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

Example 76 with Lop

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

the class QuaternaryOp method constructMRLopsWeightedSigmoid.

private void constructMRLopsWeightedSigmoid(WSigmoidType wtype) {
    // NOTE: the common case for wsigmoid 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 Sigmoid only if this constraint holds.
    Hop X = getInput().get(0);
    Hop U = getInput().get(1);
    Hop V = getInput().get(2);
    // 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 isMapWsig = (m1Size + m2Size < OptimizerUtils.getRemoteMemBudgetMap(true));
    if (// broadcast
    !FORCE_REPLICATION && isMapWsig) {
        // 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 wsig always with broadcast
        Lop wsigmoid = new WeightedSigmoid(X.constructLops(), lU, lV, DataType.MATRIX, ValueType.DOUBLE, wtype, ExecType.MR);
        setOutputDimensions(wsigmoid);
        setLineNumbers(wsigmoid);
        setLops(wsigmoid);
    // in contrast to wsloss no aggregation required
    } 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(), X.getNnz());
        setLineNumbers(grpX);
        Lop lU = constructLeftFactorMRLop(U, V, cacheU, m1Size);
        Lop lV = constructRightFactorMRLop(U, V, cacheV, m2Size);
        // reduce-side wsig w/ or without broadcast
        Lop wsigmoid = new WeightedSigmoidR(grpX, lU, lV, DataType.MATRIX, ValueType.DOUBLE, wtype, cacheU, cacheV, ExecType.MR);
        setOutputDimensions(wsigmoid);
        setLineNumbers(wsigmoid);
        setLops(wsigmoid);
    // in contrast to wsloss no aggregation required
    }
}

Example 77 with Lop

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

the class QuaternaryOp method constructMRLopsWeightedUMM.

private void constructMRLopsWeightedUMM(WUMMType wtype) {
    // NOTE: the common case for wumm 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 UnaryMM  only if this constraint holds.
    Unary.OperationTypes uop = _uop != null ? HopsOpOp1LopsU.get(_uop) : _sop == OpOp2.POW ? Unary.OperationTypes.POW2 : Unary.OperationTypes.MULTIPLY2;
    Hop X = getInput().get(0);
    Hop U = getInput().get(1);
    Hop V = getInput().get(2);
    // 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 isMapWumm = (m1Size + m2Size < OptimizerUtils.getRemoteMemBudgetMap(true));
    if (// broadcast
    !FORCE_REPLICATION && isMapWumm) {
        // 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 wumm always with broadcast
        Lop wumm = new WeightedUnaryMM(X.constructLops(), lU, lV, DataType.MATRIX, ValueType.DOUBLE, wtype, uop, ExecType.MR);
        setOutputDimensions(wumm);
        setLineNumbers(wumm);
        setLops(wumm);
    // in contrast to wsloss no aggregation required
    } 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(), X.getNnz());
        setLineNumbers(grpX);
        Lop lU = constructLeftFactorMRLop(U, V, cacheU, m1Size);
        Lop lV = constructRightFactorMRLop(U, V, cacheV, m2Size);
        // reduce-side wumm w/ or without broadcast
        Lop wumm = new WeightedUnaryMMR(grpX, lU, lV, DataType.MATRIX, ValueType.DOUBLE, wtype, uop, cacheU, cacheV, ExecType.MR);
        setOutputDimensions(wumm);
        setLineNumbers(wumm);
        setLops(wumm);
    // in contrast to wsloss no aggregation required
    }
}

Example 78 with Lop

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

the class QuaternaryOp method constructSparkLopsWeightedSquaredLoss.

