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

Example 6 with MatrixCell

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

the class ResultMergeLocalFile method createBinaryCellResultFile.

@SuppressWarnings("deprecation")
private void createBinaryCellResultFile(String fnameStaging, String fnameStagingCompare, String fnameNew, MetaDataFormat metadata, boolean withCompare) throws IOException, DMLRuntimeException {
    JobConf job = new JobConf(ConfigurationManager.getCachedJobConf());
    Path path = new Path(fnameNew);
    FileSystem fs = IOUtilFunctions.getFileSystem(path, job);
    MatrixCharacteristics mc = metadata.getMatrixCharacteristics();
    long rlen = mc.getRows();
    long clen = mc.getCols();
    int brlen = mc.getRowsPerBlock();
    int bclen = mc.getColsPerBlock();
    MatrixIndexes indexes = new MatrixIndexes(1, 1);
    MatrixCell cell = new MatrixCell(0);
    // beware ca 50ms
    SequenceFile.Writer out = new SequenceFile.Writer(fs, job, path, MatrixIndexes.class, MatrixCell.class);
    try {
        boolean written = false;
        for (long brow = 1; brow <= (long) Math.ceil(rlen / (double) brlen); brow++) for (long bcol = 1; bcol <= (long) Math.ceil(clen / (double) bclen); bcol++) {
            File dir = new File(fnameStaging + "/" + brow + "_" + bcol);
            File dir2 = new File(fnameStagingCompare + "/" + brow + "_" + bcol);
            MatrixBlock mb = null;
            long row_offset = (brow - 1) * brlen + 1;
            long col_offset = (bcol - 1) * bclen + 1;
            if (dir.exists()) {
                if (// WITH COMPARE BLOCK
                withCompare && dir2.exists()) {
                    // copy only values that are different from the original
                    String[] lnames2 = dir2.list();
                    if (// there should be exactly 1 compare block
                    lnames2.length != 1)
                        throw new DMLRuntimeException("Unable to merge results because multiple compare blocks found.");
                    mb = StagingFileUtils.readCellList2BlockFromLocal(dir2 + "/" + lnames2[0], brlen, bclen);
                    boolean appendOnly = mb.isInSparseFormat();
                    DenseBlock compare = DataConverter.convertToDenseBlock(mb, false);
                    for (String lname : dir.list()) {
                        MatrixBlock tmp = StagingFileUtils.readCellList2BlockFromLocal(dir + "/" + lname, brlen, bclen);
                        mergeWithComp(mb, tmp, compare);
                    }
                    // sort sparse due to append-only
                    if (appendOnly && !_isAccum)
                        mb.sortSparseRows();
                    // change sparsity if required after
                    mb.examSparsity();
                } else // WITHOUT COMPARE BLOCK
                {
                    // copy all non-zeros from all workers
                    boolean appendOnly = false;
                    for (String lname : dir.list()) {
                        if (mb == null) {
                            mb = StagingFileUtils.readCellList2BlockFromLocal(dir + "/" + lname, brlen, bclen);
                            appendOnly = mb.isInSparseFormat();
                        } else {
                            MatrixBlock tmp = StagingFileUtils.readCellList2BlockFromLocal(dir + "/" + lname, brlen, bclen);
                            mergeWithoutComp(mb, tmp, appendOnly);
                        }
                    }
                    // sort sparse due to append-only
                    if (appendOnly && !_isAccum)
                        mb.sortSparseRows();
                    // change sparsity if required after
                    mb.examSparsity();
                }
            }
            // write the block to binary cell
            if (mb != null) {
                if (mb.isInSparseFormat()) {
                    Iterator<IJV> iter = mb.getSparseBlockIterator();
                    while (iter.hasNext()) {
                        IJV lcell = iter.next();
                        indexes.setIndexes(row_offset + lcell.getI(), col_offset + lcell.getJ());
                        cell.setValue(lcell.getV());
                        out.append(indexes, cell);
                        written = true;
                    }
                } else {
                    for (int i = 0; i < brlen; i++) for (int j = 0; j < bclen; j++) {
                        double lvalue = mb.getValueDenseUnsafe(i, j);
                        if (// for nnz
                        lvalue != 0) {
                            indexes.setIndexes(row_offset + i, col_offset + j);
                            cell.setValue(lvalue);
                            out.append(indexes, cell);
                            written = true;
                        }
                    }
                }
            }
        }
        if (!written)
            out.append(indexes, cell);
    } finally {
        IOUtilFunctions.closeSilently(out);
    }
}

Example 7 with MatrixCell

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

the class PairWritableCell method readFields.

