Examples with SpoofRowwise org.apache.sysml.runtime.codegen.SpoofRowwise used on opensource projects

Example 1 with SpoofRowwise

use of org.apache.sysml.runtime.codegen.SpoofRowwise in project incubator-systemml by apache.

the class SpoofSPInstruction method updateOutputMatrixCharacteristics.

private void updateOutputMatrixCharacteristics(SparkExecutionContext sec, SpoofOperator op) {
    if (op instanceof SpoofCellwise) {
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(_in[0].getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
        if (((SpoofCellwise) op).getCellType() == CellType.ROW_AGG)
            mcOut.set(mcIn.getRows(), 1, mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
        else if (((SpoofCellwise) op).getCellType() == CellType.NO_AGG)
            mcOut.set(mcIn);
    } else if (op instanceof SpoofOuterProduct) {
        // X
        MatrixCharacteristics mcIn1 = sec.getMatrixCharacteristics(_in[0].getName());
        // U
        MatrixCharacteristics mcIn2 = sec.getMatrixCharacteristics(_in[1].getName());
        // V
        MatrixCharacteristics mcIn3 = sec.getMatrixCharacteristics(_in[2].getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
        OutProdType type = ((SpoofOuterProduct) op).getOuterProdType();
        if (type == OutProdType.CELLWISE_OUTER_PRODUCT)
            mcOut.set(mcIn1.getRows(), mcIn1.getCols(), mcIn1.getRowsPerBlock(), mcIn1.getColsPerBlock());
        else if (type == OutProdType.LEFT_OUTER_PRODUCT)
            mcOut.set(mcIn3.getRows(), mcIn3.getCols(), mcIn3.getRowsPerBlock(), mcIn3.getColsPerBlock());
        else if (type == OutProdType.RIGHT_OUTER_PRODUCT)
            mcOut.set(mcIn2.getRows(), mcIn2.getCols(), mcIn2.getRowsPerBlock(), mcIn2.getColsPerBlock());
    } else if (op instanceof SpoofRowwise) {
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(_in[0].getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
        RowType type = ((SpoofRowwise) op).getRowType();
        if (type == RowType.NO_AGG)
            mcOut.set(mcIn);
        else if (type == RowType.ROW_AGG)
            mcOut.set(mcIn.getRows(), 1, mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
        else if (type == RowType.COL_AGG)
            mcOut.set(1, mcIn.getCols(), mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
        else if (type == RowType.COL_AGG_T)
            mcOut.set(mcIn.getCols(), 1, mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
    }
}

Example 2 with SpoofRowwise

use of org.apache.sysml.runtime.codegen.SpoofRowwise in project systemml by apache.

the class SpoofSPInstruction method updateOutputMatrixCharacteristics.

private void updateOutputMatrixCharacteristics(SparkExecutionContext sec, SpoofOperator op) {
    if (op instanceof SpoofCellwise) {
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(_in[0].getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
        if (((SpoofCellwise) op).getCellType() == CellType.ROW_AGG)
            mcOut.set(mcIn.getRows(), 1, mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
        else if (((SpoofCellwise) op).getCellType() == CellType.NO_AGG)
            mcOut.set(mcIn);
    } else if (op instanceof SpoofOuterProduct) {
        // X
        MatrixCharacteristics mcIn1 = sec.getMatrixCharacteristics(_in[0].getName());
        // U
        MatrixCharacteristics mcIn2 = sec.getMatrixCharacteristics(_in[1].getName());
        // V
        MatrixCharacteristics mcIn3 = sec.getMatrixCharacteristics(_in[2].getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
        OutProdType type = ((SpoofOuterProduct) op).getOuterProdType();
        if (type == OutProdType.CELLWISE_OUTER_PRODUCT)
            mcOut.set(mcIn1.getRows(), mcIn1.getCols(), mcIn1.getRowsPerBlock(), mcIn1.getColsPerBlock());
        else if (type == OutProdType.LEFT_OUTER_PRODUCT)
            mcOut.set(mcIn3.getRows(), mcIn3.getCols(), mcIn3.getRowsPerBlock(), mcIn3.getColsPerBlock());
        else if (type == OutProdType.RIGHT_OUTER_PRODUCT)
            mcOut.set(mcIn2.getRows(), mcIn2.getCols(), mcIn2.getRowsPerBlock(), mcIn2.getColsPerBlock());
    } else if (op instanceof SpoofRowwise) {
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(_in[0].getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
        RowType type = ((SpoofRowwise) op).getRowType();
        if (type == RowType.NO_AGG)
            mcOut.set(mcIn);
        else if (type == RowType.ROW_AGG)
            mcOut.set(mcIn.getRows(), 1, mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
        else if (type == RowType.COL_AGG)
            mcOut.set(1, mcIn.getCols(), mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
        else if (type == RowType.COL_AGG_T)
            mcOut.set(mcIn.getCols(), 1, mcIn.getRowsPerBlock(), mcIn.getColsPerBlock());
    }
}

Example 3 with SpoofRowwise

use of org.apache.sysml.runtime.codegen.SpoofRowwise in project incubator-systemml by apache.

the class SpoofSPInstruction method processInstruction.

