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

Example 1 with UnaryCP

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

the class QuaternaryOp method constructMRLopsWeightedCeMM.

private void constructMRLopsWeightedCeMM(WCeMMType wtype) throws HopsException, LopsException {
    //NOTE: the common case for wcemm are factors U/V with a rank of 10s to 100s; the current runtime only
    //supports single block outer products (U/V rank <= blocksize, i.e., 1000 by default); we enforce this
    //by applying the hop rewrite for Weighted Cross Entropy only if this constraint holds. 
    Hop X = getInput().get(0);
    Hop U = getInput().get(1);
    Hop V = getInput().get(2);
    Hop eps = getInput().get(3);
    //MR operator selection, part1
    //size U
    double m1Size = OptimizerUtils.estimateSize(U.getDim1(), U.getDim2());
    //size V
    double m2Size = OptimizerUtils.estimateSize(V.getDim1(), V.getDim2());
    boolean isMapWcemm = (m1Size + m2Size < OptimizerUtils.getRemoteMemBudgetMap(true));
    if (//broadcast
    !FORCE_REPLICATION && isMapWcemm) {
        //partitioning of U
        boolean needPartU = !U.dimsKnown() || U.getDim1() * U.getDim2() > DistributedCacheInput.PARTITION_SIZE;
        Lop lU = U.constructLops();
        if (needPartU) {
            //requires partitioning
            lU = new DataPartition(lU, DataType.MATRIX, ValueType.DOUBLE, (m1Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
            lU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), getRowsInBlock(), getColsInBlock(), U.getNnz());
            setLineNumbers(lU);
        }
        //partitioning of V
        boolean needPartV = !V.dimsKnown() || V.getDim1() * V.getDim2() > DistributedCacheInput.PARTITION_SIZE;
        Lop lV = V.constructLops();
        if (needPartV) {
            //requires partitioning
            lV = new DataPartition(lV, DataType.MATRIX, ValueType.DOUBLE, (m2Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
            lV.getOutputParameters().setDimensions(V.getDim1(), V.getDim2(), getRowsInBlock(), getColsInBlock(), V.getNnz());
            setLineNumbers(lV);
        }
        //map-side wcemm always with broadcast
        Lop wcemm = new WeightedCrossEntropy(X.constructLops(), lU, lV, eps.constructLops(), DataType.MATRIX, ValueType.DOUBLE, wtype, ExecType.MR);
        wcemm.getOutputParameters().setDimensions(1, 1, X.getRowsInBlock(), X.getColsInBlock(), -1);
        setLineNumbers(wcemm);
        Group grp = new Group(wcemm, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        grp.getOutputParameters().setDimensions(1, 1, X.getRowsInBlock(), X.getColsInBlock(), -1);
        setLineNumbers(grp);
        Aggregate agg1 = new Aggregate(grp, HopsAgg2Lops.get(AggOp.SUM), DataType.MATRIX, ValueType.DOUBLE, ExecType.MR);
        // aggregation uses kahanSum 
        agg1.setupCorrectionLocation(CorrectionLocationType.NONE);
        agg1.getOutputParameters().setDimensions(1, 1, X.getRowsInBlock(), X.getColsInBlock(), -1);
        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);
        setLops(unary1);
    } else //general case
    {
        //MR operator selection part 2
        boolean cacheU = !FORCE_REPLICATION && (m1Size < OptimizerUtils.getRemoteMemBudgetReduce());
        boolean cacheV = !FORCE_REPLICATION && ((!cacheU && m2Size < OptimizerUtils.getRemoteMemBudgetReduce()) || (cacheU && m1Size + m2Size < OptimizerUtils.getRemoteMemBudgetReduce()));
        Group grpX = new Group(X.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        grpX.getOutputParameters().setDimensions(X.getDim1(), X.getDim2(), X.getRowsInBlock(), X.getColsInBlock(), -1);
        setLineNumbers(grpX);
        Lop lU = null;
        if (cacheU) {
            //partitioning of U for read through distributed cache
