| /* |
| * 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.runtime.DMLRuntimeException; |
| import org.apache.sysds.runtime.controlprogram.caching.MatrixObject; |
| import org.apache.sysds.runtime.controlprogram.context.ExecutionContext; |
| import org.apache.sysds.runtime.functionobjects.Multiply; |
| import org.apache.sysds.runtime.functionobjects.Plus; |
| import org.apache.sysds.runtime.functionobjects.SwapIndex; |
| import org.apache.sysds.runtime.instructions.InstructionUtils; |
| import org.apache.sysds.runtime.instructions.cp.CPOperand; |
| import org.apache.sysds.runtime.matrix.data.LibMatrixCUDA; |
| import org.apache.sysds.runtime.matrix.data.LibMatrixCuMatMult; |
| import org.apache.sysds.runtime.matrix.data.MatrixBlock; |
| import org.apache.sysds.runtime.matrix.operators.AggregateBinaryOperator; |
| import org.apache.sysds.runtime.matrix.operators.Operator; |
| import org.apache.sysds.runtime.matrix.operators.ReorgOperator; |
| import org.apache.sysds.utils.GPUStatistics; |
| |
| public class AggregateBinaryGPUInstruction extends GPUInstruction { |
| private CPOperand _input1 = null; |
| private CPOperand _input2 = null; |
| private CPOperand _output = null; |
| private boolean _isLeftTransposed; |
| private boolean _isRightTransposed; |
| |
| private AggregateBinaryGPUInstruction(Operator op, CPOperand in1, CPOperand in2, CPOperand out, String opcode, |
| String istr, boolean leftTranspose, boolean rightTranspose) { |
| super(op, opcode, istr); |
| _gputype = GPUINSTRUCTION_TYPE.AggregateBinary; |
| _input1 = in1; |
| _input2 = in2; |
| _output = out; |
| _isLeftTransposed = leftTranspose; |
| _isRightTransposed = rightTranspose; |
| } |
| |
| public static AggregateBinaryGPUInstruction parseInstruction( String str ) { |
| String[] parts = InstructionUtils.getInstructionPartsWithValueType(str); |
| String opcode = parts[0]; |
| if ( !opcode.equalsIgnoreCase("ba+*")) |
| throw new DMLRuntimeException("AggregateBinaryInstruction.parseInstruction():: Unknown opcode " + opcode); |
| InstructionUtils.checkNumFields( parts, 5 ); |
| CPOperand in1 = new CPOperand(parts[1]); |
| CPOperand in2 = new CPOperand(parts[2]); |
| CPOperand out = new CPOperand(parts[3]); |
| boolean isLeftTransposed = Boolean.parseBoolean(parts[4]); |
| boolean isRightTransposed = Boolean.parseBoolean(parts[5]); |
| AggregateBinaryOperator aggbin = InstructionUtils.getMatMultOperator(1); |
| return new AggregateBinaryGPUInstruction(aggbin, in1, in2, out, opcode, str, isLeftTransposed, isRightTransposed); |
| } |
| |
| @Override |
| public void processInstruction(ExecutionContext ec) { |
| GPUStatistics.incrementNoOfExecutedGPUInst(); |
| AggregateBinaryOperator op = (AggregateBinaryOperator) _optr; |
| if( !(op.binaryFn instanceof Multiply && op.aggOp.increOp.fn instanceof Plus) ) |
| throw new DMLRuntimeException("Unsupported binary aggregate operation: ("+op.binaryFn+", "+op.aggOp+")."); |
| MatrixObject m1 = getMatrixInputForGPUInstruction(ec, _input1.getName()); |
| MatrixObject m2 = getMatrixInputForGPUInstruction(ec, _input2.getName()); |
| //compute matrix multiplication |
| int rlen = (int) (_isLeftTransposed ? m1.getNumColumns() : m1.getNumRows()); |
| int clen = (int) (_isRightTransposed ? m2.getNumRows() : m2.getNumColumns()); |
| ec.setMetaData(_output.getName(), rlen, clen); |
| LibMatrixCuMatMult.matmult(ec, ec.getGPUContext(0), getExtendedOpcode(), m1, m2, _output.getName(), _isLeftTransposed, _isRightTransposed); |
| //release inputs/outputs |
| ec.releaseMatrixInputForGPUInstruction(_input1.getName()); |
| ec.releaseMatrixInputForGPUInstruction(_input2.getName()); |
| ec.releaseMatrixOutputForGPUInstruction(_output.getName()); |
| } |
| |
| @SuppressWarnings("unused") |
| private static MatrixBlock transpose(MatrixBlock m1) { |
| ReorgOperator r_op = new ReorgOperator(SwapIndex.getSwapIndexFnObject(), 1); |
| return m1.reorgOperations(r_op, new MatrixBlock(), 0, 0, 0); |
| } |
| |
| @SuppressWarnings("unused") |
| private static boolean isSparse(ExecutionContext ec, String var) { |
| MatrixObject mo = ec.getMatrixObject(var); |
| return LibMatrixCUDA.isInSparseFormat(ec.getGPUContext(0), mo); |
| } |
| } |