@Override
public void processInstruction(ExecutionContext ec) {
    SparkExecutionContext sec = (SparkExecutionContext) ec;
    // decide upon broadcast side inputs
    boolean[] bcVect = determineBroadcastInputs(sec, _in);
    boolean[] bcVect2 = getMatrixBroadcastVector(sec, _in, bcVect);
    int main = getMainInputIndex(_in, bcVect);
    // create joined input rdd w/ replication if needed
    MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(_in[main].getName());
    JavaPairRDD<MatrixIndexes, MatrixBlock[]> in = createJoinedInputRDD(sec, _in, bcVect, (_class.getSuperclass() == SpoofOuterProduct.class));
    JavaPairRDD<MatrixIndexes, MatrixBlock> out = null;
    // create lists of input broadcasts and scalars
    ArrayList<PartitionedBroadcast<MatrixBlock>> bcMatrices = new ArrayList<>();
    ArrayList<ScalarObject> scalars = new ArrayList<>();
    for (int i = 0; i < _in.length; i++) {
        if (_in[i].getDataType() == DataType.MATRIX && bcVect[i]) {
            bcMatrices.add(sec.getBroadcastForVariable(_in[i].getName()));
        } else if (_in[i].getDataType() == DataType.SCALAR) {
            // note: even if literal, it might be compiled as scalar placeholder
            scalars.add(sec.getScalarInput(_in[i].getName(), _in[i].getValueType(), _in[i].isLiteral()));
        }
    }
    // execute generated operator
    if (// CELL
    _class.getSuperclass() == SpoofCellwise.class) {
        SpoofCellwise op = (SpoofCellwise) CodegenUtils.createInstance(_class);
        AggregateOperator aggop = getAggregateOperator(op.getAggOp());
        if (_out.getDataType() == DataType.MATRIX) {
            // execute codegen block operation
            out = in.mapPartitionsToPair(new CellwiseFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            if ((op.getCellType() == CellType.ROW_AGG && mcIn.getCols() > mcIn.getColsPerBlock()) || (op.getCellType() == CellType.COL_AGG && mcIn.getRows() > mcIn.getRowsPerBlock())) {
                long numBlocks = (op.getCellType() == CellType.ROW_AGG) ? mcIn.getNumRowBlocks() : mcIn.getNumColBlocks();
                out = RDDAggregateUtils.aggByKeyStable(out, aggop, (int) Math.min(out.getNumPartitions(), numBlocks), false);
            }
            sec.setRDDHandleForVariable(_out.getName(), out);
            // maintain lineage info and output characteristics
            maintainLineageInfo(sec, _in, bcVect, _out);
            updateOutputMatrixCharacteristics(sec, op);
        } else {
            // SCALAR
            out = in.mapPartitionsToPair(new CellwiseFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            MatrixBlock tmpMB = RDDAggregateUtils.aggStable(out, aggop);
            sec.setVariable(_out.getName(), new DoubleObject(tmpMB.getValue(0, 0)));
        }
    } else if (// MAGG
    _class.getSuperclass() == SpoofMultiAggregate.class) {
        SpoofMultiAggregate op = (SpoofMultiAggregate) CodegenUtils.createInstance(_class);
        AggOp[] aggOps = op.getAggOps();
        MatrixBlock tmpMB = in.mapToPair(new MultiAggregateFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars)).values().fold(new MatrixBlock(), new MultiAggAggregateFunction(aggOps));
        sec.setMatrixOutput(_out.getName(), tmpMB, getExtendedOpcode());
    } else if (// OUTER
    _class.getSuperclass() == SpoofOuterProduct.class) {
        if (_out.getDataType() == DataType.MATRIX) {
            SpoofOperator op = (SpoofOperator) CodegenUtils.createInstance(_class);
            OutProdType type = ((SpoofOuterProduct) op).getOuterProdType();
            // update matrix characteristics
            updateOutputMatrixCharacteristics(sec, op);
            MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
            out = in.mapPartitionsToPair(new OuterProductFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            if (type == OutProdType.LEFT_OUTER_PRODUCT || type == OutProdType.RIGHT_OUTER_PRODUCT) {
                long numBlocks = mcOut.getNumRowBlocks() * mcOut.getNumColBlocks();
                out = RDDAggregateUtils.sumByKeyStable(out, (int) Math.min(out.getNumPartitions(), numBlocks), false);
            }
            sec.setRDDHandleForVariable(_out.getName(), out);
            // maintain lineage info and output characteristics
            maintainLineageInfo(sec, _in, bcVect, _out);
        } else {
            out = in.mapPartitionsToPair(new OuterProductFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            MatrixBlock tmp = RDDAggregateUtils.sumStable(out);
            sec.setVariable(_out.getName(), new DoubleObject(tmp.getValue(0, 0)));
        }
    } else if (_class.getSuperclass() == SpoofRowwise.class) {
        // ROW
        if (mcIn.getCols() > mcIn.getColsPerBlock()) {
            throw new DMLRuntimeException("Invalid spark rowwise operator w/ ncol=" + mcIn.getCols() + ", ncolpb=" + mcIn.getColsPerBlock() + ".");
        }
        SpoofRowwise op = (SpoofRowwise) CodegenUtils.createInstance(_class);
        long clen2 = op.getRowType().isConstDim2(op.getConstDim2()) ? op.getConstDim2() : op.getRowType().isRowTypeB1() ? sec.getMatrixCharacteristics(_in[1].getName()).getCols() : -1;
        RowwiseFunction fmmc = new RowwiseFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars, (int) mcIn.getCols(), (int) clen2);
        out = in.mapPartitionsToPair(fmmc, op.getRowType() == RowType.ROW_AGG || op.getRowType() == RowType.NO_AGG);
        if (op.getRowType().isColumnAgg() || op.getRowType() == RowType.FULL_AGG) {
            MatrixBlock tmpMB = RDDAggregateUtils.sumStable(out);
            if (op.getRowType().isColumnAgg())
                sec.setMatrixOutput(_out.getName(), tmpMB, getExtendedOpcode());
            else
                sec.setScalarOutput(_out.getName(), new DoubleObject(tmpMB.quickGetValue(0, 0)));
        } else // row-agg or no-agg
        {
            if (op.getRowType() == RowType.ROW_AGG && mcIn.getCols() > mcIn.getColsPerBlock()) {
                out = RDDAggregateUtils.sumByKeyStable(out, (int) Math.min(out.getNumPartitions(), mcIn.getNumRowBlocks()), false);
            }
            sec.setRDDHandleForVariable(_out.getName(), out);
            // maintain lineage info and output characteristics
            maintainLineageInfo(sec, _in, bcVect, _out);
            updateOutputMatrixCharacteristics(sec, op);
        }
    } else {
        throw new DMLRuntimeException("Operator " + _class.getSuperclass() + " is not supported on Spark");
    }
}

