Examples with FrameObject org.apache.sysml.runtime.controlprogram.caching.FrameObject used on opensource projects

Example 6 with FrameObject

use of org.apache.sysml.runtime.controlprogram.caching.FrameObject in project incubator-systemml by apache.

the class MLContextConversionUtil method frameBlockToFrameObject.

/**
 * Convert a {@code FrameBlock} to a {@code FrameObject}.
 *
 * @param variableName
 *            name of the variable associated with the frame
 * @param frameBlock
 *            frame as a FrameBlock
 * @param frameMetadata
 *            the frame metadata
 * @return the {@code FrameBlock} converted to a {@code FrameObject}
 */
public static FrameObject frameBlockToFrameObject(String variableName, FrameBlock frameBlock, FrameMetadata frameMetadata) {
    try {
        MatrixCharacteristics mc = (frameMetadata != null) ? frameMetadata.asMatrixCharacteristics() : new MatrixCharacteristics();
        MetaDataFormat mtd = new MetaDataFormat(mc, OutputInfo.BinaryBlockOutputInfo, InputInfo.BinaryBlockInputInfo);
        FrameObject frameObject = new FrameObject(OptimizerUtils.getUniqueTempFileName(), mtd, frameMetadata.getFrameSchema().getSchema().toArray(new ValueType[0]));
        frameObject.acquireModify(frameBlock);
        frameObject.release();
        return frameObject;
    } catch (DMLRuntimeException e) {
        throw new MLContextException("Exception converting FrameBlock to FrameObject", e);
    }
}

Example 7 with FrameObject

use of org.apache.sysml.runtime.controlprogram.caching.FrameObject in project incubator-systemml by apache.

the class MLContextConversionUtil method binaryBlocksToFrameObject.

/**
 * Convert a {@code JavaPairRDD<Long, FrameBlock>} to a {@code FrameObject}.
 *
 * @param binaryBlocks
 *            {@code JavaPairRDD<Long, FrameBlock>} representation of a
 *            binary-block frame
 * @param frameMetadata
 *            the frame metadata
 * @return the {@code JavaPairRDD<Long, FrameBlock>} frame converted to a
 *         {@code FrameObject}
 */
public static FrameObject binaryBlocksToFrameObject(JavaPairRDD<Long, FrameBlock> binaryBlocks, FrameMetadata frameMetadata) {
    MatrixCharacteristics mc = (frameMetadata != null) ? frameMetadata.asMatrixCharacteristics() : new MatrixCharacteristics();
    ValueType[] schema = (frameMetadata != null) ? frameMetadata.getFrameSchema().getSchema().toArray(new ValueType[0]) : UtilFunctions.nCopies((int) mc.getCols(), ValueType.STRING);
    FrameObject frameObject = new FrameObject(OptimizerUtils.getUniqueTempFileName(), new MetaDataFormat(mc, OutputInfo.BinaryBlockOutputInfo, InputInfo.BinaryBlockInputInfo), schema);
    frameObject.setRDDHandle(new RDDObject(binaryBlocks));
    return frameObject;
}

Example 8 with FrameObject

use of org.apache.sysml.runtime.controlprogram.caching.FrameObject in project incubator-systemml by apache.

the class MLContextConversionUtil method javaRDDStringCSVToFrameObject.

/**
 * Convert a {@code JavaRDD<String>} in CSV format to a {@code FrameObject}
 *
 * @param javaRDD
 *            the Java RDD of strings
 * @param frameMetadata
 *            frame metadata
 * @return the {@code JavaRDD<String>} converted to a {@code FrameObject}
 */
public static FrameObject javaRDDStringCSVToFrameObject(JavaRDD<String> javaRDD, FrameMetadata frameMetadata) {
    JavaPairRDD<LongWritable, Text> javaPairRDD = javaRDD.mapToPair(new ConvertStringToLongTextPair());
    MatrixCharacteristics mc = (frameMetadata != null) ? frameMetadata.asMatrixCharacteristics() : new MatrixCharacteristics();
    JavaPairRDD<LongWritable, Text> javaPairRDDText = javaPairRDD.mapToPair(new CopyTextInputFunction());
    FrameObject frameObject = new FrameObject(OptimizerUtils.getUniqueTempFileName(), new MetaDataFormat(mc, OutputInfo.BinaryBlockOutputInfo, InputInfo.BinaryBlockInputInfo), frameMetadata.getFrameSchema().getSchema().toArray(new ValueType[0]));
    JavaPairRDD<Long, FrameBlock> rdd;
    try {
        rdd = FrameRDDConverterUtils.csvToBinaryBlock(jsc(), javaPairRDDText, mc, frameObject.getSchema(), false, ",", false, -1);
    } catch (DMLRuntimeException e) {
        e.printStackTrace();
        return null;
    }
    frameObject.setRDDHandle(new RDDObject(rdd));
    return frameObject;
}

