
sb.append( _bSortIndInMem);
}
return sb.toString();
}
//MR instructions
@Override
public String getInstructions(int input_index, int output_index)
throws LopsException
{
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input_index));
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output_index));
return sb.toString();
}
@Override
public String getInstructions(int input_index1, int input_index2, int input_index3, int input_index4, int output_index)
throws LopsException
{
//only used for reshape
StringBuilder sb = new StringBuilder();
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(0).prepInputOperand(input_index1));
//rows
Lop input2 = getInputs().get(1);
String rowsString = input2.prepScalarLabel();
sb.append( OPERAND_DELIMITOR );
sb.append( rowsString );
//cols
Lop input3 = getInputs().get(2);
String colsString = input3.prepScalarLabel();
sb.append( OPERAND_DELIMITOR );
sb.append( colsString );
//byrow
Lop input4 = getInputs().get(3);
String byrowString = input4.prepScalarLabel();
if ( input4.getExecLocation() == ExecLocation.Data
&& !((Data)input4).isLiteral() || !(input4.getExecLocation() == ExecLocation.Data ) ){
throw new LopsException(this.printErrorLocation() + "Parameter 'byRow' must be a literal for a matrix operation.");
}
sb.append( OPERAND_DELIMITOR );
sb.append( byrowString );
//output
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output_index));
return sb.toString();
}
public static Transform constructTransformLop(Lop input1, OperationTypes op, DataType dt, ValueType vt) {
for (Lop lop : input1.getOutputs()) {
if ( lop.type == Lop.Type.Transform ) {
return (Transform)lop;
}
}
Transform retVal = new Transform(input1, op, dt, vt);
retVal.setAllPositions(input1.getBeginLine(), input1.getBeginColumn(), input1.getEndLine(), input1.getEndColumn());
return retVal;
}
public static Transform constructTransformLop(Lop input1, OperationTypes op, DataType dt, ValueType vt, ExecType et) {
for (Lop lop : input1.getOutputs()) {
if ( lop.type == Lop.Type.Transform ) {
return (Transform)lop;
}
}
Transform retVal = new Transform(input1, op, dt, vt, et);
retVal.setAllPositions(input1.getBeginLine(), input1.getBeginColumn(), input1.getEndLine(), input1.getEndColumn());
return retVal;
}
}