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

Example 11 with Aggregate

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

the class AggUnaryOp method constructLops.

@Override
public Lop constructLops() {
    // return already created lops
    if (getLops() != null)
        return getLops();
    try {
        ExecType et = optFindExecType();
        Hop input = getInput().get(0);
        if (et == ExecType.CP || et == ExecType.GPU) {
            Lop agg1 = null;
            long numChannels = isChannelSumRewriteApplicable() ? Hop.computeSizeInformation(getInput().get(0).getInput().get(1)) : -1;
            if (numChannels > 0 && numChannels < 1000000) {
                // Apply channel sums only if rewrite is applicable and if the dimension of C is known at compile time
                // and if numChannels is less than 8 MB.
                ReorgOp in = ((ReorgOp) getInput().get(0));
                agg1 = new ConvolutionTransform(in.getInput().get(0).getInput().get(0).constructLops(), in.getInput().get(1).constructLops(), in.getInput().get(2).constructLops(), ConvolutionTransform.OperationTypes.CHANNEL_SUMS, getDataType(), getValueType(), et, -1);
                agg1.getOutputParameters().setDimensions(numChannels, 1, getRowsInBlock(), getColsInBlock(), -1);
                setLineNumbers(agg1);
                setLops(agg1);
            } else {
                if (isTernaryAggregateRewriteApplicable()) {
                    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);
                    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()) {
                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 12 with Aggregate

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

the class IndexingOp method constructLops.

@Override
public Lop constructLops() {
    // return already created lops
    if (getLops() != null)
        return getLops();
    Hop input = getInput().get(0);
    // rewrite remove unnecessary right indexing
    if (HopRewriteUtils.isUnnecessaryRightIndexing(this)) {
        setLops(input.constructLops());
    } else // actual lop construction, incl operator selection
    {
        try {
            ExecType et = optFindExecType();
            if (et == ExecType.MR) {
                IndexingMethod method = optFindIndexingMethod(_rowLowerEqualsUpper, _colLowerEqualsUpper, input._dim1, input._dim2, _dim1, _dim2);
                Lop dummy = Data.createLiteralLop(ValueType.INT, Integer.toString(-1));
                RightIndex reindex = new RightIndex(input.constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getInput().get(3).constructLops(), getInput().get(4).constructLops(), dummy, dummy, getDataType(), getValueType(), et);
                setOutputDimensions(reindex);
                setLineNumbers(reindex);
                if (method == IndexingMethod.MR_RIX) {
                    Group group1 = new Group(reindex, Group.OperationTypes.Sort, DataType.MATRIX, getValueType());
                    setOutputDimensions(group1);
                    setLineNumbers(group1);
                    Aggregate agg1 = new Aggregate(group1, Aggregate.OperationTypes.Sum, DataType.MATRIX, getValueType(), et);
                    setOutputDimensions(agg1);
                    setLineNumbers(agg1);
                    setLops(agg1);
                } else // method == IndexingMethod.MR_VRIX
                {
                    setLops(reindex);
                }
            } else if (et == ExecType.SPARK) {
                IndexingMethod method = optFindIndexingMethod(_rowLowerEqualsUpper, _colLowerEqualsUpper, input._dim1, input._dim2, _dim1, _dim2);
                SparkAggType aggtype = (method == IndexingMethod.MR_VRIX || isBlockAligned()) ? SparkAggType.NONE : SparkAggType.MULTI_BLOCK;
                Lop dummy = Data.createLiteralLop(ValueType.INT, Integer.toString(-1));
                RightIndex reindex = new RightIndex(input.constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getInput().get(3).constructLops(), getInput().get(4).constructLops(), dummy, dummy, getDataType(), getValueType(), aggtype, et);
                setOutputDimensions(reindex);
                setLineNumbers(reindex);
                setLops(reindex);
            } else // CP or GPU
            {
                Lop dummy = Data.createLiteralLop(ValueType.INT, Integer.toString(-1));
                RightIndex reindex = new RightIndex(input.constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getInput().get(3).constructLops(), getInput().get(4).constructLops(), dummy, dummy, getDataType(), getValueType(), et);
                setOutputDimensions(reindex);
                setLineNumbers(reindex);
                setLops(reindex);
            }
        } catch (Exception e) {
            throw new HopsException(this.printErrorLocation() + "In IndexingOp Hop, error constructing Lops ", e);
        }
    }
    // add reblock/checkpoint lops if necessary
    constructAndSetLopsDataFlowProperties();
    return getLops();
}

Example 13 with Aggregate

use of org.apache.sysml.lops.Aggregate in project systemml by apache.

the class BinaryOp method constructMRAppendLop.