@Override
public void readFields(DataInput in) throws IOException {
    indexes = new MatrixIndexes();
    indexes.readFields(in);
    cell = new MatrixCell();
    cell.readFields(in);
}

Example 8 with MatrixCell

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

the class TestUtils method writeBinaryTestMatrixCells.

/**
 * <p>
 * Writes a matrix to a file using the binary cells format.
 * </p>
 *
 * @param file
 *            file name
 * @param matrix
 *            matrix
 */
@SuppressWarnings("deprecation")
public static void writeBinaryTestMatrixCells(String file, double[][] matrix) {
    try {
        SequenceFile.Writer writer = null;
        try {
            Path path = new Path(file);
            FileSystem fs = IOUtilFunctions.getFileSystem(path, conf);
            writer = new SequenceFile.Writer(fs, conf, path, MatrixIndexes.class, MatrixCell.class);
            MatrixIndexes index = new MatrixIndexes();
            MatrixCell value = new MatrixCell();
            for (int i = 0; i < matrix.length; i++) {
                for (int j = 0; j < matrix[i].length; j++) {
                    if (matrix[i][j] != 0) {
                        index.setIndexes((i + 1), (j + 1));
                        value.setValue(matrix[i][j]);
                        writer.append(index, value);
                    }
                }
            }
        } finally {
            IOUtilFunctions.closeSilently(writer);
        }
    } catch (IOException e) {
        e.printStackTrace();
        fail("unable to write test matrix: " + e.getMessage());
    }
}

Example 9 with MatrixCell

use of org.apache.sysml.runtime.matrix.data.MatrixCell in project systemml by apache.

the class RandSPInstruction method generateSample.

/**
 * Helper function to construct a sample.
 *
 * @param sec spark execution context
 */
private void generateSample(SparkExecutionContext sec) {
    long lrows = sec.getScalarInput(rows).getLongValue();
    if (maxValue < lrows && !replace)
        throw new DMLRuntimeException("Sample (size=" + rows + ") larger than population (size=" + maxValue + ") can only be generated with replacement.");
    if (LOG.isTraceEnabled())
        LOG.trace("Process RandSPInstruction sample with range=" + maxValue + ", size=" + lrows + ", replace=" + replace + ", seed=" + seed);
    // sampling rate that guarantees a sample of size >= sampleSizeLowerBound 99.99% of the time.
    double fraction = SamplingUtils.computeFractionForSampleSize((int) lrows, UtilFunctions.toLong(maxValue), replace);
    Well1024a bigrand = LibMatrixDatagen.setupSeedsForRand(seed);
    // divide the population range across numPartitions by creating SampleTasks
    double hdfsBlockSize = InfrastructureAnalyzer.getHDFSBlockSize();
    long outputSize = MatrixBlock.estimateSizeDenseInMemory(lrows, 1);
    int numPartitions = (int) Math.ceil((double) outputSize / hdfsBlockSize);
    long partitionSize = (long) Math.ceil(maxValue / numPartitions);
    ArrayList<SampleTask> offsets = new ArrayList<>();
    long st = 1;
    while (st <= maxValue) {
        SampleTask s = new SampleTask();
        s.range_start = st;
        s.seed = bigrand.nextLong();
        offsets.add(s);
        st = st + partitionSize;
    }
    JavaRDD<SampleTask> offsetRDD = sec.getSparkContext().parallelize(offsets, numPartitions);
    // Construct the sample in a distributed manner
    JavaRDD<Double> rdd = offsetRDD.flatMap((new GenerateSampleBlock(replace, fraction, (long) maxValue, partitionSize)));
    // Randomize the sampled elements
    JavaRDD<Double> randomizedRDD = rdd.mapToPair(new AttachRandom()).sortByKey().values();
    // Trim the sampled list to required size & attach matrix indexes to randomized elements
    JavaPairRDD<MatrixIndexes, MatrixCell> miRDD = randomizedRDD.zipWithIndex().filter(new TrimSample(lrows)).mapToPair(new Double2MatrixCell());
    MatrixCharacteristics mcOut = new MatrixCharacteristics(lrows, 1, rowsInBlock, colsInBlock, lrows);
    // Construct BinaryBlock representation
    JavaPairRDD<MatrixIndexes, MatrixBlock> mbRDD = RDDConverterUtils.binaryCellToBinaryBlock(sec.getSparkContext(), miRDD, mcOut, true);
    sec.getMatrixCharacteristics(output.getName()).setNonZeros(lrows);
    sec.setRDDHandleForVariable(output.getName(), mbRDD);
}