Example 9 with FrameObject

use of org.apache.sysml.runtime.controlprogram.caching.FrameObject in project incubator-systemml by apache.

the class MultiReturnParameterizedBuiltinSPInstruction method processInstruction.

@Override
@SuppressWarnings("unchecked")
public void processInstruction(ExecutionContext ec) {
    SparkExecutionContext sec = (SparkExecutionContext) ec;
    try {
        // get input RDD and meta data
        FrameObject fo = sec.getFrameObject(input1.getName());
        FrameObject fometa = sec.getFrameObject(_outputs.get(1).getName());
        JavaPairRDD<Long, FrameBlock> in = (JavaPairRDD<Long, FrameBlock>) sec.getRDDHandleForFrameObject(fo, InputInfo.BinaryBlockInputInfo);
        String spec = ec.getScalarInput(input2.getName(), input2.getValueType(), input2.isLiteral()).getStringValue();
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(input1.getName());
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
        String[] colnames = !TfMetaUtils.isIDSpec(spec) ? in.lookup(1L).get(0).getColumnNames() : null;
        // step 1: build transform meta data
        Encoder encoderBuild = EncoderFactory.createEncoder(spec, colnames, fo.getSchema(), (int) fo.getNumColumns(), null);
        MaxLongAccumulator accMax = registerMaxLongAccumulator(sec.getSparkContext());
        JavaRDD<String> rcMaps = in.mapPartitionsToPair(new TransformEncodeBuildFunction(encoderBuild)).distinct().groupByKey().flatMap(new TransformEncodeGroupFunction(accMax));
        if (containsMVImputeEncoder(encoderBuild)) {
            EncoderMVImpute mva = getMVImputeEncoder(encoderBuild);
            rcMaps = rcMaps.union(in.mapPartitionsToPair(new TransformEncodeBuild2Function(mva)).groupByKey().flatMap(new TransformEncodeGroup2Function(mva)));
        }
        // trigger eval
        rcMaps.saveAsTextFile(fometa.getFileName());
        // consolidate meta data frame (reuse multi-threaded reader, special handling missing values)
        FrameReader reader = FrameReaderFactory.createFrameReader(InputInfo.TextCellInputInfo);
        FrameBlock meta = reader.readFrameFromHDFS(fometa.getFileName(), accMax.value(), fo.getNumColumns());
        // recompute num distinct items per column
        meta.recomputeColumnCardinality();
        meta.setColumnNames((colnames != null) ? colnames : meta.getColumnNames());
        // step 2: transform apply (similar to spark transformapply)
        // compute omit offset map for block shifts
        TfOffsetMap omap = null;
        if (TfMetaUtils.containsOmitSpec(spec, colnames)) {
            omap = new TfOffsetMap(SparkUtils.toIndexedLong(in.mapToPair(new RDDTransformApplyOffsetFunction(spec, colnames)).collect()));
        }
        // create encoder broadcast (avoiding replication per task)
        Encoder encoder = EncoderFactory.createEncoder(spec, colnames, fo.getSchema(), (int) fo.getNumColumns(), meta);
        mcOut.setDimension(mcIn.getRows() - ((omap != null) ? omap.getNumRmRows() : 0), encoder.getNumCols());
        Broadcast<Encoder> bmeta = sec.getSparkContext().broadcast(encoder);
        Broadcast<TfOffsetMap> bomap = (omap != null) ? sec.getSparkContext().broadcast(omap) : null;
        // execute transform apply
        JavaPairRDD<Long, FrameBlock> tmp = in.mapToPair(new RDDTransformApplyFunction(bmeta, bomap));
        JavaPairRDD<MatrixIndexes, MatrixBlock> out = FrameRDDConverterUtils.binaryBlockToMatrixBlock(tmp, mcOut, mcOut);
        // set output and maintain lineage/output characteristics
        sec.setRDDHandleForVariable(_outputs.get(0).getName(), out);
        sec.addLineageRDD(_outputs.get(0).getName(), input1.getName());
        sec.setFrameOutput(_outputs.get(1).getName(), meta);
    } catch (IOException ex) {
        throw new RuntimeException(ex);
    }
}

Example 10 with FrameObject

use of org.apache.sysml.runtime.controlprogram.caching.FrameObject in project incubator-systemml by apache.

the class ParameterizedBuiltinSPInstruction method processInstruction.