Example 4 with SpoofRowwise

use of org.apache.sysml.runtime.codegen.SpoofRowwise in project systemml by apache.

the class SpoofSPInstruction method processInstruction.

@Override
public void processInstruction(ExecutionContext ec) {
    SparkExecutionContext sec = (SparkExecutionContext) ec;
    // decide upon broadcast side inputs
    boolean[] bcVect = determineBroadcastInputs(sec, _in);
    boolean[] bcVect2 = getMatrixBroadcastVector(sec, _in, bcVect);
    int main = getMainInputIndex(_in, bcVect);
    // create joined input rdd w/ replication if needed
    MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(_in[main].getName());
    JavaPairRDD<MatrixIndexes, MatrixBlock[]> in = createJoinedInputRDD(sec, _in, bcVect, (_class.getSuperclass() == SpoofOuterProduct.class));
    JavaPairRDD<MatrixIndexes, MatrixBlock> out = null;
    // create lists of input broadcasts and scalars
    ArrayList<PartitionedBroadcast<MatrixBlock>> bcMatrices = new ArrayList<>();
    ArrayList<ScalarObject> scalars = new ArrayList<>();
    for (int i = 0; i < _in.length; i++) {
        if (_in[i].getDataType() == DataType.MATRIX && bcVect[i]) {
            bcMatrices.add(sec.getBroadcastForVariable(_in[i].getName()));
        } else if (_in[i].getDataType() == DataType.SCALAR) {
            // note: even if literal, it might be compiled as scalar placeholder
            scalars.add(sec.getScalarInput(_in[i].getName(), _in[i].getValueType(), _in[i].isLiteral()));
        }
    }
    // execute generated operator
    if (// CELL
    _class.getSuperclass() == SpoofCellwise.class) {
        SpoofCellwise op = (SpoofCellwise) CodegenUtils.createInstance(_class);
        AggregateOperator aggop = getAggregateOperator(op.getAggOp());
        if (_out.getDataType() == DataType.MATRIX) {
            // execute codegen block operation
            out = in.mapPartitionsToPair(new CellwiseFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            if ((op.getCellType() == CellType.ROW_AGG && mcIn.getCols() > mcIn.getColsPerBlock()) || (op.getCellType() == CellType.COL_AGG && mcIn.getRows() > mcIn.getRowsPerBlock())) {
                long numBlocks = (op.getCellType() == CellType.ROW_AGG) ? mcIn.getNumRowBlocks() : mcIn.getNumColBlocks();
                out = RDDAggregateUtils.aggByKeyStable(out, aggop, (int) Math.min(out.getNumPartitions(), numBlocks), false);
            }
            sec.setRDDHandleForVariable(_out.getName(), out);
            // maintain lineage info and output characteristics
            maintainLineageInfo(sec, _in, bcVect, _out);
            updateOutputMatrixCharacteristics(sec, op);
        } else {
            // SCALAR
            out = in.mapPartitionsToPair(new CellwiseFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            MatrixBlock tmpMB = RDDAggregateUtils.aggStable(out, aggop);
            sec.setVariable(_out.getName(), new DoubleObject(tmpMB.getValue(0, 0)));
        }
    } else if (// MAGG
    _class.getSuperclass() == SpoofMultiAggregate.class) {
        SpoofMultiAggregate op = (SpoofMultiAggregate) CodegenUtils.createInstance(_class);
        AggOp[] aggOps = op.getAggOps();
        MatrixBlock tmpMB = in.mapToPair(new MultiAggregateFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars)).values().fold(new MatrixBlock(), new MultiAggAggregateFunction(aggOps));
