Examples with MatrixCharacteristics org.apache.sysml.runtime.matrix.MatrixCharacteristics used on opensource projects

Example 31 with MatrixCharacteristics

use of org.apache.sysml.runtime.matrix.MatrixCharacteristics in project incubator-systemml by apache.

the class QuaternaryOp method inferOutputCharacteristics.

@Override
protected long[] inferOutputCharacteristics(MemoTable memo) {
    long[] ret = null;
    switch(_op) {
        case // always scalar output
        WSLOSS:
            ret = null;
            break;
        case WSIGMOID:
        case WUMM:
            {
                MatrixCharacteristics mcW = memo.getAllInputStats(getInput().get(0));
                ret = new long[] { mcW.getRows(), mcW.getCols(), mcW.getNonZeros() };
                break;
            }
        case WDIVMM:
            {
                if (_baseType == 0) {
                    // basic
                    MatrixCharacteristics mcW = memo.getAllInputStats(getInput().get(0));
                    ret = new long[] { mcW.getRows(), mcW.getCols(), mcW.getNonZeros() };
                } else if (_baseType == 1 || _baseType == 3) {
                    // left (w/ transpose or w/ epsilon)
                    MatrixCharacteristics mcV = memo.getAllInputStats(getInput().get(2));
                    ret = new long[] { mcV.getRows(), mcV.getCols(), -1 };
                } else {
                    // right
                    MatrixCharacteristics mcU = memo.getAllInputStats(getInput().get(1));
                    ret = new long[] { mcU.getRows(), mcU.getCols(), -1 };
                }
                break;
            }
        default:
            throw new RuntimeException("Memory for operation (" + _op + ") can not be estimated.");
    }
    return ret;
}

Example 32 with MatrixCharacteristics

use of org.apache.sysml.runtime.matrix.MatrixCharacteristics in project incubator-systemml by apache.

the class TernaryOp method inferOutputCharacteristics.

@Override
protected long[] inferOutputCharacteristics(MemoTable memo) {
    long[] ret = null;
    MatrixCharacteristics[] mc = memo.getAllInputStats(getInput());
    switch(_op) {
        case CTABLE:
            boolean dimsSpec = (getInput().size() > 3);
            // Step 1: general dimension info inputs
            long worstCaseDim = -1;
            // since the dimensions of both inputs must be the same, checking for one input is sufficient
            if (mc[0].dimsKnown() || mc[1].dimsKnown()) {
                // Output dimensions are completely data dependent. In the worst case,
                // #categories in each attribute = #rows (e.g., an ID column, say EmployeeID).
                // both inputs are one-dimensional matrices with exact same dimensions, m = size of longer dimension
                worstCaseDim = (mc[0].dimsKnown()) ? (mc[0].getRows() > 1 ? mc[0].getRows() : mc[0].getCols()) : (mc[1].getRows() > 1 ? mc[1].getRows() : mc[1].getCols());
            // note: for ctable histogram dim2 known but automatically replaces m
            // ret = new long[]{m, m, m};
            }
            // Step 2: special handling specified dims
            if (dimsSpec && getInput().get(3) instanceof LiteralOp && getInput().get(4) instanceof LiteralOp) {
                long outputDim1 = HopRewriteUtils.getIntValueSafe((LiteralOp) getInput().get(3));
                long outputDim2 = HopRewriteUtils.getIntValueSafe((LiteralOp) getInput().get(4));
                long outputNNZ = (outputDim1 * outputDim2 > outputDim1 ? outputDim1 : outputDim1 * outputDim2);
                _dim1 = outputDim1;
                _dim2 = outputDim2;
                return new long[] { outputDim1, outputDim2, outputNNZ };
            }
            // note: for ctable histogram dim2 known but automatically replaces m
            return new long[] { worstCaseDim, worstCaseDim, worstCaseDim };
        case QUANTILE:
            if (mc[2].dimsKnown())
                return new long[] { mc[2].getRows(), 1, mc[2].getRows() };
            break;
        case IFELSE:
            for (MatrixCharacteristics lmc : mc) if (// known matrix
            lmc.dimsKnown() && lmc.getRows() >= 0)
                return new long[] { lmc.getRows(), lmc.getCols(), -1 };
            break;
        case PLUS_MULT:
        case MINUS_MULT:
            // compute back NNz
            double sp1 = OptimizerUtils.getSparsity(mc[0].getRows(), mc[0].getRows(), mc[0].getNonZeros());
            double sp2 = OptimizerUtils.getSparsity(mc[2].getRows(), mc[2].getRows(), mc[2].getNonZeros());
            return new long[] { mc[0].getRows(), mc[0].getCols(), (long) Math.min(sp1 + sp2, 1) };
        default:
            throw new RuntimeException("Memory for operation (" + _op + ") can not be estimated.");
    }
    return ret;
}

Example 33 with MatrixCharacteristics

use of org.apache.sysml.runtime.matrix.MatrixCharacteristics in project incubator-systemml by apache.

the class SpoofFusedOp method inferOutputCharacteristics.

