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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 binary operation. Both inputs must be matrices or vectors.
* Example - A = B + C, where B and C are matrices or vectors.
*/
public class Binary extends Lop
{
public enum OperationTypes {
ADD, SUBTRACT, MULTIPLY, DIVIDE, MINUS1_MULTIPLY, MODULUS, INTDIV, MATMULT,
LESS_THAN, LESS_THAN_OR_EQUALS, GREATER_THAN, GREATER_THAN_OR_EQUALS, EQUALS, NOT_EQUALS,
AND, OR, XOR,
MAX, MIN, POW, SOLVE, NOTSUPPORTED,
BW_AND, BW_OR, BW_XOR, BW_SHIFTL, BW_SHIFTR, //Bitwise operations
}
private OperationTypes operation;
private int numThreads = -1;
boolean isLeftTransposed; boolean isRightTransposed; // Used for GPU matmult operation
/**
* Constructor to perform a binary operation.
*
* @param input1 low-level operator 1
* @param input2 low-level operator 2
* @param op operation type
* @param dt data type
* @param vt value type
* @param et exec type
*/
public Binary(Lop input1, Lop input2, OperationTypes op, DataType dt, ValueType vt, ExecType et) {
this(input1, input2, op, dt, vt, et, 1);
}
public Binary(Lop input1, Lop input2, OperationTypes op, DataType dt, ValueType vt, ExecType et, int k) {
super(Lop.Type.Binary, dt, vt);
init(input1, input2, op, dt, vt, et);
numThreads = k;
}
public Binary(Lop input1, Lop input2, OperationTypes op, DataType dt, ValueType vt, ExecType et,
boolean isLeftTransposed, boolean isRightTransposed) {
super(Lop.Type.Binary, dt, vt);
init(input1, input2, op, dt, vt, et);
this.isLeftTransposed = isLeftTransposed;
this.isRightTransposed = isRightTransposed;
}
private void init(Lop input1, Lop input2, OperationTypes op, DataType dt, ValueType vt, ExecType et)
{
operation = op;
this.addInput(input1);
this.addInput(input2);
input1.addOutput(this);
input2.addOutput(this);
boolean breaksAlignment = false;
boolean aligner = false;
boolean definesMRJob = false;
if ( et == ExecType.MR ) {
lps.addCompatibility(JobType.GMR);
lps.addCompatibility(JobType.DATAGEN);
lps.addCompatibility(JobType.REBLOCK);
this.lps.setProperties( inputs, et, ExecLocation.Reduce, breaksAlignment, aligner, definesMRJob );
}
else if ( et == ExecType.CP || et == ExecType.SPARK || et == ExecType.GPU ){
lps.addCompatibility(JobType.INVALID);
this.lps.setProperties( inputs, et, ExecLocation.ControlProgram, breaksAlignment, aligner, definesMRJob );
}
}
@Override
public String toString() {
return " Operation: " + operation;
}
/**
* method to get operation type
* @return operation type
*/
public OperationTypes getOperationType()
{
return operation;
}
private String getOpcode()
{
return getOpcode( operation );
}
public static String getOpcode( OperationTypes op ) {
switch(op) {
/* Arithmetic */
case ADD:
return "+";
case SUBTRACT:
return "-";
case MULTIPLY:
return "*";
case DIVIDE:
return "/";
case MODULUS:
return "%%";
case INTDIV:
return "%/%";
case MATMULT:
return "ba+*";
case MINUS1_MULTIPLY:
return "1-*";
/* Relational */
case LESS_THAN:
return "<";
case LESS_THAN_OR_EQUALS:
return "<=";
case GREATER_THAN:
return ">";
case GREATER_THAN_OR_EQUALS:
return ">=";
case EQUALS:
return "==";
case NOT_EQUALS:
return "!=";
/* Boolean */
case AND:
return "&&";
case OR:
return "||";
/* Binary Builtin Function */
case XOR:
return "xor";
case BW_AND:
return "bitwAnd";
case BW_OR:
return "bitwOr";
case BW_XOR:
return "bitwXor";
case BW_SHIFTL:
return "bitwShiftL";
case BW_SHIFTR:
return "bitwShiftR";
/* Builtin Functions */
case MIN:
return "min";
case MAX:
return "max";
case POW:
return "^";
case SOLVE:
return "solve";
default:
throw new UnsupportedOperationException("Instruction is not defined for Binary operation: " + op);
}
}
@Override
public String getInstructions(int input_index1, int input_index2, int output_index) throws LopsException {
return getInstructions(
String.valueOf(input_index1),
String.valueOf(input_index2),
String.valueOf(output_index));
}
@Override
public String getInstructions(String input1, String input2, 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 ( getInputs().get(1).prepInputOperand(input2));
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output));
//append degree of parallelism for matrix multiplications
if( operation == OperationTypes.MATMULT && getExecType()==ExecType.CP ) {
sb.append( OPERAND_DELIMITOR );
sb.append( numThreads );
}
else if( operation == OperationTypes.MATMULT && getExecType()==ExecType.GPU ) {
sb.append( OPERAND_DELIMITOR );
sb.append( isLeftTransposed );
sb.append( OPERAND_DELIMITOR );
sb.append( isRightTransposed );
}
return sb.toString();
}
}