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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.MMCJ.MMCJType;
import org.apache.sysml.lops.MapMult;
import org.apache.sysml.lops.MapMult.CacheType;
import org.apache.sysml.runtime.DMLRuntimeException;
import org.apache.sysml.runtime.DMLUnsupportedOperationException;
import org.apache.sysml.runtime.functionobjects.Multiply;
import org.apache.sysml.runtime.functionobjects.Plus;
import org.apache.sysml.runtime.instructions.InstructionUtils;
import org.apache.sysml.runtime.matrix.data.MatrixIndexes;
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.DistributedCacheInput;
import org.apache.sysml.runtime.matrix.mapred.IndexedMatrixValue;
import org.apache.sysml.runtime.matrix.mapred.MRBaseForCommonInstructions;
import org.apache.sysml.runtime.matrix.operators.AggregateBinaryOperator;
import org.apache.sysml.runtime.matrix.operators.AggregateOperator;
import org.apache.sysml.runtime.matrix.operators.Operator;
public class AggregateBinaryInstruction extends BinaryMRInstructionBase implements IDistributedCacheConsumer
{
private String _opcode = null;
//optional argument for cpmm
private MMCJType _aggType = MMCJType.AGG;
//optional argument for mapmm
private CacheType _cacheType = null;
private boolean _outputEmptyBlocks = true;
public AggregateBinaryInstruction(Operator op, String opcode, byte in1, byte in2, byte out, String istr)
{
super(op, in1, in2, out);
mrtype = MRINSTRUCTION_TYPE.AggregateBinary;
instString = istr;
_opcode = opcode;
}
/**
*
* @param flag
*/
public void setCacheTypeMapMult( CacheType type )
{
_cacheType = type;
}
/**
*
* @param flag
*/
public void setOutputEmptyBlocksMapMult( boolean flag )
{
_outputEmptyBlocks = flag;
}
public boolean getOutputEmptyBlocks()
{
return _outputEmptyBlocks;
}
public void setMMCJType( MMCJType type )
{
_aggType = type;
}
public MMCJType getMMCJType()
{
return _aggType;
}
/**
*
* @param str
* @return
* @throws DMLRuntimeException
*/
public static AggregateBinaryInstruction parseInstruction ( String str )
throws DMLRuntimeException
{
String[] parts = InstructionUtils.getInstructionParts ( str );
byte in1, in2, out;
String opcode = parts[0];
in1 = Byte.parseByte(parts[1]);
in2 = Byte.parseByte(parts[2]);
out = Byte.parseByte(parts[3]);
if ( opcode.equalsIgnoreCase("cpmm")
|| opcode.equalsIgnoreCase("rmm")
|| opcode.equalsIgnoreCase(MapMult.OPCODE) )
{
AggregateOperator agg = new AggregateOperator(0, Plus.getPlusFnObject());
AggregateBinaryOperator aggbin = new AggregateBinaryOperator(Multiply.getMultiplyFnObject(), agg);
AggregateBinaryInstruction inst = new AggregateBinaryInstruction(aggbin, opcode, in1, in2, out, str);
if( parts.length==5 ){
inst.setMMCJType(MMCJType.valueOf(parts[4]));
}
else if( parts.length==6 ) { //mapmm
inst.setCacheTypeMapMult( CacheType.valueOf(parts[4]) );
inst.setOutputEmptyBlocksMapMult( Boolean.parseBoolean(parts[5]) );
}
return inst;
}
throw new DMLRuntimeException("AggregateBinaryInstruction.parseInstruction():: Unknown opcode " + opcode);
}
@Override //IDistributedCacheConsumer
public boolean isDistCacheOnlyIndex( String inst, byte index )
{
return _cacheType.isRightCache() ?
