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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.hops.AggBinaryOp.SparkAggType;
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.DataType;
import org.apache.sysml.parser.Expression.ValueType;
public class MapMult extends Lop
{
public static final String OPCODE = "mapmm";
public enum CacheType {
RIGHT,
RIGHT_PART,
LEFT,
LEFT_PART;
public boolean isRight() {
return (this == RIGHT || this == RIGHT_PART);
}
public CacheType getFlipped() {
switch( this ) {
case RIGHT: return LEFT;
case RIGHT_PART: return LEFT_PART;
case LEFT: return RIGHT;
case LEFT_PART: return RIGHT_PART;
default: return null;
}
}
}
private CacheType _cacheType = null;
private boolean _outputEmptyBlocks = true;
//optional attribute for spark exec type
private SparkAggType _aggtype = SparkAggType.MULTI_BLOCK;
/**
* Constructor to setup a partial Matrix-Vector Multiplication for MR
*
* @param input1 low-level operator 1
* @param input2 low-level operator 2
* @param dt data type
* @param vt value type
* @param rightCache true if right cache, false if left cache
* @param partitioned true if partitioned, false if not partitioned
* @param emptyBlocks true if output empty blocks
* @throws LopsException if LopsException occurs
*/
public MapMult(Lop input1, Lop input2, DataType dt, ValueType vt, boolean rightCache, boolean partitioned, boolean emptyBlocks )
throws LopsException
{
super(Lop.Type.MapMult, dt, vt);
this.addInput(input1);
this.addInput(input2);
input1.addOutput(this);
input2.addOutput(this);
//setup mapmult parameters
if( rightCache )
_cacheType = partitioned ? CacheType.RIGHT_PART : CacheType.RIGHT;
else
_cacheType = partitioned ? CacheType.LEFT_PART : CacheType.LEFT;
_outputEmptyBlocks = emptyBlocks;
//setup MR parameters
boolean breaksAlignment = true;
boolean aligner = false;
boolean definesMRJob = false;
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.setProperties( inputs, ExecType.MR, ExecLocation.Map, breaksAlignment, aligner, definesMRJob );
}
/**
* Constructor to setup a partial Matrix-Vector Multiplication for Spark
*
* @param input1 low-level operator 1
* @param input2 low-level operator 2
* @param dt data type
* @param vt value type
* @param rightCache true if right cache, false if left cache
* @param partitioned true if partitioned, false if not partitioned
* @param emptyBlocks true if output empty blocks
* @param aggtype spark aggregation type
* @throws LopsException if LopsException occurs
*/
public MapMult(Lop input1, Lop input2, DataType dt, ValueType vt, boolean rightCache, boolean partitioned, boolean emptyBlocks, SparkAggType aggtype)
throws LopsException
{
super(Lop.Type.MapMult, dt, vt);
this.addInput(input1);
this.addInput(input2);
input1.addOutput(this);
input2.addOutput(this);
//setup mapmult parameters
if( rightCache )
_cacheType = partitioned ? CacheType.RIGHT_PART : CacheType.RIGHT;
else
_cacheType = partitioned ? CacheType.LEFT_PART : CacheType.LEFT;
_outputEmptyBlocks = emptyBlocks;
_aggtype = aggtype;
//setup MR parameters
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = false;
lps.addCompatibility(JobType.INVALID);
lps.setProperties( inputs, ExecType.SPARK, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob );
}
@Override
public String toString() {
return "Operation = MapMM";
}
@Override
public String getInstructions(int input_index1, int input_index2, int output_index) {
return getInstructions(String.valueOf(input_index1),
String.valueOf(input_index2), String.valueOf(output_index));
}
@Override
public String getInstructions(String input1, String input2, String output)
{
StringBuilder sb = new StringBuilder();
sb.append(getExecType());
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(OPCODE);
sb.append(Lop.OPERAND_DELIMITOR);
sb.append( getInputs().get(0).prepInputOperand(input1));
sb.append(Lop.OPERAND_DELIMITOR);
sb.append( getInputs().get(1).prepInputOperand(input2));
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(prepOutputOperand(output));
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(_cacheType);
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(_outputEmptyBlocks);
if( getExecType() == ExecType.SPARK ) {
sb.append(Lop.OPERAND_DELIMITOR);
sb.append(_aggtype.toString());
}
return sb.toString();
}
@Override
public boolean usesDistributedCache() {
return true;
}
@Override
public int[] distributedCacheInputIndex() {
return _cacheType.isRight() ?
new int[]{2} : new int[]{1};
}
}