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

Example 26 with IndexedMatrixValue

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

the class BinUaggChainInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    ArrayList<IndexedMatrixValue> blkList = cachedValues.get(input);
    if (blkList == null)
        return;
    for (IndexedMatrixValue imv : blkList) {
        if (imv == null)
            continue;
        MatrixIndexes inIx = imv.getIndexes();
        MatrixValue inVal = imv.getValue();
        // allocate space for the intermediate and output value
        IndexedMatrixValue iout = cachedValues.holdPlace(output, valueClass);
        MatrixIndexes outIx = iout.getIndexes();
        MatrixValue outVal = iout.getValue();
        // process instruction
        OperationsOnMatrixValues.performAggregateUnary(inIx, inVal, _tmpIx, _tmpVal, _uaggOp, blockRowFactor, blockColFactor);
        ((MatrixBlock) _tmpVal).dropLastRowsOrColumns(_uaggOp.aggOp.correctionLocation);
        OperationsOnMatrixValues.performBinaryIgnoreIndexes(inVal, _tmpVal, outVal, _bOp);
        outIx.setIndexes(inIx);
    }
}

Example 27 with IndexedMatrixValue

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

the class BinaryInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    IndexedMatrixValue in1 = cachedValues.getFirst(input1);
    IndexedMatrixValue in2 = cachedValues.getFirst(input2);
    if (in1 == null && in2 == null)
        return;
    // allocate space for the output value
    // try to avoid coping as much as possible
    IndexedMatrixValue out;
    if ((output != input1 && output != input2) || (output == input1 && in1 == null) || (output == input2 && in2 == null))
        out = cachedValues.holdPlace(output, valueClass);
    else
        out = tempValue;
    // if one of the inputs is null, then it is a all zero block
    MatrixIndexes finalIndexes = null;
    if (in1 == null) {
        in1 = zeroInput;
        in1.getValue().reset(in2.getValue().getNumRows(), in2.getValue().getNumColumns());
        finalIndexes = in2.getIndexes();
    } else
        finalIndexes = in1.getIndexes();
    if (in2 == null) {
        in2 = zeroInput;
        in2.getValue().reset(in1.getValue().getNumRows(), in1.getValue().getNumColumns());
    }
    // process instruction
    out.getIndexes().setIndexes(finalIndexes);
    OperationsOnMatrixValues.performBinaryIgnoreIndexes(in1.getValue(), in2.getValue(), out.getValue(), ((BinaryOperator) optr));
    // put the output value in the cache
    if (out == tempValue)
        cachedValues.add(output, out);
}

Example 28 with IndexedMatrixValue

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

the class CtableInstruction method processInstruction.

public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue zeroInput, HashMap<Byte, CTableMap> resultMaps, HashMap<Byte, MatrixBlock> resultBlocks, int blockRowFactor, int blockColFactor) {
    IndexedMatrixValue in1, in2, in3 = null;
    in1 = cachedValues.getFirst(input1);
    CTableMap ctableResult = null;
    MatrixBlock ctableResultBlock = null;
    if (knownOutputDims()) {
        if (resultBlocks != null) {
            ctableResultBlock = resultBlocks.get(output);
            if (ctableResultBlock == null) {
                // From MR, output of ctable is set to be sparse since it is built from a single input block.
                ctableResultBlock = new MatrixBlock((int) _outputDim1, (int) _outputDim2, true);
                resultBlocks.put(output, ctableResultBlock);
            }
        } else {
            throw new DMLRuntimeException("Unexpected error in processing table instruction.");
        }
    } else {
        // prepare aggregation maps
        ctableResult = resultMaps.get(output);
        if (ctableResult == null) {
            ctableResult = new CTableMap();
            resultMaps.put(output, ctableResult);
        }
    }
    // get inputs and process instruction
    switch(_op) {
        case CTABLE_TRANSFORM:
            {
                in2 = cachedValues.getFirst(input2);
                in3 = cachedValues.getFirst(input3);
                if (in1 == null || in2 == null || in3 == null)
                    return;
                OperationsOnMatrixValues.performCtable(in1.getIndexes(), in1.getValue(), in2.getIndexes(), in2.getValue(), in3.getIndexes(), in3.getValue(), ctableResult, ctableResultBlock, optr);
                break;
            }
        case CTABLE_TRANSFORM_SCALAR_WEIGHT:
            {
                // 3rd input is a scalar
                in2 = cachedValues.getFirst(input2);
                if (in1 == null || in2 == null)
                    return;
                OperationsOnMatrixValues.performCtable(in1.getIndexes(), in1.getValue(), in2.getIndexes(), in2.getValue(), scalar_input3, ctableResult, ctableResultBlock, optr);
                break;
            }
        case CTABLE_EXPAND_SCALAR_WEIGHT:
            {
                // 2nd and 3rd input is a scalar
                if (in1 == null)
                    return;
                OperationsOnMatrixValues.performCtable(in1.getIndexes(), in1.getValue(), scalar_input2, (scalar_input3 == 1), blockRowFactor, ctableResult, ctableResultBlock, optr);
                break;
            }
        case CTABLE_TRANSFORM_HISTOGRAM:
            {
                // 2nd and 3rd inputs are scalars
                if (in1 == null)
                    return;
                OperationsOnMatrixValues.performCtable(in1.getIndexes(), in1.getValue(), scalar_input2, scalar_input3, ctableResult, ctableResultBlock, optr);
                break;
            }
        case CTABLE_TRANSFORM_WEIGHTED_HISTOGRAM:
            {
                // 2nd and 3rd inputs are scalars
                in3 = cachedValues.getFirst(input3);
                if (in1 == null || in3 == null)
                    return;
                OperationsOnMatrixValues.performCtable(in1.getIndexes(), in1.getValue(), scalar_input2, in3.getIndexes(), in3.getValue(), ctableResult, ctableResultBlock, optr);
                break;
            }
        default:
            throw new DMLRuntimeException("Unrecognized opcode in Tertiary Instruction: " + instString);
    }
}