/**
 * General case binary append.
 *
 * @param left high-level operator left
 * @param right high-level operator right
 * @param dt data type
 * @param vt value type
 * @param cbind true if cbind
 * @param current current high-level operator
 * @return low-level operator
 */
public static Lop constructMRAppendLop(Hop left, Hop right, DataType dt, ValueType vt, boolean cbind, Hop current) {
    Lop ret = null;
    long m1_dim1 = left.getDim1();
    long m1_dim2 = left.getDim2();
    long m2_dim1 = right.getDim1();
    long m2_dim2 = right.getDim2();
    // output rows
    long m3_dim1 = cbind ? m1_dim1 : ((m1_dim1 >= 0 && m2_dim1 >= 0) ? (m1_dim1 + m2_dim1) : -1);
    // output cols
    long m3_dim2 = cbind ? ((m1_dim2 >= 0 && m2_dim2 >= 0) ? (m1_dim2 + m2_dim2) : -1) : m1_dim2;
    // output nnz
    long m3_nnz = (left.getNnz() > 0 && right.getNnz() > 0) ? (left.getNnz() + right.getNnz()) : -1;
    long brlen = left.getRowsInBlock();
    long bclen = left.getColsInBlock();
    // offset 1st input
    Lop offset = createOffsetLop(left, cbind);
    AppendMethod am = optFindAppendMethod(m1_dim1, m1_dim2, m2_dim1, m2_dim2, brlen, bclen, cbind);
    switch(am) {
        case // special case map-only append
        MR_MAPPEND:
            {
                boolean needPart = requiresPartitioning(right);
                // pre partitioning
                Lop dcInput = right.constructLops();
                if (needPart) {
                    // right side in distributed cache
                    ExecType etPart = (OptimizerUtils.estimateSizeExactSparsity(right.getDim1(), right.getDim2(), OptimizerUtils.getSparsity(right.getDim1(), right.getDim2(), right.getNnz())) < OptimizerUtils.getLocalMemBudget()) ? ExecType.CP : // operator selection
                    ExecType.MR;
                    dcInput = new DataPartition(dcInput, DataType.MATRIX, ValueType.DOUBLE, etPart, PDataPartitionFormat.ROW_BLOCK_WISE_N);
                    dcInput.getOutputParameters().setDimensions(right.getDim1(), right.getDim2(), right.getRowsInBlock(), right.getColsInBlock(), right.getNnz());
                    dcInput.setAllPositions(right.getFilename(), right.getBeginLine(), right.getBeginColumn(), right.getEndLine(), right.getEndColumn());
                }
                AppendM appM = new AppendM(left.constructLops(), dcInput, offset, dt, vt, cbind, needPart, ExecType.MR);
                appM.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
                appM.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, m3_nnz);
                ret = appM;
                break;
            }
        case // special case reduce append w/ one column block
        MR_RAPPEND:
            {
                // group
                Group group1 = new Group(left.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, vt);
                group1.getOutputParameters().setDimensions(m1_dim1, m1_dim2, brlen, bclen, left.getNnz());
                group1.setAllPositions(left.getFilename(), left.getBeginLine(), left.getBeginColumn(), left.getEndLine(), left.getEndColumn());
                Group group2 = new Group(right.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, vt);
                group1.getOutputParameters().setDimensions(m2_dim1, m2_dim2, brlen, bclen, right.getNnz());
                group1.setAllPositions(right.getFilename(), right.getBeginLine(), right.getBeginColumn(), right.getEndLine(), right.getEndColumn());
                AppendR appR = new AppendR(group1, group2, dt, vt, cbind, ExecType.MR);
                appR.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, m3_nnz);
                appR.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
                ret = appR;
                break;
            }
        case MR_GAPPEND:
            {
                // general case: map expand append, reduce aggregate
                // offset second input
                Lop offset2 = createOffsetLop(right, cbind);
                AppendG appG = new AppendG(left.constructLops(), right.constructLops(), offset, offset2, dt, vt, cbind, ExecType.MR);
                appG.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, m3_nnz);
                appG.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
                // group
                Group group1 = new Group(appG, Group.OperationTypes.Sort, DataType.MATRIX, vt);
                group1.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, m3_nnz);
                group1.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
                // aggregate
                Aggregate agg1 = new Aggregate(group1, Aggregate.OperationTypes.Sum, DataType.MATRIX, vt, ExecType.MR);
                agg1.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, m3_nnz);
                agg1.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
                ret = agg1;
                break;
            }
        default:
            throw new HopsException("Invalid MR append method: " + am);
    }
    return ret;
}

