Examples with PartialAggregate org.apache.sysml.lops.PartialAggregate used on opensource projects

Example 1 with PartialAggregate

use of org.apache.sysml.lops.PartialAggregate in project incubator-systemml by apache.

the class AggUnaryOp method constructLops.

@Override
public Lop constructLops() throws HopsException, LopsException {
    //return already created lops
    if (getLops() != null)
        return getLops();
    try {
        ExecType et = optFindExecType();
        Hop input = getInput().get(0);
        if (et == ExecType.CP) {
            Lop agg1 = null;
            if (isTernaryAggregateRewriteApplicable(et)) {
                agg1 = constructLopsTernaryAggregateRewrite(et);
            } else if (isUnaryAggregateOuterCPRewriteApplicable()) {
                OperationTypes op = HopsAgg2Lops.get(_op);
                DirectionTypes dir = HopsDirection2Lops.get(_direction);
                BinaryOp binput = (BinaryOp) getInput().get(0);
                agg1 = new UAggOuterChain(binput.getInput().get(0).constructLops(), binput.getInput().get(1).constructLops(), op, dir, HopsOpOp2LopsB.get(binput.getOp()), DataType.MATRIX, getValueType(), ExecType.CP);
                PartialAggregate.setDimensionsBasedOnDirection(agg1, getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock(), dir);
                if (getDataType() == DataType.SCALAR) {
                    UnaryCP unary1 = new UnaryCP(agg1, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
                    unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
                    setLineNumbers(unary1);
                    setLops(unary1);
                }
            } else {
                //general case		
                int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
                if (DMLScript.USE_ACCELERATOR && (DMLScript.FORCE_ACCELERATOR || getMemEstimate() < OptimizerUtils.GPU_MEMORY_BUDGET)) {
                    // Only implemented methods for GPU
                    if ((_op == AggOp.SUM && (_direction == Direction.RowCol || _direction == Direction.Row || _direction == Direction.Col)) || (_op == AggOp.SUM_SQ && (_direction == Direction.RowCol || _direction == Direction.Row || _direction == Direction.Col)) || (_op == AggOp.MAX && (_direction == Direction.RowCol || _direction == Direction.Row || _direction == Direction.Col)) || (_op == AggOp.MIN && (_direction == Direction.RowCol || _direction == Direction.Row || _direction == Direction.Col)) || (_op == AggOp.MEAN && (_direction == Direction.RowCol || _direction == Direction.Row || _direction == Direction.Col)) || (_op == AggOp.VAR && (_direction == Direction.RowCol || _direction == Direction.Row || _direction == Direction.Col)) || (_op == AggOp.PROD && (_direction == Direction.RowCol))) {
                        et = ExecType.GPU;
                        k = 1;
                    }
                }
                agg1 = new PartialAggregate(input.constructLops(), HopsAgg2Lops.get(_op), HopsDirection2Lops.get(_direction), getDataType(), getValueType(), et, k);
            }
            setOutputDimensions(agg1);
            setLineNumbers(agg1);
            setLops(agg1);
            if (getDataType() == DataType.SCALAR) {
                agg1.getOutputParameters().setDimensions(1, 1, getRowsInBlock(), getColsInBlock(), getNnz());
            }
        } else if (et == ExecType.MR) {
            OperationTypes op = HopsAgg2Lops.get(_op);
            DirectionTypes dir = HopsDirection2Lops.get(_direction);
            //unary aggregate operation
            Lop transform1 = null;
            if (isUnaryAggregateOuterRewriteApplicable()) {
                BinaryOp binput = (BinaryOp) getInput().get(0);
                transform1 = new UAggOuterChain(binput.getInput().get(0).constructLops(), binput.getInput().get(1).constructLops(), op, dir, HopsOpOp2LopsB.get(binput.getOp()), DataType.MATRIX, getValueType(), ExecType.MR);
                PartialAggregate.setDimensionsBasedOnDirection(transform1, getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock(), dir);
            } else //default
            {
                transform1 = new PartialAggregate(input.constructLops(), op, dir, DataType.MATRIX, getValueType());
                ((PartialAggregate) transform1).setDimensionsBasedOnDirection(getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock());
            }
            setLineNumbers(transform1);
            //aggregation if required
            Lop aggregate = null;
            Group group1 = null;
            Aggregate agg1 = null;
            if (requiresAggregation(input, _direction) || transform1 instanceof UAggOuterChain) {
                group1 = new Group(transform1, Group.OperationTypes.Sort, DataType.MATRIX, getValueType());
                group1.getOutputParameters().setDimensions(getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock(), getNnz());
                setLineNumbers(group1);
                agg1 = new Aggregate(group1, HopsAgg2Lops.get(_op), DataType.MATRIX, getValueType(), et);
                agg1.getOutputParameters().setDimensions(getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock(), getNnz());
                agg1.setupCorrectionLocation(PartialAggregate.getCorrectionLocation(op, dir));
                setLineNumbers(agg1);
                aggregate = agg1;
            } else {
                ((PartialAggregate) transform1).setDropCorrection();
                aggregate = transform1;
            }
            setLops(aggregate);
            //cast if required
            if (getDataType() == DataType.SCALAR) {
                // Set the dimensions of PartialAggregate LOP based on the
                // direction in which aggregation is performed
                PartialAggregate.setDimensionsBasedOnDirection(transform1, input.getDim1(), input.getDim2(), input.getRowsInBlock(), input.getColsInBlock(), dir);
                if (group1 != null && agg1 != null) {
                    //if aggregation required
                    group1.getOutputParameters().setDimensions(input.getDim1(), input.getDim2(), input.getRowsInBlock(), input.getColsInBlock(), getNnz());
                    agg1.getOutputParameters().setDimensions(1, 1, input.getRowsInBlock(), input.getColsInBlock(), getNnz());
                }
                UnaryCP unary1 = new UnaryCP(aggregate, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
                unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
                setLineNumbers(unary1);
                setLops(unary1);
            }
        } else if (et == ExecType.SPARK) {
            OperationTypes op = HopsAgg2Lops.get(_op);
            DirectionTypes dir = HopsDirection2Lops.get(_direction);
            //unary aggregate
            if (isTernaryAggregateRewriteApplicable(et)) {
                Lop aggregate = constructLopsTernaryAggregateRewrite(et);
                //0x0 (scalar)
                setOutputDimensions(aggregate);
                setLineNumbers(aggregate);
                setLops(aggregate);
            } else if (isUnaryAggregateOuterSPRewriteApplicable()) {
                BinaryOp binput = (BinaryOp) getInput().get(0);
                Lop transform1 = new UAggOuterChain(binput.getInput().get(0).constructLops(), binput.getInput().get(1).constructLops(), op, dir, HopsOpOp2LopsB.get(binput.getOp()), DataType.MATRIX, getValueType(), ExecType.SPARK);
                PartialAggregate.setDimensionsBasedOnDirection(transform1, getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock(), dir);
                setLineNumbers(transform1);
                setLops(transform1);
                if (getDataType() == DataType.SCALAR) {
                    UnaryCP unary1 = new UnaryCP(transform1, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
                    unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
                    setLineNumbers(unary1);
                    setLops(unary1);
                }
            } else //default
            {
                boolean needAgg = requiresAggregation(input, _direction);
                SparkAggType aggtype = getSparkUnaryAggregationType(needAgg);
                PartialAggregate aggregate = new PartialAggregate(input.constructLops(), HopsAgg2Lops.get(_op), HopsDirection2Lops.get(_direction), DataType.MATRIX, getValueType(), aggtype, et);
                aggregate.setDimensionsBasedOnDirection(getDim1(), getDim2(), input.getRowsInBlock(), input.getColsInBlock());
                setLineNumbers(aggregate);
                setLops(aggregate);
                if (getDataType() == DataType.SCALAR) {
                    UnaryCP unary1 = new UnaryCP(aggregate, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
                    unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
                    setLineNumbers(unary1);
                    setLops(unary1);
                }
            }
        }
    } catch (Exception e) {
        throw new HopsException(this.printErrorLocation() + "In AggUnary Hop, error constructing Lops ", e);
    }
    //add reblock/checkpoint lops if necessary
    constructAndSetLopsDataFlowProperties();
    //return created lops
    return getLops();
}