Example 10 with MatrixCell

use of org.apache.sysml.runtime.matrix.data.MatrixCell in project systemml by apache.

the class CtableSPInstruction method processInstruction.

@Override
public void processInstruction(ExecutionContext ec) {
    SparkExecutionContext sec = (SparkExecutionContext) ec;
    // get input rdd handle
    JavaPairRDD<MatrixIndexes, MatrixBlock> in1 = sec.getBinaryBlockRDDHandleForVariable(input1.getName());
    JavaPairRDD<MatrixIndexes, MatrixBlock> in2 = null;
    JavaPairRDD<MatrixIndexes, MatrixBlock> in3 = null;
    double scalar_input2 = -1, scalar_input3 = -1;
    Ctable.OperationTypes ctableOp = Ctable.findCtableOperationByInputDataTypes(input1.getDataType(), input2.getDataType(), input3.getDataType());
    ctableOp = _isExpand ? Ctable.OperationTypes.CTABLE_EXPAND_SCALAR_WEIGHT : ctableOp;
    MatrixCharacteristics mc1 = sec.getMatrixCharacteristics(input1.getName());
    MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
    // First get the block sizes and then set them as -1 to allow for binary cell reblock
    int brlen = mc1.getRowsPerBlock();
    int bclen = mc1.getColsPerBlock();
    JavaPairRDD<MatrixIndexes, ArrayList<MatrixBlock>> inputMBs = null;
    JavaPairRDD<MatrixIndexes, CTableMap> ctables = null;
    JavaPairRDD<MatrixIndexes, Double> bincellsNoFilter = null;
    boolean setLineage2 = false;
    boolean setLineage3 = false;
    switch(ctableOp) {
        case // (VECTOR)
        CTABLE_TRANSFORM:
            // F=ctable(A,B,W)
            in2 = sec.getBinaryBlockRDDHandleForVariable(input2.getName());
            in3 = sec.getBinaryBlockRDDHandleForVariable(input3.getName());
            setLineage2 = true;
            setLineage3 = true;
            inputMBs = in1.cogroup(in2).cogroup(in3).mapToPair(new MapThreeMBIterableIntoAL());
            ctables = inputMBs.mapToPair(new PerformCTableMapSideOperation(ctableOp, scalar_input2, scalar_input3, this.instString, (SimpleOperator) _optr, _ignoreZeros));
            break;
        case // (VECTOR)
        CTABLE_EXPAND_SCALAR_WEIGHT:
            // F = ctable(seq,A) or F = ctable(seq,B,1)
            scalar_input3 = sec.getScalarInput(input3.getName(), input3.getValueType(), input3.isLiteral()).getDoubleValue();
            if (scalar_input3 == 1) {
                in2 = sec.getBinaryBlockRDDHandleForVariable(input2.getName());
                setLineage2 = true;
                bincellsNoFilter = in2.flatMapToPair(new ExpandScalarCtableOperation(brlen));
                break;
            }
        case // (VECTOR/MATRIX)
        CTABLE_TRANSFORM_SCALAR_WEIGHT:
            // F = ctable(A,B) or F = ctable(A,B,1)
            in2 = sec.getBinaryBlockRDDHandleForVariable(input2.getName());
            setLineage2 = true;
            scalar_input3 = sec.getScalarInput(input3.getName(), input3.getValueType(), input3.isLiteral()).getDoubleValue();
            inputMBs = in1.cogroup(in2).mapToPair(new MapTwoMBIterableIntoAL());
            ctables = inputMBs.mapToPair(new PerformCTableMapSideOperation(ctableOp, scalar_input2, scalar_input3, this.instString, (SimpleOperator) _optr, _ignoreZeros));
            break;
        case // (VECTOR)
        CTABLE_TRANSFORM_HISTOGRAM:
            // F=ctable(A,1) or F = ctable(A,1,1)
            scalar_input2 = sec.getScalarInput(input2.getName(), input2.getValueType(), input2.isLiteral()).getDoubleValue();
