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

Example 51 with Group

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

the class AggBinaryOp method constructMRLopsCPMM.

private void constructMRLopsCPMM() {
    if (isLeftTransposeRewriteApplicable(false, false)) {
        setLops(constructMRLopsCPMMWithLeftTransposeRewrite());
    } else // general case
    {
        Hop X = getInput().get(0);
        Hop Y = getInput().get(1);
        MMCJType type = getMMCJAggregationType(X, Y);
        MMCJ mmcj = new MMCJ(X.constructLops(), Y.constructLops(), getDataType(), getValueType(), type, ExecType.MR);
        setOutputDimensions(mmcj);
        setLineNumbers(mmcj);
        Group grp = new Group(mmcj, Group.OperationTypes.Sort, getDataType(), getValueType());
        setOutputDimensions(grp);
        setLineNumbers(grp);
        Aggregate agg1 = new Aggregate(grp, HopsAgg2Lops.get(outerOp), getDataType(), getValueType(), ExecType.MR);
        setOutputDimensions(agg1);
        setLineNumbers(agg1);
        // aggregation uses kahanSum but the inputs do not have correction values
        agg1.setupCorrectionLocation(CorrectionLocationType.NONE);
        setLops(agg1);
    }
}

Example 52 with Group

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

the class AggBinaryOp method constructMRLopsMapMMWithLeftTransposeRewrite.

private Lop constructMRLopsMapMMWithLeftTransposeRewrite() {
    // guaranteed to exists
    Hop X = getInput().get(0).getInput().get(0);
    Hop Y = getInput().get(1);
    // right vector transpose CP
    Lop tY = new Transform(Y.constructLops(), OperationTypes.Transpose, getDataType(), getValueType(), ExecType.CP);
    tY.getOutputParameters().setDimensions(Y.getDim2(), Y.getDim1(), getRowsInBlock(), getColsInBlock(), Y.getNnz());
    setLineNumbers(tY);
    // matrix mult
    // If number of columns is smaller than block size then explicit aggregation is not required.
    // i.e., entire matrix multiplication can be performed in the mappers.
    boolean needAgg = (X.getDim1() <= 0 || X.getDim1() > X.getRowsInBlock());
    // R disregarding transpose rewrite
    boolean needPart = requiresPartitioning(MMultMethod.MAPMM_R, true);
    // pre partitioning
    Lop dcinput = null;
    if (needPart) {
        ExecType etPart = (OptimizerUtils.estimateSizeExactSparsity(Y.getDim2(), Y.getDim1(), OptimizerUtils.getSparsity(Y.getDim2(), Y.getDim1(), Y.getNnz())) < OptimizerUtils.getLocalMemBudget()) ? ExecType.CP : // operator selection
        ExecType.MR;
        dcinput = new DataPartition(tY, DataType.MATRIX, ValueType.DOUBLE, etPart, PDataPartitionFormat.COLUMN_BLOCK_WISE_N);
        dcinput.getOutputParameters().setDimensions(Y.getDim2(), Y.getDim1(), getRowsInBlock(), getColsInBlock(), Y.getNnz());
        setLineNumbers(dcinput);
    } else
        dcinput = tY;
    MapMult mapmult = new MapMult(dcinput, X.constructLops(), getDataType(), getValueType(), false, needPart, false);
    mapmult.getOutputParameters().setDimensions(Y.getDim2(), X.getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    setLineNumbers(mapmult);
    // post aggregation
    Lop mult = null;
    if (needAgg) {
        Group grp = new Group(mapmult, Group.OperationTypes.Sort, getDataType(), getValueType());
        grp.getOutputParameters().setDimensions(Y.getDim2(), X.getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        setLineNumbers(grp);
        Aggregate agg1 = new Aggregate(grp, HopsAgg2Lops.get(outerOp), getDataType(), getValueType(), ExecType.MR);
        agg1.getOutputParameters().setDimensions(Y.getDim2(), X.getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        setLineNumbers(agg1);
        agg1.setupCorrectionLocation(CorrectionLocationType.NONE);
        mult = agg1;
    } else
        mult = mapmult;
    // result transpose CP
    Lop out = new Transform(mult, OperationTypes.Transpose, getDataType(), getValueType(), ExecType.CP);
    out.getOutputParameters().setDimensions(X.getDim2(), Y.getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    return out;
}

Example 53 with Group

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

the class AggBinaryOp method constructMRLopsMapMMChain.

