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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.DataType;
import org.apache.sysml.parser.Expression.ValueType;
public class ConvolutionTransform extends Lop
{
public enum OperationTypes {
MAX_POOLING, MAX_POOLING_BACKWARD, RELU_MAX_POOLING, RELU_BACKWARD, RELU_MAX_POOLING_BACKWARD,
DIRECT_CONV2D, DIRECT_CONV2D_BACKWARD_FILTER, DIRECT_CONV2D_BACKWARD_DATA,
BIAS_ADD, DIRECT_CONV2D_BIAS_ADD, BIAS_MULTIPLY, CHANNEL_SUMS
}
private OperationTypes operation = null;
private int numThreads = -1;
private double intermediateMemBudget = 0;
/**
* Constructor when we have one input.
*
* @param input low-level operator
* @param op convolution transform operation type
* @param dt data type
* @param vt value type
* @param et execution type
* @param k number of threads
* @param intermediateMemBudget intermediate memory budget
*/
public ConvolutionTransform(Lop input, ConvolutionTransform.OperationTypes op, DataType dt, ValueType vt, ExecType et, int k, double intermediateMemBudget)
{
super(Lop.Type.Transform, dt, vt);
init(input, op, dt, vt, et);
numThreads = k;
this.intermediateMemBudget = intermediateMemBudget;
}
public ConvolutionTransform(Lop input1, Lop input2, ConvolutionTransform.OperationTypes op, DataType dt, ValueType vt, ExecType et, int k)
{
super(Lop.Type.Transform, dt, vt);
init(input1, op, dt, vt, et);
numThreads = k;
this.addInput(input2);
input2.addOutput(this);
setLevel();
}
public ConvolutionTransform(Lop input1, Lop input2, Lop input3, ConvolutionTransform.OperationTypes op, DataType dt, ValueType vt, ExecType et, int k)
{
super(Lop.Type.Transform, dt, vt);
init(input1, op, dt, vt, et);
numThreads = k;
this.addInput(input2);
input2.addOutput(this);
this.addInput(input3);
input3.addOutput(this);
setLevel();
}
private void init (Lop input, ConvolutionTransform.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 ) {
throw new RuntimeException("The execution type is not supported: " + et.name());
}
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 );
}
}
public void updateLopProperties() {
lps.setLevel(inputs);
}
@Override
public String toString() {
return " Operation: " + operation;
}
/**
* method to get operation type
* @return operation type
*/
public OperationTypes getOperationType()
{
return operation;
}
private String getOpcode() {
switch(operation) {
case MAX_POOLING:
return "maxpooling";
case RELU_MAX_POOLING:
return "relu_maxpooling";
case RELU_MAX_POOLING_BACKWARD:
return "relu_maxpooling_backward";
case RELU_BACKWARD:
return "relu_backward";
case MAX_POOLING_BACKWARD:
return "maxpooling_backward";
case DIRECT_CONV2D:
return "conv2d";
case DIRECT_CONV2D_BIAS_ADD:
return "conv2d_bias_add";
case BIAS_ADD:
return "bias_add";
case BIAS_MULTIPLY:
return "bias_multiply";
case DIRECT_CONV2D_BACKWARD_FILTER:
return "conv2d_backward_filter";
case DIRECT_CONV2D_BACKWARD_DATA:
return "conv2d_backward_data";
case CHANNEL_SUMS:
return "channel_sums";
default:
throw new UnsupportedOperationException(this.printErrorLocation() + "Instruction is not defined for Transform operation " + operation);
}
}
@Override
public String getInstructions(String input, String bias, String output) throws LopsException {
if(operation == OperationTypes.BIAS_ADD || operation == OperationTypes.BIAS_MULTIPLY || operation == OperationTypes.RELU_BACKWARD) {
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 );
sb.append( getInputs().get(0).prepInputOperand(bias));
//output
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output));
//append degree of parallelism
if( getExecType()==ExecType.CP ) {
sb.append( OPERAND_DELIMITOR );
sb.append( numThreads );
}
sb.append( OPERAND_DELIMITOR );
sb.append( intermediateMemBudget );
return sb.toString();
}
else {
throw new LopsException("The operation is not supported with two operands:" + operation.name());
}
}
@Override
public String getInstructions(String input, String C, String HW, String output) throws LopsException {
if(operation == OperationTypes.CHANNEL_SUMS) {
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 );
sb.append( getInputs().get(1).prepInputOperand(C));
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(2).prepInputOperand(HW));
//output
sb.append( OPERAND_DELIMITOR );
sb.append( this.prepOutputOperand(output));
return sb.toString();
}
else {
throw new LopsException("The operation is not supported with three operands:" + operation.name());
}
}
@Override
public String getInstructions(String[] inputs, String output) throws LopsException {
StringBuilder sb = new StringBuilder();
appendOpcode(sb);
for( int i=0; i 0 )
sb.append( OPERAND_DELIMITOR );
sb.append( getInputs().get(i).prepInputOperand(inputs[i]));
}
appendOperands(inputs.length-12, inputs.length, output, sb);
return sb.toString();
}
public void appendOpcode(StringBuilder sb) {
sb.append( getExecType() );
sb.append( OPERAND_DELIMITOR );
sb.append( getOpcode() );
sb.append( OPERAND_DELIMITOR );
}
public void appendOperands(int startInputIndex, int endInputIndex, String output, StringBuilder sb) {
for( int i=startInputIndex; i < endInputIndex; 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));
//append degree of parallelism
if( getExecType()==ExecType.CP ) {
sb.append( OPERAND_DELIMITOR );
sb.append( numThreads );
}
sb.append( OPERAND_DELIMITOR );
sb.append( intermediateMemBudget );
}
}