| /* |
| * 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.gpu; |
| |
| import org.apache.sysds.common.Types.DataType; |
| import org.apache.sysds.runtime.DMLRuntimeException; |
| import org.apache.sysds.runtime.controlprogram.caching.MatrixObject; |
| import org.apache.sysds.runtime.controlprogram.context.ExecutionContext; |
| import org.apache.sysds.runtime.instructions.InstructionUtils; |
| import org.apache.sysds.runtime.instructions.cp.CPOperand; |
| import org.apache.sysds.runtime.instructions.cp.ScalarObject; |
| import org.apache.sysds.runtime.matrix.data.LibMatrixCUDA; |
| import org.apache.sysds.runtime.matrix.operators.Operator; |
| import org.apache.sysds.utils.GPUStatistics; |
| |
| public class MatrixMatrixAxpyGPUInstruction extends ArithmeticBinaryGPUInstruction { |
| CPOperand constant = null; |
| int multiplier = 1; |
| |
| private MatrixMatrixAxpyGPUInstruction(Operator op, CPOperand in1, CPOperand constant, int multiplier, |
| CPOperand in2, CPOperand out, String opcode, String istr) { |
| super(op, in1, in2, out, opcode, istr); |
| this.constant = constant; |
| this.multiplier = multiplier; |
| } |
| |
| public static MatrixMatrixAxpyGPUInstruction parseInstruction ( String str ) { |
| String[] parts = InstructionUtils.getInstructionPartsWithValueType(str); |
| InstructionUtils.checkNumFields ( parts, 4 ); |
| |
| String opcode = parts[0]; |
| int multiplier = 1; |
| if(opcode.equals("-*")) |
| multiplier = -1; |
| CPOperand in1 = new CPOperand(parts[1]); |
| CPOperand constant = new CPOperand(parts[2]); |
| if(constant.getDataType() != DataType.SCALAR) |
| throw new DMLRuntimeException("Expected second operand to be a scalar"); |
| CPOperand in2 = new CPOperand(parts[3]); |
| CPOperand out = new CPOperand(parts[4]); |
| |
| DataType dt1 = in1.getDataType(); |
| DataType dt2 = in2.getDataType(); |
| DataType dt3 = out.getDataType(); |
| |
| Operator operator = (dt1 != dt2) ? |
| InstructionUtils.parseScalarBinaryOperator(opcode, (dt1 == DataType.SCALAR)) : |
| InstructionUtils.parseTernaryOperator(opcode); |
| |
| if(dt1 == DataType.MATRIX && dt2 == DataType.MATRIX && dt3 == DataType.MATRIX) { |
| return new MatrixMatrixAxpyGPUInstruction(operator, in1, constant, multiplier, in2, out, opcode, str); |
| } |
| else if( dt3 == DataType.MATRIX && ((dt1 == DataType.SCALAR && dt2 == DataType.MATRIX) || (dt1 == DataType.MATRIX && dt2 == DataType.SCALAR)) ) { |
| throw new DMLRuntimeException("Unsupported GPU PlusMult/MinusMult ArithmeticInstruction."); |
| // return new ScalarMatrixArithmeticGPUInstruction(operator, in1, in2, out, opcode, str); |
| } |
| else |
| throw new DMLRuntimeException("Unsupported GPU ArithmeticInstruction."); |
| } |
| |
| |
| @Override |
| public void processInstruction(ExecutionContext ec) { |
| GPUStatistics.incrementNoOfExecutedGPUInst(); |
| |
| MatrixObject in1 = getMatrixInputForGPUInstruction(ec, _input1.getName()); |
| MatrixObject in2 = getMatrixInputForGPUInstruction(ec, _input2.getName()); |
| ScalarObject scalar = ec.getScalarInput(constant); |
| |
| long rlen1 = in1.getNumRows(); |
| long clen1 = in1.getNumColumns(); |
| long rlen2 = in2.getNumRows(); |
| long clen2 = in2.getNumColumns(); |
| if(isValidMMOperation(rlen1, rlen2, clen1, clen2) || isValidMVOperation(rlen1, rlen2, clen1, clen2)) { |
| ec.setMetaData(_output.getName(), (int)rlen1, (int)clen1); |
| } |
| else { |
| throw new DMLRuntimeException("Incorrect dimensions of inputs in GPU axpy operation. input1:" + rlen1 + " X " + clen1 + |
| " and input2:" + rlen2 + " X " + clen2); |
| } |
| |
| LibMatrixCUDA.axpy(ec, ec.getGPUContext(0), getExtendedOpcode(), in1, in2, _output.getName(), multiplier*scalar.getDoubleValue()); |
| |
| ec.releaseMatrixInputForGPUInstruction(_input1.getName()); |
| ec.releaseMatrixInputForGPUInstruction(_input2.getName()); |
| ec.releaseMatrixOutputForGPUInstruction(_output.getName()); |
| } |
| |
| private static boolean isValidMMOperation(long rlen1, long rlen2, long clen1, long clen2) { |
| return rlen1 == rlen2 && clen1 == clen2; |
| } |
| |
| private static boolean isValidMVOperation(long rlen1, long rlen2, long clen1, long clen2) { |
| return (rlen1 == rlen2 && clen2 == 1) || (rlen2 == 1 && clen1 == clen2); |
| } |
| |
| } |