@Override
@SuppressWarnings("unchecked")
public void processInstruction(ExecutionContext ec) {
    SparkExecutionContext sec = (SparkExecutionContext) ec;
    String opcode = getOpcode();
    // opcode guaranteed to be a valid opcode (see parsing)
    if (opcode.equalsIgnoreCase("mapgroupedagg")) {
        // get input rdd handle
        String targetVar = params.get(Statement.GAGG_TARGET);
        String groupsVar = params.get(Statement.GAGG_GROUPS);
        JavaPairRDD<MatrixIndexes, MatrixBlock> target = sec.getBinaryBlockRDDHandleForVariable(targetVar);
        PartitionedBroadcast<MatrixBlock> groups = sec.getBroadcastForVariable(groupsVar);
        MatrixCharacteristics mc1 = sec.getMatrixCharacteristics(targetVar);
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
        CPOperand ngrpOp = new CPOperand(params.get(Statement.GAGG_NUM_GROUPS));
        int ngroups = (int) sec.getScalarInput(ngrpOp.getName(), ngrpOp.getValueType(), ngrpOp.isLiteral()).getLongValue();
        // single-block aggregation
        if (ngroups <= mc1.getRowsPerBlock() && mc1.getCols() <= mc1.getColsPerBlock()) {
            // execute map grouped aggregate
            JavaRDD<MatrixBlock> out = target.map(new RDDMapGroupedAggFunction2(groups, _optr, ngroups));
            MatrixBlock out2 = RDDAggregateUtils.sumStable(out);
            // put output block into symbol table (no lineage because single block)
            // this also includes implicit maintenance of matrix characteristics
            sec.setMatrixOutput(output.getName(), out2, getExtendedOpcode());
        } else // multi-block aggregation
        {
            // execute map grouped aggregate
            JavaPairRDD<MatrixIndexes, MatrixBlock> out = target.flatMapToPair(new RDDMapGroupedAggFunction(groups, _optr, ngroups, mc1.getRowsPerBlock(), mc1.getColsPerBlock()));
            out = RDDAggregateUtils.sumByKeyStable(out, false);
            // updated characteristics and handle outputs
            mcOut.set(ngroups, mc1.getCols(), mc1.getRowsPerBlock(), mc1.getColsPerBlock(), -1);
            sec.setRDDHandleForVariable(output.getName(), out);
            sec.addLineageRDD(output.getName(), targetVar);
            sec.addLineageBroadcast(output.getName(), groupsVar);
        }
    } else if (opcode.equalsIgnoreCase("groupedagg")) {
        boolean broadcastGroups = Boolean.parseBoolean(params.get("broadcast"));
        // get input rdd handle
        String groupsVar = params.get(Statement.GAGG_GROUPS);
        JavaPairRDD<MatrixIndexes, MatrixBlock> target = sec.getBinaryBlockRDDHandleForVariable(params.get(Statement.GAGG_TARGET));
        JavaPairRDD<MatrixIndexes, MatrixBlock> groups = broadcastGroups ? null : sec.getBinaryBlockRDDHandleForVariable(groupsVar);
        JavaPairRDD<MatrixIndexes, MatrixBlock> weights = null;
        MatrixCharacteristics mc1 = sec.getMatrixCharacteristics(params.get(Statement.GAGG_TARGET));
        MatrixCharacteristics mc2 = sec.getMatrixCharacteristics(groupsVar);
        if (mc1.dimsKnown() && mc2.dimsKnown() && (mc1.getRows() != mc2.getRows() || mc2.getCols() != 1)) {
            throw new DMLRuntimeException("Grouped Aggregate dimension mismatch between target and groups.");
        }
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
        JavaPairRDD<MatrixIndexes, WeightedCell> groupWeightedCells = null;
        // Step 1: First extract groupWeightedCells from group, target and weights
        if (params.get(Statement.GAGG_WEIGHTS) != null) {
            weights = sec.getBinaryBlockRDDHandleForVariable(params.get(Statement.GAGG_WEIGHTS));
            MatrixCharacteristics mc3 = sec.getMatrixCharacteristics(params.get(Statement.GAGG_WEIGHTS));
            if (mc1.dimsKnown() && mc3.dimsKnown() && (mc1.getRows() != mc3.getRows() || mc1.getCols() != mc3.getCols())) {
                throw new DMLRuntimeException("Grouped Aggregate dimension mismatch between target, groups, and weights.");