Example 29 with IndexedMatrixValue

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

the class CumulativeAggregateInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    ArrayList<IndexedMatrixValue> blkList = cachedValues.get(input);
    if (blkList == null)
        return;
    for (IndexedMatrixValue in1 : blkList) {
        if (in1 == null)
            continue;
        MatrixIndexes inix = in1.getIndexes();
        // output allocation
        IndexedMatrixValue out = cachedValues.holdPlace(output, valueClass);
        // process instruction
        OperationsOnMatrixValues.performAggregateUnary(inix, in1.getValue(), out.getIndexes(), out.getValue(), ((AggregateUnaryOperator) optr), blockRowFactor, blockColFactor);
        if (((AggregateUnaryOperator) optr).aggOp.correctionExists)
            ((MatrixBlock) out.getValue()).dropLastRowsOrColumns(((AggregateUnaryOperator) optr).aggOp.correctionLocation);
        // cumsum expand partial aggregates
        long rlenOut = (long) Math.ceil((double) _mcIn.getRows() / blockRowFactor);
        long rixOut = (long) Math.ceil((double) inix.getRowIndex() / blockRowFactor);
        int rlenBlk = (int) Math.min(rlenOut - (rixOut - 1) * blockRowFactor, blockRowFactor);
        int clenBlk = out.getValue().getNumColumns();
        int posBlk = (int) ((inix.getRowIndex() - 1) % blockRowFactor);
        MatrixBlock outBlk = new MatrixBlock(rlenBlk, clenBlk, false);
        outBlk.copy(posBlk, posBlk, 0, clenBlk - 1, (MatrixBlock) out.getValue(), true);
        MatrixIndexes outIx = out.getIndexes();
        outIx.setIndexes(rixOut, outIx.getColumnIndex());
        out.set(outIx, outBlk);
    }
}

Example 30 with IndexedMatrixValue

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

the class CumulativeOffsetInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    // original data
    IndexedMatrixValue in1 = cachedValues.getFirst(input1);
    // offset row vector
    IndexedMatrixValue in2 = cachedValues.getFirst(input2);
    if (in1 == null || in2 == null)
        throw new DMLRuntimeException("Unexpected empty input (left=" + ((in1 == null) ? "null" : in1.getIndexes()) + ", right=" + ((in2 == null) ? "null" : in2.getIndexes()) + ").");
    // prepare inputs and outputs
    IndexedMatrixValue out = cachedValues.holdPlace(output, valueClass);
    MatrixBlock data = (MatrixBlock) in1.getValue();
    MatrixBlock offset = (MatrixBlock) in2.getValue();
    MatrixBlock blk = (MatrixBlock) out.getValue();
    blk.reset(data.getNumRows(), data.getNumColumns());
    // blockwise offset aggregation and prefix sum computation
    // cp data
    MatrixBlock data2 = new MatrixBlock(data);
    // 1-based
    MatrixBlock fdata2 = data2.slice(0, 0, 0, data2.getNumColumns() - 1, new MatrixBlock());
    // sum offset to first row
    fdata2.binaryOperationsInPlace(_bop, offset);
    // 0-based
    data2.copy(0, 0, 0, data2.getNumColumns() - 1, fdata2, true);
    // compute columnwise prefix sums/prod/min/max
    data2.unaryOperations(_uop, blk);
    // set output indexes
    out.getIndexes().setIndexes(in1.getIndexes());
}