org.apache.sysml.lops.RangeBasedReIndex Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of systemml Show documentation
Show all versions of systemml Show documentation
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 RangeBasedReIndex extends Lop
{
private boolean forLeftIndexing = false;
//optional attribute for spark exec type
private SparkAggType _aggtype = SparkAggType.MULTI_BLOCK;
public RangeBasedReIndex(Lop input, Lop rowL, Lop rowU, Lop colL, Lop colU, Lop rowDim, Lop colDim,
DataType dt, ValueType vt, ExecType et, boolean forleft)
throws LopsException
{
super(Lop.Type.RangeReIndex, dt, vt);
init(input, rowL, rowU, colL, colU, rowDim, colDim, dt, vt, et, forleft);
}
public RangeBasedReIndex(Lop input, Lop rowL, Lop rowU, Lop colL, Lop colU, Lop rowDim, Lop colDim,
DataType dt, ValueType vt, ExecType et)
throws LopsException
{
super(Lop.Type.RangeReIndex, dt, vt);
init(input, rowL, rowU, colL, colU, rowDim, colDim, dt, vt, et, false);
}
public RangeBasedReIndex(Lop input, Lop rowL, Lop rowU, Lop colL, Lop colU, Lop rowDim, Lop colDim,
DataType dt, ValueType vt, SparkAggType aggtype, ExecType et)
throws LopsException
{
super(Lop.Type.RangeReIndex, dt, vt);
_aggtype = aggtype;
init(input, rowL, rowU, colL, colU, rowDim, colDim, dt, vt, et, false);
}
private void init(Lop inputMatrix, Lop rowL, Lop rowU, Lop colL, Lop colU, Lop leftMatrixRowDim,
Lop leftMatrixColDim, DataType dt, ValueType vt, ExecType et, boolean forleft)
{
addInput(inputMatrix);
addInput(rowL);
addInput(rowU);
addInput(colL);
addInput(colU);
addInput(leftMatrixRowDim);
addInput(leftMatrixColDim);
inputMatrix.addOutput(this);
rowL.addOutput(this);
rowU.addOutput(this);
colL.addOutput(this);
colU.addOutput(this);
leftMatrixRowDim.addOutput(this);
leftMatrixColDim.addOutput(this);
boolean breaksAlignment = true;
boolean aligner = false;
boolean definesMRJob = false;
if ( et == ExecType.MR ) {
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.addCompatibility(JobType.MMCJ);
lps.addCompatibility(JobType.MMRJ);
lps.setProperties(inputs, et, ExecLocation.Map, breaksAlignment, aligner, definesMRJob);
}
else {
lps.addCompatibility(JobType.INVALID);
lps.setProperties(inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob);
}
forLeftIndexing=forleft;
}
private String getOpcode() {
if(forLeftIndexing)
return "rangeReIndexForLeft";
else
return "rangeReIndex";
}
@Override
public String getInstructions(String input, String rowl, String rowu, String coll, String colu, String leftRowDim, String leftColDim, 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(input));
sb.append( OPERAND_DELIMITOR );
// rowl, rowu
sb.append( getInputs().get(1).prepScalarInputOperand(rowl));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(2).prepScalarInputOperand(rowu));
sb.append( OPERAND_DELIMITOR );
// coll, colu
sb.append( getInputs().get(3).prepScalarInputOperand(coll));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(4).prepScalarInputOperand(colu));
sb.append( OPERAND_DELIMITOR );
sb.append( output );
sb.append( DATATYPE_PREFIX );
sb.append( getDataType() );
sb.append( VALUETYPE_PREFIX );
sb.append( getValueType() );
if(getExecType() == ExecType.MR) {
// following fields are added only when this lop is executed in MR (both for left & right indexing)
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(5).prepScalarInputOperand(leftRowDim));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(6).prepScalarInputOperand(leftColDim));
}
//in case of spark, we also compile the optional aggregate flag into the instruction.
if( getExecType() == ExecType.SPARK ) {
sb.append( OPERAND_DELIMITOR );
sb.append( _aggtype );
}
return sb.toString();
}
@Override
public String getInstructions(int input_index1, int input_index2, int input_index3, int input_index4, int input_index5, int input_index6, int input_index7, int output_index)
throws LopsException {
/*
* Example: B = A[row_l:row_u, col_l:col_u]
* A - input matrix (input_index1)
* row_l - lower bound in row dimension
* row_u - upper bound in row dimension
* col_l - lower bound in column dimension
* col_u - upper bound in column dimension
*
* Since row_l,row_u,col_l,col_u are scalars, values for input_index(2,3,4,5,6,7)
* will be equal to -1. They should be ignored and the scalar value labels must
* be derived from input lops.
*/
String rowl = getInputs().get(1).prepScalarLabel();
String rowu = getInputs().get(2).prepScalarLabel();
String coll = getInputs().get(3).prepScalarLabel();
String colu = getInputs().get(4).prepScalarLabel();
String left_nrow = getInputs().get(5).prepScalarLabel();
String left_ncol = getInputs().get(6).prepScalarLabel();
return getInstructions(Integer.toString(input_index1), rowl, rowu, coll, colu, left_nrow, left_ncol, Integer.toString(output_index));
}
@Override
public String toString() {
if(forLeftIndexing)
return "rangeReIndexForLeft";
else
return "rangeReIndex";
}
}