            }
            groupWeightedCells = groups.join(target).join(weights).flatMapToPair(new ExtractGroupNWeights());
        } else // input vector or matrix
        {
            String ngroupsStr = params.get(Statement.GAGG_NUM_GROUPS);
            long ngroups = (ngroupsStr != null) ? (long) Double.parseDouble(ngroupsStr) : -1;
            // execute basic grouped aggregate (extract and preagg)
            if (broadcastGroups) {
                PartitionedBroadcast<MatrixBlock> pbm = sec.getBroadcastForVariable(groupsVar);
                groupWeightedCells = target.flatMapToPair(new ExtractGroupBroadcast(pbm, mc1.getColsPerBlock(), ngroups, _optr));
            } else {
                // replicate groups if necessary
                if (mc1.getNumColBlocks() > 1) {
                    groups = groups.flatMapToPair(new ReplicateVectorFunction(false, mc1.getNumColBlocks()));
                }
                groupWeightedCells = groups.join(target).flatMapToPair(new ExtractGroupJoin(mc1.getColsPerBlock(), ngroups, _optr));
            }
        }
        // Step 2: Make sure we have brlen required while creating <MatrixIndexes, MatrixCell>
        if (mc1.getRowsPerBlock() == -1) {
            throw new DMLRuntimeException("The block sizes are not specified for grouped aggregate");
        }
        int brlen = mc1.getRowsPerBlock();
        // Step 3: Now perform grouped aggregate operation (either on combiner side or reducer side)
        JavaPairRDD<MatrixIndexes, MatrixCell> out = null;
        if (_optr instanceof CMOperator && ((CMOperator) _optr).isPartialAggregateOperator() || _optr instanceof AggregateOperator) {
            out = groupWeightedCells.reduceByKey(new PerformGroupByAggInCombiner(_optr)).mapValues(new CreateMatrixCell(brlen, _optr));
        } else {
            // Use groupby key because partial aggregation is not supported
            out = groupWeightedCells.groupByKey().mapValues(new PerformGroupByAggInReducer(_optr)).mapValues(new CreateMatrixCell(brlen, _optr));
        }
        // Step 4: Set output characteristics and rdd handle
        setOutputCharacteristicsForGroupedAgg(mc1, mcOut, out);
        // store output rdd handle
        sec.setRDDHandleForVariable(output.getName(), out);
        sec.addLineageRDD(output.getName(), params.get(Statement.GAGG_TARGET));
        sec.addLineage(output.getName(), groupsVar, broadcastGroups);
        if (params.get(Statement.GAGG_WEIGHTS) != null) {
            sec.addLineageRDD(output.getName(), params.get(Statement.GAGG_WEIGHTS));
        }
    } else if (opcode.equalsIgnoreCase("rmempty")) {
        String rddInVar = params.get("target");
        String rddOffVar = params.get("offset");
        boolean rows = sec.getScalarInput(params.get("margin"), ValueType.STRING, true).getStringValue().equals("rows");
        boolean emptyReturn = Boolean.parseBoolean(params.get("empty.return").toLowerCase());
        long maxDim = sec.getScalarInput(params.get("maxdim"), ValueType.DOUBLE, false).getLongValue();
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(rddInVar);
        if (// default case
        maxDim > 0) {
            // get input rdd handle
            JavaPairRDD<MatrixIndexes, MatrixBlock> in = sec.getBinaryBlockRDDHandleForVariable(rddInVar);
            JavaPairRDD<MatrixIndexes, MatrixBlock> off;
            PartitionedBroadcast<MatrixBlock> broadcastOff;
            long brlen = mcIn.getRowsPerBlock();
            long bclen = mcIn.getColsPerBlock();
            long numRep = (long) Math.ceil(rows ? (double) mcIn.getCols() / bclen : (double) mcIn.getRows() / brlen);
            // execute remove empty rows/cols operation
            JavaPairRDD<MatrixIndexes, MatrixBlock> out;
            if (_bRmEmptyBC) {
                broadcastOff = sec.getBroadcastForVariable(rddOffVar);
                // Broadcast offset vector