            boolean needPartU = !U.dimsKnown() || U.getDim1() * U.getDim2() > DistributedCacheInput.PARTITION_SIZE;
            lU = U.constructLops();
            if (needPartU) {
                //requires partitioning
                lU = new DataPartition(lU, DataType.MATRIX, ValueType.DOUBLE, (m1Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
                lU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), getRowsInBlock(), getColsInBlock(), U.getNnz());
                setLineNumbers(lU);
            }
        } else {
            //replication of U for shuffle to target block
            //ncol of t(V) -> nrow of V determines num replicates
            Lop offset = createOffsetLop(V, false);
            lU = new RepMat(U.constructLops(), offset, true, V.getDataType(), V.getValueType());
            lU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), U.getRowsInBlock(), U.getColsInBlock(), U.getNnz());
            setLineNumbers(lU);
            Group grpU = new Group(lU, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
            grpU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), U.getRowsInBlock(), U.getColsInBlock(), -1);
            setLineNumbers(grpU);
            lU = grpU;
        }
        Lop lV = null;
        if (cacheV) {
            //partitioning of V for read through distributed cache
            boolean needPartV = !V.dimsKnown() || V.getDim1() * V.getDim2() > DistributedCacheInput.PARTITION_SIZE;
            lV = V.constructLops();
            if (needPartV) {
                //requires partitioning
                lV = new DataPartition(lV, DataType.MATRIX, ValueType.DOUBLE, (m2Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
                lV.getOutputParameters().setDimensions(V.getDim1(), V.getDim2(), getRowsInBlock(), getColsInBlock(), V.getNnz());
                setLineNumbers(lV);
            }
        } else {
            //replication of t(V) for shuffle to target block
            Transform ltV = new Transform(V.constructLops(), HopsTransf2Lops.get(ReOrgOp.TRANSPOSE), getDataType(), getValueType(), ExecType.MR);
            ltV.getOutputParameters().setDimensions(V.getDim2(), V.getDim1(), V.getColsInBlock(), V.getRowsInBlock(), V.getNnz());
            setLineNumbers(ltV);
            //nrow of U determines num replicates
            Lop offset = createOffsetLop(U, false);
            lV = new RepMat(ltV, offset, false, V.getDataType(), V.getValueType());
            lV.getOutputParameters().setDimensions(V.getDim2(), V.getDim1(), V.getColsInBlock(), V.getRowsInBlock(), V.getNnz());
            setLineNumbers(lV);
            Group grpV = new Group(lV, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
            grpV.getOutputParameters().setDimensions(V.getDim2(), V.getDim1(), V.getColsInBlock(), V.getRowsInBlock(), -1);
            setLineNumbers(grpV);
            lV = grpV;
        }
        //reduce-side wcemm w/ or without broadcast
        Lop wcemm = new WeightedCrossEntropyR(grpX, lU, lV, eps.constructLops(), DataType.MATRIX, ValueType.DOUBLE, wtype, cacheU, cacheV, ExecType.MR);
        wcemm.getOutputParameters().setDimensions(1, 1, X.getRowsInBlock(), X.getColsInBlock(), -1);
        setLineNumbers(wcemm);
        Group grp = new Group(wcemm, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        grp.getOutputParameters().setDimensions(1, 1, X.getRowsInBlock(), X.getColsInBlock(), -1);
        setLineNumbers(grp);
        Aggregate agg1 = new Aggregate(grp, HopsAgg2Lops.get(AggOp.SUM), DataType.MATRIX, ValueType.DOUBLE, ExecType.MR);
        // aggregation uses kahanSum 
        agg1.setupCorrectionLocation(CorrectionLocationType.NONE);
        agg1.getOutputParameters().setDimensions(1, 1, X.getRowsInBlock(), X.getColsInBlock(), -1);
        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);
        setLops(unary1);
    }
}