private void constructMRLopsMapMMChain(ChainType chainType) {
    Lop mapmult = null;
    if (chainType == ChainType.XtXv) {
        // v never needs partitioning because always single block
        Hop hX = getInput().get(0).getInput().get(0);
        Hop hv = getInput().get(1).getInput().get(1);
        // core matrix mult
        mapmult = new MapMultChain(hX.constructLops(), hv.constructLops(), getDataType(), getValueType(), ExecType.MR);
        mapmult.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        setLineNumbers(mapmult);
    } else // ChainType.XtwXv / ChainType.XtXvy
    {
        // v never needs partitioning because always single block
        int wix = (chainType == ChainType.XtwXv) ? 0 : 1;
        int vix = (chainType == ChainType.XtwXv) ? 1 : 0;
        Hop hX = getInput().get(0).getInput().get(0);
        Hop hw = getInput().get(1).getInput().get(wix);
        Hop hv = getInput().get(1).getInput().get(vix).getInput().get(1);
        double mestW = OptimizerUtils.estimateSize(hw.getDim1(), hw.getDim2());
        boolean needPart = !hw.dimsKnown() || hw.getDim1() * hw.getDim2() > DistributedCacheInput.PARTITION_SIZE;
        Lop X = hX.constructLops(), v = hv.constructLops(), w = null;
        if (needPart) {
            // requires partitioning
            w = new DataPartition(hw.constructLops(), DataType.MATRIX, ValueType.DOUBLE, (mestW > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
            w.getOutputParameters().setDimensions(hw.getDim1(), hw.getDim2(), getRowsInBlock(), getColsInBlock(), hw.getNnz());
            setLineNumbers(w);
        } else
            w = hw.constructLops();
        // core matrix mult
        mapmult = new MapMultChain(X, v, w, chainType, getDataType(), getValueType(), ExecType.MR);
        mapmult.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
        setLineNumbers(mapmult);
    }
    // post aggregation
    Group grp = new Group(mapmult, Group.OperationTypes.Sort, getDataType(), getValueType());
    grp.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    Aggregate agg1 = new Aggregate(grp, HopsAgg2Lops.get(outerOp), getDataType(), getValueType(), ExecType.MR);
    agg1.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    // aggregation uses kahanSum
    agg1.setupCorrectionLocation(CorrectionLocationType.NONE);
    setLineNumbers(agg1);
    setLops(agg1);
}

Example 54 with Group

use of org.apache.sysml.lops.Group in project 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 55 with Group

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

the class BinaryOp method constructAppendLopChain.

/**
 * Special case ternary append. Here, we also compile a MR_RAPPEND or MR_GAPPEND
 *
 * @param left ?
 * @param right1 ?
 * @param right2 ?
 * @param dt ?
 * @param vt ?
 * @param cbind ?
 * @param current ?
 * @return low-level operator
 */
public static Lop constructAppendLopChain(Hop left, Hop right1, Hop right2, DataType dt, ValueType vt, boolean cbind, Hop current) {
    long m1_dim1 = left.getDim1();
    long m1_dim2 = left.getDim2();
    long m2_dim1 = right1.getDim1();
    long m2_dim2 = right1.getDim2();
    long m3_dim1 = right2.getDim1();
    long m3_dim2 = right2.getDim2();
    // output cols
    long m41_dim2 = (m1_dim2 >= 0 && m2_dim2 >= 0) ? (m1_dim2 + m2_dim2) : -1;
    long m41_nnz = (left.getNnz() > 0 && right1.getNnz() > 0) ? (left.getNnz() + right1.getNnz()) : // output nnz
    -1;
    // output cols
    long m42_dim2 = (m1_dim2 >= 0 && m2_dim2 >= 0 && m3_dim2 >= 0) ? (m1_dim2 + m2_dim2 + m3_dim2) : -1;
    long m42_nnz = (left.getNnz() > 0 && right1.getNnz() > 0 && right2.getNnz() > 0) ? (left.getNnz() + right1.getNnz() + right2.getNnz()) : // output nnz
    -1;
    long brlen = left.getRowsInBlock();
    long bclen = left.getColsInBlock();
    // warn if assumption of blocksize>=3 does not hold
    if (bclen < 3)
        throw new HopsException("MR_RAPPEND requires a blocksize of >= 3.");
    // case MR_RAPPEND:
    // special case reduce append w/ one column block
    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(right1.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, vt);
    group1.getOutputParameters().setDimensions(m2_dim1, m2_dim2, brlen, bclen, right1.getNnz());
    group1.setAllPositions(right1.getFilename(), right1.getBeginLine(), right1.getBeginColumn(), right1.getEndLine(), right1.getEndColumn());
    Group group3 = new Group(right2.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, vt);
    group1.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, right2.getNnz());
    group1.setAllPositions(right2.getFilename(), right2.getBeginLine(), right2.getBeginColumn(), right2.getEndLine(), right2.getEndColumn());
    AppendR appR1 = new AppendR(group1, group2, dt, vt, cbind, ExecType.MR);
    appR1.getOutputParameters().setDimensions(m1_dim1, m41_dim2, brlen, bclen, m41_nnz);
    appR1.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
    AppendR appR2 = new AppendR(appR1, group3, dt, vt, cbind, ExecType.MR);
    appR1.getOutputParameters().setDimensions(m1_dim1, m42_dim2, brlen, bclen, m42_nnz);
    appR1.setAllPositions(current.getFilename(), current.getBeginLine(), current.getBeginColumn(), current.getEndLine(), current.getEndColumn());
    return appR2;
}