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

Example 41 with IndexedMatrixValue

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

the class LibMatrixReorg method reshape.

/**
 * MR/SPARK reshape interface - for reshape we cannot view blocks independently, and hence,
 * there are different CP and MR interfaces.
 *
 * @param in indexed matrix value
 * @param mcIn input matrix characteristics
 * @param out list of indexed matrix values
 * @param mcOut output matrix characteristics
 * @param rowwise if true, reshape by row
 * @return list of indexed matrix values
 */
public static ArrayList<IndexedMatrixValue> reshape(IndexedMatrixValue in, MatrixCharacteristics mcIn, ArrayList<IndexedMatrixValue> out, MatrixCharacteristics mcOut, boolean rowwise) {
    // prepare inputs
    MatrixIndexes ixIn = in.getIndexes();
    MatrixBlock mbIn = (MatrixBlock) in.getValue();
    // prepare result blocks (no reuse in order to guarantee mem constraints)
    Collection<MatrixIndexes> rix = computeAllResultBlockIndexes(ixIn, mcIn, mcOut, rowwise);
    HashMap<MatrixIndexes, MatrixBlock> rblk = createAllResultBlocks(rix, mbIn.nonZeros, mcIn, mcOut, rowwise, out);
    // basic algorithm
    long row_offset = (ixIn.getRowIndex() - 1) * mcIn.getRowsPerBlock();
    long col_offset = (ixIn.getColumnIndex() - 1) * mcIn.getColsPerBlock();
    if (mbIn.sparse)
        reshapeSparse(mbIn, row_offset, col_offset, rblk, mcIn, mcOut, rowwise);
    else
        // dense
        reshapeDense(mbIn, row_offset, col_offset, rblk, mcIn, mcOut, rowwise);
    // prepare output
    out = new ArrayList<>();
    for (Entry<MatrixIndexes, MatrixBlock> e : rblk.entrySet()) {
        // ensure correct format
        e.getValue().examSparsity();
        out.add(new IndexedMatrixValue(e.getKey(), e.getValue()));
    }
    return out;
}

Example 42 with IndexedMatrixValue

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

the class OperationsOnMatrixValues method performMapGroupedAggregate.

public static void performMapGroupedAggregate(Operator op, IndexedMatrixValue inTarget, MatrixBlock groups, int ngroups, int brlen, int bclen, ArrayList<IndexedMatrixValue> outlist) {
    MatrixIndexes ix = inTarget.getIndexes();
    MatrixBlock target = (MatrixBlock) inTarget.getValue();
    // execute grouped aggregate operations
    MatrixBlock out = groups.groupedAggOperations(target, null, new MatrixBlock(), ngroups, op);
    if (out.getNumRows() <= brlen && out.getNumColumns() <= bclen) {
        // single output block
        outlist.add(new IndexedMatrixValue(new MatrixIndexes(1, ix.getColumnIndex()), out));
    } else {
        // multiple output blocks (by op def, single column block )
        for (int blockRow = 0; blockRow < (int) Math.ceil(out.getNumRows() / (double) brlen); blockRow++) {
            int maxRow = (blockRow * brlen + brlen < out.getNumRows()) ? brlen : out.getNumRows() - blockRow * brlen;
            int row_offset = blockRow * brlen;
            // copy submatrix to block
            MatrixBlock tmp = out.slice(row_offset, row_offset + maxRow - 1, 0, out.getNumColumns() - 1, new MatrixBlock());
            // append block to result cache
            outlist.add(new IndexedMatrixValue(new MatrixIndexes(blockRow + 1, ix.getColumnIndex()), tmp));
        }
    }
}

Example 43 with IndexedMatrixValue

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

the class OperationsOnMatrixValues method performSlice.

@SuppressWarnings("rawtypes")
public static ArrayList performSlice(IndexRange ixrange, int brlen, int bclen, int iix, int jix, MatrixBlock in) {
    IndexedMatrixValue imv = new IndexedMatrixValue(new MatrixIndexes(iix, jix), (MatrixBlock) in);
    ArrayList<IndexedMatrixValue> outlist = new ArrayList<>();
    performSlice(imv, ixrange, brlen, bclen, outlist);
    return SparkUtils.fromIndexedMatrixBlockToPair(outlist);
}

