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

Example 61 with Lop

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

the class AggBinaryOp method constructSparkLopsCPMMWithLeftTransposeRewrite.

private Lop constructSparkLopsCPMMWithLeftTransposeRewrite() {
    SparkAggType aggtype = getSparkMMAggregationType(true);
    // 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 multiply
    MMCJ mmcj = new MMCJ(tY, X.constructLops(), getDataType(), getValueType(), aggtype, ExecType.SPARK);
    mmcj.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    setLineNumbers(mmcj);
    // result transpose CP
    Lop out = new Transform(mmcj, OperationTypes.Transpose, getDataType(), getValueType(), ExecType.CP);
    out.getOutputParameters().setDimensions(X.getDim2(), Y.getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    return out;
}

Example 62 with Lop

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

the class AggBinaryOp method constructCPLopsMM.

private void constructCPLopsMM(ExecType et) {
    Lop matmultCP = null;
    if (et == ExecType.GPU) {
        Hop h1 = getInput().get(0);
        Hop h2 = getInput().get(1);
        // Since GPU backend is in experimental mode, rewrite optimization can be skipped.
        // CuSPARSE's cusparsecsrmm2 fails with only following parameters, but passes for all other settings:
        // transa=1 transb=1 m=300 n=300 k=300 ldb=300 ldc=300
        // Hence, we disable hope rewrite optimization.
        // HopRewriteUtils.isTransposeOperation(h1);
        boolean leftTrans = false;
        // HopRewriteUtils.isTransposeOperation(h2);
        boolean rightTrans = false;
        Lop left = !leftTrans ? h1.constructLops() : h1.getInput().get(0).constructLops();
        Lop right = !rightTrans ? h2.constructLops() : h2.getInput().get(0).constructLops();
        matmultCP = new Binary(left, right, Binary.OperationTypes.MATMULT, getDataType(), getValueType(), et, leftTrans, rightTrans);
        setOutputDimensions(matmultCP);
    } else {
        if (isLeftTransposeRewriteApplicable(true, false)) {
            matmultCP = constructCPLopsMMWithLeftTransposeRewrite();
        } else {
            int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
            matmultCP = new Binary(getInput().get(0).constructLops(), getInput().get(1).constructLops(), Binary.OperationTypes.MATMULT, getDataType(), getValueType(), et, k);
        }
        setOutputDimensions(matmultCP);
    }
    setLineNumbers(matmultCP);
    setLops(matmultCP);
}

Example 63 with Lop

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

the class AggBinaryOp method constructSparkLopsCPMM.

private void constructSparkLopsCPMM() {
    if (isLeftTransposeRewriteApplicable(false, false)) {
        setLops(constructSparkLopsCPMMWithLeftTransposeRewrite());
    } else {
        SparkAggType aggtype = getSparkMMAggregationType(true);
        Lop cpmm = new MMCJ(getInput().get(0).constructLops(), getInput().get(1).constructLops(), getDataType(), getValueType(), aggtype, ExecType.SPARK);
        setOutputDimensions(cpmm);
        setLineNumbers(cpmm);
        setLops(cpmm);
    }
}

Example 64 with Lop

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

the class AggBinaryOp method constructCPLopsTSMM.

// ////////////////////////
// CP Lops generation
// ///////////////////////
private void constructCPLopsTSMM(MMTSJType mmtsj, ExecType et) {
    int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
    Lop matmultCP = new MMTSJ(getInput().get(mmtsj.isLeft() ? 1 : 0).constructLops(), getDataType(), getValueType(), et, mmtsj, false, k);
    matmultCP.getOutputParameters().setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    setLineNumbers(matmultCP);
    setLops(matmultCP);
}

Example 65 with Lop

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

the class AggBinaryOp method constructMRLopsCPMMWithLeftTransposeRewrite.

private Lop constructMRLopsCPMMWithLeftTransposeRewrite() {
    // 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 multiply
    MMCJType type = getMMCJAggregationType(X, Y);
    MMCJ mmcj = new MMCJ(tY, X.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);
    // result transpose CP
    Lop out = new Transform(agg1, OperationTypes.Transpose, getDataType(), getValueType(), ExecType.CP);
    out.getOutputParameters().setDimensions(X.getDim2(), Y.getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
    return out;
}