                out = in.flatMapToPair(new RDDRemoveEmptyFunctionInMem(rows, maxDim, brlen, bclen, broadcastOff));
            } else {
                off = sec.getBinaryBlockRDDHandleForVariable(rddOffVar);
                out = in.join(off.flatMapToPair(new ReplicateVectorFunction(!rows, numRep))).flatMapToPair(new RDDRemoveEmptyFunction(rows, maxDim, brlen, bclen));
            }
            out = RDDAggregateUtils.mergeByKey(out, false);
            // store output rdd handle
            sec.setRDDHandleForVariable(output.getName(), out);
            sec.addLineageRDD(output.getName(), rddInVar);
            if (!_bRmEmptyBC)
                sec.addLineageRDD(output.getName(), rddOffVar);
            else
                sec.addLineageBroadcast(output.getName(), rddOffVar);
            // update output statistics (required for correctness)
            MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
            mcOut.set(rows ? maxDim : mcIn.getRows(), rows ? mcIn.getCols() : maxDim, (int) brlen, (int) bclen, mcIn.getNonZeros());
        } else // special case: empty output (ensure valid dims)
        {
            int n = emptyReturn ? 1 : 0;
            MatrixBlock out = new MatrixBlock(rows ? n : (int) mcIn.getRows(), rows ? (int) mcIn.getCols() : n, true);
            sec.setMatrixOutput(output.getName(), out, getExtendedOpcode());
        }
    } else if (opcode.equalsIgnoreCase("replace")) {
        // get input rdd handle
        String rddVar = params.get("target");
        JavaPairRDD<MatrixIndexes, MatrixBlock> in1 = sec.getBinaryBlockRDDHandleForVariable(rddVar);
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(rddVar);
        // execute replace operation
        double pattern = Double.parseDouble(params.get("pattern"));
        double replacement = Double.parseDouble(params.get("replacement"));
        JavaPairRDD<MatrixIndexes, MatrixBlock> out = in1.mapValues(new RDDReplaceFunction(pattern, replacement));
        // store output rdd handle
        sec.setRDDHandleForVariable(output.getName(), out);
        sec.addLineageRDD(output.getName(), rddVar);
        // update output statistics (required for correctness)
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
        mcOut.set(mcIn.getRows(), mcIn.getCols(), mcIn.getRowsPerBlock(), mcIn.getColsPerBlock(), (pattern != 0 && replacement != 0) ? mcIn.getNonZeros() : -1);
    } else if (opcode.equalsIgnoreCase("rexpand")) {
        String rddInVar = params.get("target");
        // get input rdd handle
        JavaPairRDD<MatrixIndexes, MatrixBlock> in = sec.getBinaryBlockRDDHandleForVariable(rddInVar);
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(rddInVar);
        double maxVal = Double.parseDouble(params.get("max"));
        long lmaxVal = UtilFunctions.toLong(maxVal);
        boolean dirRows = params.get("dir").equals("rows");
        boolean cast = Boolean.parseBoolean(params.get("cast"));
        boolean ignore = Boolean.parseBoolean(params.get("ignore"));
        long brlen = mcIn.getRowsPerBlock();
        long bclen = mcIn.getColsPerBlock();
        // repartition input vector for higher degree of parallelism
        // (avoid scenarios where few input partitions create huge outputs)
        MatrixCharacteristics mcTmp = new MatrixCharacteristics(dirRows ? lmaxVal : mcIn.getRows(), dirRows ? mcIn.getRows() : lmaxVal, (int) brlen, (int) bclen, mcIn.getRows());
        int numParts = (int) Math.min(SparkUtils.getNumPreferredPartitions(mcTmp, in), mcIn.getNumBlocks());
        if (numParts > in.getNumPartitions() * 2)
            in = in.repartition(numParts);
        // execute rexpand rows/cols operation (no shuffle required because outputs are
        // block-aligned with the input, i.e., one input block generates n output blocks)
        JavaPairRDD<MatrixIndexes, MatrixBlock> out = in.flatMapToPair(new RDDRExpandFunction(maxVal, dirRows, cast, ignore, brlen, bclen));