Example 44 with IndexedMatrixValue

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

the class TernaryInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    MatrixBlock lm1 = input1.isMatrix() ? (MatrixBlock) cachedValues.getFirst(ixinput1).getValue() : m1;
    MatrixBlock lm2 = input2.isMatrix() ? (MatrixBlock) cachedValues.getFirst(ixinput2).getValue() : m2;
    MatrixBlock lm3 = input3.isMatrix() ? (MatrixBlock) cachedValues.getFirst(ixinput3).getValue() : m3;
    MatrixIndexes ixin = input1.isMatrix() ? cachedValues.getFirst(ixinput1).getIndexes() : input2.isMatrix() ? cachedValues.getFirst(ixinput2).getIndexes() : cachedValues.getFirst(ixinput3).getIndexes();
    // prepare output
    IndexedMatrixValue out = new IndexedMatrixValue(new MatrixIndexes(), new MatrixBlock());
    out.getIndexes().setIndexes(ixin);
    // process instruction
    TernaryOperator op = (TernaryOperator) optr;
    lm1.ternaryOperations(op, lm2, lm3, (MatrixBlock) out.getValue());
    // put the output value in the cache
    cachedValues.add(ixoutput, out);
}

Example 45 with IndexedMatrixValue

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

the class UaggOuterChainInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    ArrayList<IndexedMatrixValue> blkList = null;
    boolean rightCached = (_uaggOp.indexFn instanceof ReduceCol || _uaggOp.indexFn instanceof ReduceAll || !LibMatrixOuterAgg.isSupportedUaggOp(_uaggOp, _bOp));
    // get the main data input
    if (rightCached)
        blkList = cachedValues.get(input1);
    else
        // ReduceRow
        blkList = cachedValues.get(input2);
    if (blkList == null)
        return;
    for (IndexedMatrixValue imv : blkList) {
        if (imv == null)
            continue;
        MatrixIndexes in1Ix = imv.getIndexes();
        MatrixValue in1Val = imv.getValue();
        // allocate space for the intermediate and output value
        IndexedMatrixValue iout = cachedValues.holdPlace(output, valueClass);
        MatrixIndexes outIx = iout.getIndexes();
        MatrixValue outVal = iout.getValue();
        MatrixBlock corr = null;
        // get the distributed cache input
        byte dcInputIx = rightCached ? input2 : input1;
        DistributedCacheInput dcInput = MRBaseForCommonInstructions.dcValues.get(dcInputIx);
        // process instruction
        if (LibMatrixOuterAgg.isSupportedUaggOp(_uaggOp, _bOp)) {
            if ((LibMatrixOuterAgg.isRowIndexMax(_uaggOp)) || (LibMatrixOuterAgg.isRowIndexMin(_uaggOp))) {
                if (_bv == null) {
                    if (rightCached)
                        _bv = dcInput.getRowVectorArray();
                    else
                        _bv = dcInput.getColumnVectorArray();
                    _bvi = LibMatrixOuterAgg.prepareRowIndices(_bv.length, _bv, _bOp, _uaggOp);
                }
            } else {
                // step 1: prepare sorted rhs input (once per task)
                if (_bv == null) {
                    if (rightCached)
                        _bv = dcInput.getRowVectorArray();
                    else
                        _bv = dcInput.getColumnVectorArray();
                    Arrays.sort(_bv);
                }
            }
            LibMatrixOuterAgg.resetOutputMatrix(in1Ix, (MatrixBlock) in1Val, outIx, (MatrixBlock) outVal, _uaggOp);
            LibMatrixOuterAgg.aggregateMatrix((MatrixBlock) in1Val, (MatrixBlock) outVal, _bv, _bvi, _bOp, _uaggOp);
        } else // default case
        {
            long in2_cols = dcInput.getNumCols();
            long in2_colBlocks = (long) Math.ceil(((double) in2_cols) / dcInput.getNumColsPerBlock());
            for (int bidx = 1; bidx <= in2_colBlocks; bidx++) {
                IndexedMatrixValue imv2 = dcInput.getDataBlock(1, bidx);
                MatrixValue in2Val = imv2.getValue();
                // outer block operation
                OperationsOnMatrixValues.performBinaryIgnoreIndexes(in1Val, in2Val, _tmpVal1, _bOp);
                // unary aggregate operation
                OperationsOnMatrixValues.performAggregateUnary(in1Ix, _tmpVal1, outIx, _tmpVal2, _uaggOp, blockRowFactor, blockColFactor);
                // aggregate over all rhs blocks
                if (corr == null) {
                    outVal.reset(_tmpVal2.getNumRows(), _tmpVal2.getNumColumns(), false);
                    corr = new MatrixBlock(_tmpVal2.getNumRows(), _tmpVal2.getNumColumns(), false);
                }
                if (_aggOp.correctionExists)
                    OperationsOnMatrixValues.incrementalAggregation(outVal, corr, _tmpVal2, _aggOp, true);
                else
                    OperationsOnMatrixValues.incrementalAggregation(outVal, null, _tmpVal2, _aggOp, true);
            }
        }
    }
}