            scalar_input3 = sec.getScalarInput(input3.getName(), input3.getValueType(), input3.isLiteral()).getDoubleValue();
            inputMBs = in1.mapToPair(new MapMBIntoAL());
            ctables = inputMBs.mapToPair(new PerformCTableMapSideOperation(ctableOp, scalar_input2, scalar_input3, this.instString, (SimpleOperator) _optr, _ignoreZeros));
            break;
        case // (VECTOR)
        CTABLE_TRANSFORM_WEIGHTED_HISTOGRAM:
            // F=ctable(A,1,W)
            in3 = sec.getBinaryBlockRDDHandleForVariable(input3.getName());
            setLineage3 = true;
            scalar_input2 = sec.getScalarInput(input2.getName(), input2.getValueType(), input2.isLiteral()).getDoubleValue();
            inputMBs = in1.cogroup(in3).mapToPair(new MapTwoMBIterableIntoAL());
            ctables = inputMBs.mapToPair(new PerformCTableMapSideOperation(ctableOp, scalar_input2, scalar_input3, this.instString, (SimpleOperator) _optr, _ignoreZeros));
            break;
        default:
            throw new DMLRuntimeException("Encountered an invalid ctable operation (" + ctableOp + ") while executing instruction: " + this.toString());
    }
    // Now perform aggregation on ctables to get binaryCells
    if (bincellsNoFilter == null && ctables != null) {
        bincellsNoFilter = ctables.values().flatMapToPair(new ExtractBinaryCellsFromCTable());
        bincellsNoFilter = RDDAggregateUtils.sumCellsByKeyStable(bincellsNoFilter);
    } else if (!(bincellsNoFilter != null && ctables == null)) {
        throw new DMLRuntimeException("Incorrect ctable operation");
    }
    // handle known/unknown dimensions
    long outputDim1 = (_dim1Literal ? (long) Double.parseDouble(_outDim1) : (sec.getScalarInput(_outDim1, ValueType.DOUBLE, false)).getLongValue());
    long outputDim2 = (_dim2Literal ? (long) Double.parseDouble(_outDim2) : (sec.getScalarInput(_outDim2, ValueType.DOUBLE, false)).getLongValue());
    MatrixCharacteristics mcBinaryCells = null;
    boolean findDimensions = (outputDim1 == -1 && outputDim2 == -1);
    if (!findDimensions) {
        if ((outputDim1 == -1 && outputDim2 != -1) || (outputDim1 != -1 && outputDim2 == -1))
            throw new DMLRuntimeException("Incorrect output dimensions passed to TernarySPInstruction:" + outputDim1 + " " + outputDim2);
        else
            mcBinaryCells = new MatrixCharacteristics(outputDim1, outputDim2, brlen, bclen);
        // filtering according to given dimensions
        bincellsNoFilter = bincellsNoFilter.filter(new FilterCells(mcBinaryCells.getRows(), mcBinaryCells.getCols()));
    }
    // convert double values to matrix cell
    JavaPairRDD<MatrixIndexes, MatrixCell> binaryCells = bincellsNoFilter.mapToPair(new ConvertToBinaryCell());
    // find dimensions if necessary (w/ cache for reblock)
    if (findDimensions) {
        binaryCells = SparkUtils.cacheBinaryCellRDD(binaryCells);
        mcBinaryCells = SparkUtils.computeMatrixCharacteristics(binaryCells);
    }
    // store output rdd handle
    sec.setRDDHandleForVariable(output.getName(), binaryCells);
    mcOut.set(mcBinaryCells);
    // Since we are outputing binary cells, we set block sizes = -1
    mcOut.setRowsPerBlock(-1);
    mcOut.setColsPerBlock(-1);
    sec.addLineageRDD(output.getName(), input1.getName());
    if (setLineage2)
        sec.addLineageRDD(output.getName(), input2.getName());
    if (setLineage3)
        sec.addLineageRDD(output.getName(), input3.getName());
}