        // store output rdd handle
        sec.setRDDHandleForVariable(output.getName(), out);
        sec.addLineageRDD(output.getName(), rddInVar);
        // update output statistics (required for correctness)
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
        mcOut.set(dirRows ? lmaxVal : mcIn.getRows(), dirRows ? mcIn.getRows() : lmaxVal, (int) brlen, (int) bclen, -1);
    } else if (opcode.equalsIgnoreCase("transformapply")) {
        // get input RDD and meta data
        FrameObject fo = sec.getFrameObject(params.get("target"));
        JavaPairRDD<Long, FrameBlock> in = (JavaPairRDD<Long, FrameBlock>) sec.getRDDHandleForFrameObject(fo, InputInfo.BinaryBlockInputInfo);
        FrameBlock meta = sec.getFrameInput(params.get("meta"));
        MatrixCharacteristics mcIn = sec.getMatrixCharacteristics(params.get("target"));
        MatrixCharacteristics mcOut = sec.getMatrixCharacteristics(output.getName());
        String[] colnames = !TfMetaUtils.isIDSpec(params.get("spec")) ? in.lookup(1L).get(0).getColumnNames() : null;
        // compute omit offset map for block shifts
        TfOffsetMap omap = null;
        if (TfMetaUtils.containsOmitSpec(params.get("spec"), colnames)) {
            omap = new TfOffsetMap(SparkUtils.toIndexedLong(in.mapToPair(new RDDTransformApplyOffsetFunction(params.get("spec"), colnames)).collect()));
        }
        // create encoder broadcast (avoiding replication per task)
        Encoder encoder = EncoderFactory.createEncoder(params.get("spec"), colnames, fo.getSchema(), (int) fo.getNumColumns(), meta);
        mcOut.setDimension(mcIn.getRows() - ((omap != null) ? omap.getNumRmRows() : 0), encoder.getNumCols());
        Broadcast<Encoder> bmeta = sec.getSparkContext().broadcast(encoder);
        Broadcast<TfOffsetMap> bomap = (omap != null) ? sec.getSparkContext().broadcast(omap) : null;
        // execute transform apply
        JavaPairRDD<Long, FrameBlock> tmp = in.mapToPair(new RDDTransformApplyFunction(bmeta, bomap));
        JavaPairRDD<MatrixIndexes, MatrixBlock> out = FrameRDDConverterUtils.binaryBlockToMatrixBlock(tmp, mcOut, mcOut);
        // set output and maintain lineage/output characteristics
        sec.setRDDHandleForVariable(output.getName(), out);
        sec.addLineageRDD(output.getName(), params.get("target"));
        ec.releaseFrameInput(params.get("meta"));
    } else if (opcode.equalsIgnoreCase("transformdecode")) {
        // get input RDD and meta data
        JavaPairRDD<MatrixIndexes, MatrixBlock> in = sec.getBinaryBlockRDDHandleForVariable(params.get("target"));
        MatrixCharacteristics mc = sec.getMatrixCharacteristics(params.get("target"));
        FrameBlock meta = sec.getFrameInput(params.get("meta"));
        String[] colnames = meta.getColumnNames();
        // reblock if necessary (clen > bclen)
        if (mc.getCols() > mc.getNumColBlocks()) {
            in = in.mapToPair(new RDDTransformDecodeExpandFunction((int) mc.getCols(), mc.getColsPerBlock()));
            in = RDDAggregateUtils.mergeByKey(in, false);
        }
        // construct decoder and decode individual matrix blocks
        Decoder decoder = DecoderFactory.createDecoder(params.get("spec"), colnames, null, meta);
        JavaPairRDD<Long, FrameBlock> out = in.mapToPair(new RDDTransformDecodeFunction(decoder, mc.getRowsPerBlock()));
        // set output and maintain lineage/output characteristics
        sec.setRDDHandleForVariable(output.getName(), out);
        sec.addLineageRDD(output.getName(), params.get("target"));
        ec.releaseFrameInput(params.get("meta"));
        sec.getMatrixCharacteristics(output.getName()).set(mc.getRows(), meta.getNumColumns(), mc.getRowsPerBlock(), mc.getColsPerBlock(), -1);
        sec.getFrameObject(output.getName()).setSchema(decoder.getSchema());
    } else {
        throw new DMLRuntimeException("Unknown parameterized builtin opcode: " + opcode);
    }
}