        sec.setMatrixOutput(_out.getName(), tmpMB, getExtendedOpcode());
    } else if (// OUTER
    _class.getSuperclass() == SpoofOuterProduct.class) {
        if (_out.getDataType() == DataType.MATRIX) {
            SpoofOperator op = (SpoofOperator) CodegenUtils.createInstance(_class);
            OutProdType type = ((SpoofOuterProduct) op).getOuterProdType();
            // update matrix characteristics
            updateOutputMatrixCharacteristics(sec, op);
            MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(_out.getName());
            out = in.mapPartitionsToPair(new OuterProductFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            if (type == OutProdType.LEFT_OUTER_PRODUCT || type == OutProdType.RIGHT_OUTER_PRODUCT) {
                long numBlocks = mcOut.getNumRowBlocks() * mcOut.getNumColBlocks();
                out = RDDAggregateUtils.sumByKeyStable(out, (int) Math.min(out.getNumPartitions(), numBlocks), false);
            }
            sec.setRDDHandleForVariable(_out.getName(), out);
            // maintain lineage info and output characteristics
            maintainLineageInfo(sec, _in, bcVect, _out);
        } else {
            out = in.mapPartitionsToPair(new OuterProductFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars), true);
            MatrixBlock tmp = RDDAggregateUtils.sumStable(out);
            sec.setVariable(_out.getName(), new DoubleObject(tmp.getValue(0, 0)));
        }
    } else if (_class.getSuperclass() == SpoofRowwise.class) {
        // ROW
        if (mcIn.getCols() > mcIn.getColsPerBlock()) {
            throw new DMLRuntimeException("Invalid spark rowwise operator w/ ncol=" + mcIn.getCols() + ", ncolpb=" + mcIn.getColsPerBlock() + ".");
        }
        SpoofRowwise op = (SpoofRowwise) CodegenUtils.createInstance(_class);
        long clen2 = op.getRowType().isConstDim2(op.getConstDim2()) ? op.getConstDim2() : op.getRowType().isRowTypeB1() ? sec.getMatrixCharacteristics(_in[1].getName()).getCols() : -1;
        RowwiseFunction fmmc = new RowwiseFunction(_class.getName(), _classBytes, bcVect2, bcMatrices, scalars, (int) mcIn.getCols(), (int) clen2);
        out = in.mapPartitionsToPair(fmmc, op.getRowType() == RowType.ROW_AGG || op.getRowType() == RowType.NO_AGG);
        if (op.getRowType().isColumnAgg() || op.getRowType() == RowType.FULL_AGG) {
            MatrixBlock tmpMB = RDDAggregateUtils.sumStable(out);
            if (op.getRowType().isColumnAgg())
                sec.setMatrixOutput(_out.getName(), tmpMB, getExtendedOpcode());
            else
                sec.setScalarOutput(_out.getName(), new DoubleObject(tmpMB.quickGetValue(0, 0)));
        } else // row-agg or no-agg
        {
            if (op.getRowType() == RowType.ROW_AGG && mcIn.getCols() > mcIn.getColsPerBlock()) {
                out = RDDAggregateUtils.sumByKeyStable(out, (int) Math.min(out.getNumPartitions(), mcIn.getNumRowBlocks()), false);
            }
            sec.setRDDHandleForVariable(_out.getName(), out);
            // maintain lineage info and output characteristics
            maintainLineageInfo(sec, _in, bcVect, _out);
            updateOutputMatrixCharacteristics(sec, op);
        }
    } else {
        throw new DMLRuntimeException("Operator " + _class.getSuperclass() + " is not supported on Spark");
    }
}