private void constructSparkLopsWeightedSquaredLoss(WeightsType wtype) {
    // NOTE: the common case for wsloss 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 Squared Loss only if this constraint holds.
    // Notes: Any broadcast needs to fit twice in local memory because we partition the input in cp,
    // and needs to fit once in executor broadcast memory. The 2GB broadcast constraint is no longer
    // required because the max_int byte buffer constraint has been fixed in Spark 1.4
    double memBudgetExec = SparkExecutionContext.getBroadcastMemoryBudget();
    double memBudgetLocal = OptimizerUtils.getLocalMemBudget();
    Hop X = getInput().get(0);
    Hop U = getInput().get(1);
    Hop V = getInput().get(2);
    Hop W = 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 isMapWsloss = (!wtype.hasFourInputs() && m1Size + m2Size < memBudgetExec && 2 * m1Size < memBudgetLocal && 2 * m2Size < memBudgetLocal);
    if (// broadcast
    !FORCE_REPLICATION && isMapWsloss) {
        // map-side wsloss always with broadcast
        Lop wsloss = new WeightedSquaredLoss(X.constructLops(), U.constructLops(), V.constructLops(), W.constructLops(), DataType.SCALAR, ValueType.DOUBLE, wtype, ExecType.SPARK);
        setOutputDimensions(wsloss);
        setLineNumbers(wsloss);
        setLops(wsloss);
    } else // general case
    {
        // MR operator selection part 2
        boolean cacheU = !FORCE_REPLICATION && (m1Size < memBudgetExec && 2 * m1Size < memBudgetLocal);
        boolean cacheV = !FORCE_REPLICATION && ((!cacheU && m2Size < memBudgetExec) || (cacheU && m1Size + m2Size < memBudgetExec)) && 2 * m2Size < memBudgetLocal;
        // reduce-side wsloss w/ or without broadcast
        Lop wsloss = new WeightedSquaredLossR(X.constructLops(), U.constructLops(), V.constructLops(), W.constructLops(), DataType.SCALAR, ValueType.DOUBLE, wtype, cacheU, cacheV, ExecType.SPARK);
        setOutputDimensions(wsloss);
        setLineNumbers(wsloss);
        setLops(wsloss);
    }
}

Example 79 with Lop

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

the class QuaternaryOp method constructSparkLopsWeightedDivMM.

private void constructSparkLopsWeightedDivMM(WDivMMType wtype) {
    // NOTE: the common case for wdivmm 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 DivMM only if this constraint holds.
    // Notes: Any broadcast needs to fit twice in local memory because we partition the input in cp,
    // and needs to fit once in executor broadcast memory. The 2GB broadcast constraint is no longer
    // required because the max_int byte buffer constraint has been fixed in Spark 1.4
    double memBudgetExec = SparkExecutionContext.getBroadcastMemoryBudget();
    double memBudgetLocal = OptimizerUtils.getLocalMemBudget();
    Hop W = getInput().get(0);
    Hop U = getInput().get(1);
    Hop V = getInput().get(2);
    Hop X = 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 isMapWdivmm = ((!wtype.hasFourInputs() || wtype.hasScalar()) && m1Size + m2Size < memBudgetExec && 2 * m1Size < memBudgetLocal && 2 * m2Size < memBudgetLocal);
    if (// broadcast
    !FORCE_REPLICATION && isMapWdivmm) {
        // map-side wdivmm always with broadcast
        Lop wdivmm = new WeightedDivMM(W.constructLops(), U.constructLops(), V.constructLops(), X.constructLops(), DataType.MATRIX, ValueType.DOUBLE, wtype, ExecType.SPARK);
        setOutputDimensions(wdivmm);
        setLineNumbers(wdivmm);
        setLops(wdivmm);
    } else // general case
    {
        // MR operator selection part 2
        boolean cacheU = !FORCE_REPLICATION && (m1Size < memBudgetExec && 2 * m1Size < memBudgetLocal);
        boolean cacheV = !FORCE_REPLICATION && ((!cacheU && m2Size < memBudgetExec) || (cacheU && m1Size + m2Size < memBudgetExec)) && 2 * m2Size < memBudgetLocal;
        // reduce-side wdivmm w/ or without broadcast
        Lop wdivmm = new WeightedDivMMR(W.constructLops(), U.constructLops(), V.constructLops(), X.constructLops(), DataType.MATRIX, ValueType.DOUBLE, wtype, cacheU, cacheV, ExecType.SPARK);
        setOutputDimensions(wdivmm);
        setLineNumbers(wdivmm);
        setLops(wdivmm);
    }
}

Example 80 with Lop

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

the class QuaternaryOp method constructLeftFactorMRLop.

private Lop constructLeftFactorMRLop(Hop U, Hop V, boolean cacheU, double m1Size) {
    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;
    }
    return lU;
}