Example 2 with PartialAggregate

use of org.apache.sysml.lops.PartialAggregate in project incubator-systemml by apache.

the class UnaryOp method constructLopsIQM.

private Lop constructLopsIQM() throws HopsException, LopsException {
    ExecType et = optFindExecType();
    Hop input = getInput().get(0);
    if (et == ExecType.MR) {
        CombineUnary combine = CombineUnary.constructCombineLop(input.constructLops(), DataType.MATRIX, getValueType());
        combine.getOutputParameters().setDimensions(input.getDim1(), input.getDim2(), input.getRowsInBlock(), input.getColsInBlock(), input.getNnz());
        SortKeys sort = SortKeys.constructSortByValueLop(combine, SortKeys.OperationTypes.WithoutWeights, DataType.MATRIX, ValueType.DOUBLE, ExecType.MR);
        // Sort dimensions are same as the first input
        sort.getOutputParameters().setDimensions(input.getDim1(), input.getDim2(), input.getRowsInBlock(), input.getColsInBlock(), input.getNnz());
        Data lit = Data.createLiteralLop(ValueType.DOUBLE, Double.toString(0.25));
        lit.setAllPositions(this.getBeginLine(), this.getBeginColumn(), this.getEndLine(), this.getEndColumn());
        PickByCount pick = new PickByCount(sort, lit, DataType.MATRIX, getValueType(), PickByCount.OperationTypes.RANGEPICK);
        pick.getOutputParameters().setDimensions(-1, -1, getRowsInBlock(), getColsInBlock(), -1);
        setLineNumbers(pick);
        PartialAggregate pagg = new PartialAggregate(pick, HopsAgg2Lops.get(Hop.AggOp.SUM), HopsDirection2Lops.get(Hop.Direction.RowCol), DataType.MATRIX, getValueType());
        setLineNumbers(pagg);
        // Set the dimensions of PartialAggregate LOP based on the
        // direction in which aggregation is performed
        pagg.setDimensionsBasedOnDirection(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock());
        Group group1 = new Group(pagg, Group.OperationTypes.Sort, DataType.MATRIX, getValueType());
        group1.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        setLineNumbers(group1);
        Aggregate agg1 = new Aggregate(group1, HopsAgg2Lops.get(Hop.AggOp.SUM), DataType.MATRIX, getValueType(), ExecType.MR);
        agg1.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        agg1.setupCorrectionLocation(pagg.getCorrectionLocation());
        setLineNumbers(agg1);
        UnaryCP unary1 = new UnaryCP(agg1, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
        unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(unary1);
        Unary iqm = new Unary(sort, unary1, Unary.OperationTypes.MR_IQM, DataType.SCALAR, ValueType.DOUBLE, ExecType.CP);
        iqm.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(iqm);
        return iqm;
    } else {
        SortKeys sort = SortKeys.constructSortByValueLop(input.constructLops(), SortKeys.OperationTypes.WithoutWeights, DataType.MATRIX, ValueType.DOUBLE, et);
        sort.getOutputParameters().setDimensions(input.getDim1(), input.getDim2(), input.getRowsInBlock(), input.getColsInBlock(), input.getNnz());
        PickByCount pick = new PickByCount(sort, null, getDataType(), getValueType(), PickByCount.OperationTypes.IQM, et, true);
        pick.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        setLineNumbers(pick);
        return pick;
    }
}

