| /* |
| * 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.sysds.runtime.instructions.cp; |
| |
| import org.apache.commons.lang3.tuple.Pair; |
| import org.apache.sysds.common.Types; |
| import org.apache.sysds.common.Types.ValueType; |
| import org.apache.sysds.runtime.DMLRuntimeException; |
| import org.apache.sysds.runtime.controlprogram.context.ExecutionContext; |
| import org.apache.sysds.runtime.data.LibTensorReorg; |
| import org.apache.sysds.runtime.data.TensorBlock; |
| import org.apache.sysds.runtime.instructions.InstructionUtils; |
| import org.apache.sysds.runtime.lineage.LineageItem; |
| import org.apache.sysds.runtime.lineage.LineageItemUtils; |
| import org.apache.sysds.runtime.matrix.data.LibMatrixReorg; |
| import org.apache.sysds.runtime.matrix.data.MatrixBlock; |
| import org.apache.sysds.runtime.matrix.operators.Operator; |
| import org.apache.sysds.runtime.util.DataConverter; |
| |
| public class ReshapeCPInstruction extends UnaryCPInstruction { |
| private final CPOperand _opRows; |
| private final CPOperand _opCols; |
| private final CPOperand _opDims; |
| private final CPOperand _opByRow; |
| |
| private ReshapeCPInstruction(Operator op, CPOperand in1, CPOperand in2, CPOperand in3, |
| CPOperand in4, CPOperand in5, CPOperand out, String opcode, String istr) { |
| super(CPType.Reshape, op, in1, out, opcode, istr); |
| _opRows = in2; |
| _opCols = in3; |
| _opDims = in4; |
| _opByRow = in5; |
| } |
| |
| public static ReshapeCPInstruction parseInstruction (String str ) { |
| String[] parts = InstructionUtils.getInstructionPartsWithValueType(str); |
| InstructionUtils.checkNumFields( parts, 6 ); |
| String opcode = parts[0]; |
| CPOperand in1 = new CPOperand(parts[1]); |
| CPOperand in2 = new CPOperand(parts[2]); |
| CPOperand in3 = new CPOperand(parts[3]); |
| CPOperand in4 = new CPOperand(parts[4]); |
| CPOperand in5 = new CPOperand(parts[5]); |
| CPOperand out = new CPOperand(parts[6]); |
| if(!opcode.equalsIgnoreCase("rshape")) |
| throw new DMLRuntimeException("Unknown opcode while parsing an ReshapeInstruction: " + str); |
| else |
| return new ReshapeCPInstruction(new Operator(true), in1, in2, in3, in4, in5, out, opcode, str); |
| } |
| |
| @Override |
| public void processInstruction(ExecutionContext ec) { |
| if (output.getDataType() == Types.DataType.TENSOR) { |
| int[] dims = DataConverter.getTensorDimensions(ec, _opDims); |
| TensorBlock out = new TensorBlock(output.getValueType(), dims); |
| if (input1.getDataType() == Types.DataType.TENSOR) { |
| //get Tensor-data from tensor (reshape) |
| // TODO support DataTensor |
| TensorBlock data = ec.getTensorInput(input1.getName()); |
| LibTensorReorg.reshape(data.getBasicTensor(), out.getBasicTensor(), dims); |
| ec.releaseTensorInput(input1.getName()); |
| } |
| else if (input1.getDataType() == Types.DataType.MATRIX) { |
| out.allocateBlock(); |
| //get Tensor-data from matrix |
| MatrixBlock data = ec.getMatrixInput(input1.getName()); |
| // TODO metadata operation |
| out.getBasicTensor().set(data); |
| ec.releaseMatrixInput(input1.getName()); |
| } |
| else { |
| // TODO support frame and list. Before we implement list it might be good to implement heterogeneous tensors |
| throw new DMLRuntimeException("ReshapeInstruction only supports tensor and matrix as data parameter."); |
| } |
| ec.setTensorOutput(output.getName(), out); |
| } |
| else { |
| //get inputs |
| MatrixBlock in = ec.getMatrixInput(input1.getName()); |
| int rows = (int) ec.getScalarInput(_opRows).getLongValue(); //save cast |
| int cols = (int) ec.getScalarInput(_opCols).getLongValue(); //save cast |
| BooleanObject byRow = (BooleanObject) ec.getScalarInput(_opByRow.getName(), ValueType.BOOLEAN, _opByRow.isLiteral()); |
| |
| //execute operations |
| MatrixBlock out = new MatrixBlock(); |
| LibMatrixReorg.reshape(in, out, rows, cols, byRow.getBooleanValue()); |
| |
| //set output and release inputs |
| ec.setMatrixOutput(output.getName(), out); |
| ec.releaseMatrixInput(input1.getName()); |
| } |
| } |
| |
| @Override |
| public Pair<String, LineageItem> getLineageItem(ExecutionContext ec) { |
| return Pair.of(output.getName(), new LineageItem(getOpcode(), |
| LineageItemUtils.getLineage(ec, input1, _opRows, _opCols, _opDims, _opByRow))); |
| } |
| } |