(index==input2 && index!=input1) :
(index==input1 && index!=input2);
}
@Override //IDistributedCacheConsumer
public void addDistCacheIndex( String inst, ArrayList indexes )
{
indexes.add( _cacheType.isRightCache() ? input2 : input1 );
}
@Override
public void processInstruction(Class valueClass,
CachedValueMap cachedValues, IndexedMatrixValue tempValue,
IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor)
throws DMLUnsupportedOperationException, DMLRuntimeException
{
IndexedMatrixValue in1=cachedValues.getFirst(input1);
IndexedMatrixValue in2=cachedValues.getFirst(input2);
if ( _opcode.equals(MapMult.OPCODE) )
{
//check empty inputs (data for different instructions)
if( _cacheType.isRightCache() ? in1==null : in2==null )
return;
// one of the input is from distributed cache.
processMapMultInstruction(valueClass, cachedValues, in1, in2, blockRowFactor, blockColFactor);
}
else //generic matrix mult
{
//check empty inputs (data for different instructions)
if(in1==null || in2==null)
return;
//allocate space for the output value
IndexedMatrixValue out;
if(output==input1 || output==input2)
out=tempValue;
else
out=cachedValues.holdPlace(output, valueClass);
//process instruction
OperationsOnMatrixValues.performAggregateBinary(
in1.getIndexes(), in1.getValue(),
in2.getIndexes(), in2.getValue(),
out.getIndexes(), out.getValue(),
((AggregateBinaryOperator)optr));
//put the output value in the cache
if(out==tempValue)
cachedValues.add(output, out);
}
}
/**
* Helper function to perform map-side matrix-matrix multiplication.
*
* @param valueClass
* @param cachedValues
* @param in1
* @param in2
* @param blockRowFactor
* @param blockColFactor
* @throws DMLRuntimeException
* @throws DMLUnsupportedOperationException
*/
private void processMapMultInstruction(Class valueClass, CachedValueMap cachedValues, IndexedMatrixValue in1, IndexedMatrixValue in2, int blockRowFactor, int blockColFactor)
throws DMLRuntimeException, DMLUnsupportedOperationException
{
boolean removeOutput = true;
if( _cacheType.isRightCache() )
{
DistributedCacheInput dcInput = MRBaseForCommonInstructions.dcValues.get(input2);
long in2_cols = dcInput.getNumCols();
long in2_colBlocks = (long)Math.ceil(((double)in2_cols)/dcInput.getNumColsPerBlock());
for(int bidx=1; bidx <= in2_colBlocks; bidx++)
{
// Matrix multiply A[i,k] %*% B[k,bid]
// Setup input2 block
IndexedMatrixValue in2Block = dcInput.getDataBlock((int)in1.getIndexes().getColumnIndex(), bidx);
MatrixValue in2BlockValue = in2Block.getValue();
MatrixIndexes in2BlockIndex = in2Block.getIndexes();
//allocate space for the output value
IndexedMatrixValue out = cachedValues.holdPlace(output, valueClass);
//process instruction
OperationsOnMatrixValues.performAggregateBinary(in1.getIndexes(), in1.getValue(),
in2BlockIndex, in2BlockValue, out.getIndexes(), out.getValue(),
((AggregateBinaryOperator)optr));
removeOutput &= ( !_outputEmptyBlocks && out.getValue().isEmpty() );
}
}
else
{
DistributedCacheInput dcInput = MRBaseForCommonInstructions.dcValues.get(input1);
long in1_rows = dcInput.getNumRows();
long in1_rowsBlocks = (long) Math.ceil(((double)in1_rows)/dcInput.getNumRowsPerBlock());
for(int bidx=1; bidx <= in1_rowsBlocks; bidx++) {
// Matrix multiply A[i,k] %*% B[k,bid]
// Setup input2 block
IndexedMatrixValue in1Block = dcInput.getDataBlock(bidx, (int)in2.getIndexes().getRowIndex());
MatrixValue in1BlockValue = in1Block.getValue();
MatrixIndexes in1BlockIndex = in1Block.getIndexes();
//allocate space for the output value
IndexedMatrixValue out = cachedValues.holdPlace(output, valueClass);
//process instruction
OperationsOnMatrixValues.performAggregateBinary(in1BlockIndex, in1BlockValue,
in2.getIndexes(), in2.getValue(),
out.getIndexes(), out.getValue(),
((AggregateBinaryOperator)optr));
removeOutput &= ( !_outputEmptyBlocks && out.getValue().isEmpty() );
}
}
//empty block output filter (enabled by compiler consumer operation is in CP)
if( removeOutput )
cachedValues.remove(output);
}
}