Example 3 with PartialAggregate

use of org.apache.sysml.lops.PartialAggregate in project incubator-systemml by apache.

the class BinaryOp method constructLopsIQM.

private void constructLopsIQM(ExecType et) throws HopsException, LopsException {
    if (et == ExecType.MR) {
        CombineBinary combine = CombineBinary.constructCombineLop(OperationTypes.PreSort, (Lop) getInput().get(0).constructLops(), (Lop) getInput().get(1).constructLops(), DataType.MATRIX, getValueType());
        combine.getOutputParameters().setDimensions(getInput().get(0).getDim1(), getInput().get(0).getDim2(), getInput().get(0).getRowsInBlock(), getInput().get(0).getColsInBlock(), getInput().get(0).getNnz());
        SortKeys sort = SortKeys.constructSortByValueLop(combine, SortKeys.OperationTypes.WithWeights, DataType.MATRIX, ValueType.DOUBLE, ExecType.MR);
        // Sort dimensions are same as the first input
        sort.getOutputParameters().setDimensions(getInput().get(0).getDim1(), getInput().get(0).getDim2(), getInput().get(0).getRowsInBlock(), getInput().get(0).getColsInBlock(), getInput().get(0).getNnz());
        Data lit = Data.createLiteralLop(ValueType.DOUBLE, Double.toString(0.25));
        setLineNumbers(lit);
        PickByCount pick = new PickByCount(sort, lit, DataType.MATRIX, getValueType(), PickByCount.OperationTypes.RANGEPICK);
        pick.getOutputParameters().setDimensions(-1, -1, getRowsInBlock(), getColsInBlock(), -1);
        setLineNumbers(pick);
        PartialAggregate pagg = new PartialAggregate(pick, HopsAgg2Lops.get(Hop.AggOp.SUM), HopsDirection2Lops.get(Hop.Direction.RowCol), DataType.MATRIX, getValueType());
        setLineNumbers(pagg);
        // Set the dimensions of PartialAggregate LOP based on the
        // direction in which aggregation is performed
        pagg.setDimensionsBasedOnDirection(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock());
        Group group1 = new Group(pagg, Group.OperationTypes.Sort, DataType.MATRIX, getValueType());
        setOutputDimensions(group1);
        setLineNumbers(group1);
        Aggregate agg1 = new Aggregate(group1, HopsAgg2Lops.get(Hop.AggOp.SUM), DataType.MATRIX, getValueType(), ExecType.MR);
        setOutputDimensions(agg1);
        agg1.setupCorrectionLocation(pagg.getCorrectionLocation());
        setLineNumbers(agg1);
        UnaryCP unary1 = new UnaryCP(agg1, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), DataType.SCALAR, getValueType());
        unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(unary1);
        Unary iqm = new Unary(sort, unary1, Unary.OperationTypes.MR_IQM, DataType.SCALAR, ValueType.DOUBLE, ExecType.CP);
        iqm.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(iqm);
        setLops(iqm);
    } else {
        SortKeys sort = SortKeys.constructSortByValueLop(getInput().get(0).constructLops(), getInput().get(1).constructLops(), SortKeys.OperationTypes.WithWeights, getInput().get(0).getDataType(), getInput().get(0).getValueType(), et);
        sort.getOutputParameters().setDimensions(getInput().get(0).getDim1(), getInput().get(0).getDim2(), getInput().get(0).getRowsInBlock(), getInput().get(0).getColsInBlock(), getInput().get(0).getNnz());
        PickByCount pick = new PickByCount(sort, null, getDataType(), getValueType(), PickByCount.OperationTypes.IQM, et, true);
        setOutputDimensions(pick);
        setLineNumbers(pick);
        setLops(pick);
    }
}