Example 14 with Aggregate

use of org.apache.sysml.lops.Aggregate in project systemml by apache.

the class BinaryOp method constructLopsIQM.

private void constructLopsIQM(ExecType et) {
    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);
    }
}

Example 15 with Aggregate

use of org.apache.sysml.lops.Aggregate in project systemml by apache.

the class IndexingOp method constructLops.

@Override
public Lop constructLops() {
    // return already created lops
    if (getLops() != null)
        return getLops();
    Hop input = getInput().get(0);
    // rewrite remove unnecessary right indexing
    if (HopRewriteUtils.isUnnecessaryRightIndexing(this)) {
        setLops(input.constructLops());
    } else // actual lop construction, incl operator selection
    {
        try {
            ExecType et = optFindExecType();
            if (et == ExecType.MR) {
                IndexingMethod method = optFindIndexingMethod(_rowLowerEqualsUpper, _colLowerEqualsUpper, input._dim1, input._dim2, _dim1, _dim2);
                Lop dummy = Data.createLiteralLop(ValueType.INT, Integer.toString(-1));
                RightIndex reindex = new RightIndex(input.constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getInput().get(3).constructLops(), getInput().get(4).constructLops(), dummy, dummy, getDataType(), getValueType(), et);
                setOutputDimensions(reindex);
                setLineNumbers(reindex);
                if (method == IndexingMethod.MR_RIX) {
                    Group group1 = new Group(reindex, Group.OperationTypes.Sort, DataType.MATRIX, getValueType());
                    setOutputDimensions(group1);
                    setLineNumbers(group1);
                    Aggregate agg1 = new Aggregate(group1, Aggregate.OperationTypes.Sum, DataType.MATRIX, getValueType(), et);
                    setOutputDimensions(agg1);
                    setLineNumbers(agg1);
                    setLops(agg1);
                } else // method == IndexingMethod.MR_VRIX
                {
                    setLops(reindex);
                }
            } else if (et == ExecType.SPARK) {
                IndexingMethod method = optFindIndexingMethod(_rowLowerEqualsUpper, _colLowerEqualsUpper, input._dim1, input._dim2, _dim1, _dim2);
                SparkAggType aggtype = (method == IndexingMethod.MR_VRIX || isBlockAligned()) ? SparkAggType.NONE : SparkAggType.MULTI_BLOCK;
                Lop dummy = Data.createLiteralLop(ValueType.INT, Integer.toString(-1));
                RightIndex reindex = new RightIndex(input.constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getInput().get(3).constructLops(), getInput().get(4).constructLops(), dummy, dummy, getDataType(), getValueType(), aggtype, et);
                setOutputDimensions(reindex);
                setLineNumbers(reindex);
                setLops(reindex);
            } else // CP or GPU
            {
                Lop dummy = Data.createLiteralLop(ValueType.INT, Integer.toString(-1));
                RightIndex reindex = new RightIndex(input.constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), getInput().get(3).constructLops(), getInput().get(4).constructLops(), dummy, dummy, getDataType(), getValueType(), et);
                setOutputDimensions(reindex);
                setLineNumbers(reindex);
                setLops(reindex);
            }
        } catch (Exception e) {
            throw new HopsException(this.printErrorLocation() + "In IndexingOp Hop, error constructing Lops ", e);
        }
    }
    // add reblock/checkpoint lops if necessary
    constructAndSetLopsDataFlowProperties();
    return getLops();
}