Example 2 with UnaryCP

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

the class UnaryOp method constructLops.

@Override
public Lop constructLops() throws HopsException, LopsException {
    //reuse existing lop
    if (getLops() != null)
        return getLops();
    try {
        Hop input = getInput().get(0);
        if (//value type casts or matrix to scalar
        getDataType() == DataType.SCALAR || (_op == OpOp1.CAST_AS_MATRIX && getInput().get(0).getDataType() == DataType.SCALAR) || (_op == OpOp1.CAST_AS_FRAME && getInput().get(0).getDataType() == DataType.SCALAR)) {
            if (//special handling IQM
            _op == Hop.OpOp1.IQM) {
                Lop iqmLop = constructLopsIQM();
                setLops(iqmLop);
            } else if (_op == Hop.OpOp1.MEDIAN) {
                Lop medianLop = constructLopsMedian();
                setLops(medianLop);
            } else //general case SCALAR/CAST (always in CP)
            {
                UnaryCP.OperationTypes optype = HopsOpOp1LopsUS.get(_op);
                if (optype == null)
                    throw new HopsException("Unknown UnaryCP lop type for UnaryOp operation type '" + _op + "'");
                UnaryCP unary1 = null;
                if ((_op == Hop.OpOp1.NROW || _op == Hop.OpOp1.NCOL || _op == Hop.OpOp1.LENGTH) && input instanceof UnaryOp && ((UnaryOp) input).getOp() == OpOp1.SELP) {
                    // Dimensions does not change during sel+ operation.
                    // This case is helpful to avoid unnecessary sel+ operation for fused maxpooling.
                    unary1 = new UnaryCP(input.getInput().get(0).constructLops(), optype, getDataType(), getValueType());
                } else
                    unary1 = new UnaryCP(input.constructLops(), optype, getDataType(), getValueType());
                setOutputDimensions(unary1);
                setLineNumbers(unary1);
                setLops(unary1);
            }
        } else //general case MATRIX
        {
            ExecType et = optFindExecType();
            //special handling cumsum/cumprod/cummin/cumsum
            if (isCumulativeUnaryOperation() && et != ExecType.CP) {
                //TODO additional physical operation if offsets fit in memory
                Lop cumsumLop = null;
                if (et == ExecType.MR)
                    cumsumLop = constructLopsMRCumulativeUnary();
                else
                    cumsumLop = constructLopsSparkCumulativeUnary();
                setLops(cumsumLop);
            } else //default unary 
            {
                int k = isCumulativeUnaryOperation() ? OptimizerUtils.getConstrainedNumThreads(_maxNumThreads) : 1;
                switch(_op) {
                    case SELP:
                    case EXP:
                    case SQRT:
                    case LOG:
                    case ABS:
                    case ROUND:
                    case FLOOR:
                    case CEIL:
                    case SIN:
                    case COS:
                    case TAN:
                    case ASIN:
                    case ACOS:
                    case ATAN:
                    case SIGN:
                        et = findGPUExecTypeByMemEstimate(et);
                        break;
                    default:
                }
                Unary unary1 = new Unary(input.constructLops(), HopsOpOp1LopsU.get(_op), getDataType(), getValueType(), et, k);
                setOutputDimensions(unary1);
                setLineNumbers(unary1);
                setLops(unary1);
            }
        }
    } catch (Exception e) {
        throw new HopsException(this.printErrorLocation() + "error constructing Lops for UnaryOp Hop -- \n ", e);
    }
    //add reblock/checkpoint lops if necessary
    constructAndSetLopsDataFlowProperties();
    return getLops();
}

Example 3 with UnaryCP

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

the class BinaryOp method constructLopsCentralMoment.

private void constructLopsCentralMoment(ExecType et) throws HopsException, LopsException {
    // The output data type is a SCALAR if central moment 
    // gets computed in CP/SPARK, and it will be MATRIX otherwise.
    DataType dt = (et == ExecType.MR ? DataType.MATRIX : DataType.SCALAR);
    CentralMoment cm = new CentralMoment(getInput().get(0).constructLops(), getInput().get(1).constructLops(), dt, getValueType(), et);
    setLineNumbers(cm);
    if (et == ExecType.MR) {
        cm.getOutputParameters().setDimensions(1, 1, 0, 0, -1);
        UnaryCP unary1 = new UnaryCP(cm, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
        unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(unary1);
        setLops(unary1);
    } else {
        cm.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLops(cm);
    }
}

Example 4 with UnaryCP

use of org.apache.sysml.lops.UnaryCP 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 5 with UnaryCP

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

the class TernaryOp method constructLopsCentralMoment.

/**
	 * Method to construct LOPs when op = CENTRAILMOMENT.
	 * 
	 * @throws HopsException if HopsException occurs
	 * @throws LopsException if LopsException occurs
	 */
private void constructLopsCentralMoment() throws HopsException, LopsException {
    if (_op != OpOp3.CENTRALMOMENT)
        throw new HopsException("Unexpected operation: " + _op + ", expecting " + OpOp3.CENTRALMOMENT);
    ExecType et = optFindExecType();
    if (et == ExecType.MR) {
        CombineBinary combine = CombineBinary.constructCombineLop(OperationTypes.PreCentralMoment, getInput().get(0).constructLops(), 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());
        CentralMoment cm = new CentralMoment(combine, getInput().get(2).constructLops(), DataType.MATRIX, getValueType(), et);
        cm.getOutputParameters().setDimensions(1, 1, 0, 0, -1);
        setLineNumbers(cm);
        UnaryCP unary1 = new UnaryCP(cm, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
        unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(unary1);
        setLops(unary1);
    } else //CP / SPARK
    {
        CentralMoment cm = new CentralMoment(getInput().get(0).constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getDataType(), getValueType(), et);
        cm.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(cm);
        setLops(cm);
    }
}