@Override
protected long[] inferOutputCharacteristics(MemoTable memo) {
    long[] ret = null;
    // get statistics of main input
    MatrixCharacteristics mc = memo.getAllInputStats(getInput().get(0));
    if (mc.dimsKnown()) {
        switch(_dimsType) {
            case ROW_DIMS:
                ret = new long[] { mc.getRows(), 1, -1 };
                break;
            case COLUMN_DIMS_ROWS:
                ret = new long[] { mc.getCols(), 1, -1 };
                break;
            case COLUMN_DIMS_COLS:
                ret = new long[] { 1, mc.getCols(), -1 };
                break;
            case RANK_DIMS_COLS:
                {
                    MatrixCharacteristics mc2 = memo.getAllInputStats(getInput().get(1));
                    if (mc2.dimsKnown())
                        ret = new long[] { 1, mc2.getCols(), -1 };
                    break;
                }
            case INPUT_DIMS:
                ret = new long[] { mc.getRows(), mc.getCols(), -1 };
                break;
            case INPUT_DIMS_CONST2:
                ret = new long[] { mc.getRows(), _constDim2, -1 };
                break;
            case VECT_CONST2:
                ret = new long[] { 1, _constDim2, -1 };
                break;
            case SCALAR:
                ret = new long[] { 0, 0, -1 };
                break;
            case MULTI_SCALAR:
                // dim2 statically set from outside
                ret = new long[] { 1, _dim2, -1 };
                break;
            case ROW_RANK_DIMS:
                {
                    MatrixCharacteristics mc2 = memo.getAllInputStats(getInput().get(1));
                    if (mc2.dimsKnown())
                        ret = new long[] { mc.getRows(), mc2.getCols(), -1 };
                    break;
                }
            case COLUMN_RANK_DIMS:
                {
                    MatrixCharacteristics mc2 = memo.getAllInputStats(getInput().get(1));
                    if (mc2.dimsKnown())
                        ret = new long[] { mc.getCols(), mc2.getCols(), -1 };
                    break;
                }
            case COLUMN_RANK_DIMS_T:
                {
                    MatrixCharacteristics mc2 = memo.getAllInputStats(getInput().get(1));
                    if (mc2.dimsKnown())
                        ret = new long[] { mc2.getCols(), mc.getCols(), -1 };
                    break;
                }
            default:
                throw new RuntimeException("Failed to infer worst-case size information " + "for type: " + _dimsType.toString());
        }
    }
    return ret;
}

Example 34 with MatrixCharacteristics

use of org.apache.sysml.runtime.matrix.MatrixCharacteristics in project incubator-systemml by apache.

the class AggUnaryOp method inferOutputCharacteristics.

@Override
protected long[] inferOutputCharacteristics(MemoTable memo) {
    long[] ret = null;
    Hop input = getInput().get(0);
    MatrixCharacteristics mc = memo.getAllInputStats(input);
    if (_direction == Direction.Col && mc.colsKnown())
        ret = new long[] { 1, mc.getCols(), -1 };
    else if (_direction == Direction.Row && mc.rowsKnown())
        ret = new long[] { mc.getRows(), 1, -1 };
    return ret;
}

Example 35 with MatrixCharacteristics

use of org.apache.sysml.runtime.matrix.MatrixCharacteristics in project incubator-systemml by apache.

the class CumulativeAggregateSPInstruction method processInstruction.

@Override
public void processInstruction(ExecutionContext ec) {
    SparkExecutionContext sec = (SparkExecutionContext) ec;
    MatrixCharacteristics mc = sec.getMatrixCharacteristics(input1.getName());
    long rlen = mc.getRows();
    int brlen = mc.getRowsPerBlock();
    int bclen = mc.getColsPerBlock();
    // get input
    JavaPairRDD<MatrixIndexes, MatrixBlock> in = sec.getBinaryBlockRDDHandleForVariable(input1.getName());
    // execute unary aggregate (w/ implicit drop correction)
    AggregateUnaryOperator auop = (AggregateUnaryOperator) _optr;
    JavaPairRDD<MatrixIndexes, MatrixBlock> out = in.mapToPair(new RDDCumAggFunction(auop, rlen, brlen, bclen));
    out = RDDAggregateUtils.mergeByKey(out, false);
    // put output handle in symbol table
    sec.setRDDHandleForVariable(output.getName(), out);
    sec.addLineageRDD(output.getName(), input1.getName());
}