Examples with DataType org.apache.sysml.parser.Expression.DataType used on opensource projects

Example 21 with DataType

use of org.apache.sysml.parser.Expression.DataType in project systemml by apache.

the class GenerateClassesForMLContext method getParamTypeAsString.

/**
 * Obtain a string representation of a parameter type, where a Matrix or
 * Frame is represented by its full class name.
 *
 * @param param
 *            the function parameter
 * @return string representation of a parameter type
 */
public static String getParamTypeAsString(DataIdentifier param) {
    DataType dt = param.getDataType();
    ValueType vt = param.getValueType();
    if ((dt == DataType.SCALAR) && (vt == ValueType.INT)) {
        return "long";
    } else if ((dt == DataType.SCALAR) && (vt == ValueType.DOUBLE)) {
        return "double";
    } else if ((dt == DataType.SCALAR) && (vt == ValueType.BOOLEAN)) {
        return "boolean";
    } else if ((dt == DataType.SCALAR) && (vt == ValueType.STRING)) {
        return "String";
    } else if (dt == DataType.MATRIX) {
        return "org.apache.sysml.api.mlcontext.Matrix";
    } else if (dt == DataType.FRAME) {
        return "org.apache.sysml.api.mlcontext.Frame";
    }
    return null;
}

Example 22 with DataType

use of org.apache.sysml.parser.Expression.DataType in project systemml by apache.

the class Recompiler method tryReadMetaDataFileMatrixCharacteristics.

private static void tryReadMetaDataFileMatrixCharacteristics(DataOp dop) {
    try {
        // get meta data filename
        String mtdname = DataExpression.getMTDFileName(dop.getFileName());
        Path path = new Path(mtdname);
        FileSystem fs = IOUtilFunctions.getFileSystem(mtdname);
        if (fs.exists(path)) {
            BufferedReader br = null;
            try {
                br = new BufferedReader(new InputStreamReader(fs.open(path)));
                JSONObject mtd = JSONHelper.parse(br);
                DataType dt = DataType.valueOf(String.valueOf(mtd.get(DataExpression.DATATYPEPARAM)).toUpperCase());
                dop.setDataType(dt);
                if (dt != DataType.FRAME)
                    dop.setValueType(ValueType.valueOf(String.valueOf(mtd.get(DataExpression.VALUETYPEPARAM)).toUpperCase()));
                dop.setDim1((dt == DataType.MATRIX || dt == DataType.FRAME) ? Long.parseLong(mtd.get(DataExpression.READROWPARAM).toString()) : 0);
                dop.setDim2((dt == DataType.MATRIX || dt == DataType.FRAME) ? Long.parseLong(mtd.get(DataExpression.READCOLPARAM).toString()) : 0);
            } finally {
                IOUtilFunctions.closeSilently(br);
            }
        }
    } catch (Exception ex) {
        throw new DMLRuntimeException(ex);
    }
}

Example 23 with DataType

use of org.apache.sysml.parser.Expression.DataType in project systemml by apache.

the class HopRewriteUtils method createUnary.

public static UnaryOp createUnary(Hop input, OpOp1 type) {
    DataType dt = (type == OpOp1.CAST_AS_SCALAR) ? DataType.SCALAR : (type == OpOp1.CAST_AS_MATRIX) ? DataType.MATRIX : input.getDataType();
    ValueType vt = (type == OpOp1.CAST_AS_MATRIX) ? ValueType.DOUBLE : input.getValueType();
    UnaryOp unary = new UnaryOp(input.getName(), dt, vt, type, input);
    unary.setOutputBlocksizes(input.getRowsInBlock(), input.getColsInBlock());
    if (type == OpOp1.CAST_AS_SCALAR || type == OpOp1.CAST_AS_MATRIX) {
        int dim = (type == OpOp1.CAST_AS_SCALAR) ? 0 : 1;
        int blksz = (type == OpOp1.CAST_AS_SCALAR) ? 0 : ConfigurationManager.getBlocksize();
        setOutputParameters(unary, dim, dim, blksz, blksz, -1);
    }
    copyLineNumbers(input, unary);
    unary.refreshSizeInformation();
    return unary;
}

Example 24 with DataType

use of org.apache.sysml.parser.Expression.DataType in project systemml by apache.

the class InterProceduralAnalysis method extractFunctionCallReturnStatistics.

/**
 * Extract return variable statistics from this function into the
 * calling program.
 *
 * @param fstmt  The function statement.
 * @param fop  The function op.
 * @param tmpVars  Function's map of variables eligible for
 *                    extraction.
 * @param callVars  Calling program's map of variables.
 * @param overwrite  Whether or not to overwrite variables in the
 *                      calling program's variable map.
 */
private static void extractFunctionCallReturnStatistics(FunctionStatement fstmt, FunctionOp fop, LocalVariableMap tmpVars, LocalVariableMap callVars, boolean overwrite) {
    ArrayList<DataIdentifier> foutputOps = fstmt.getOutputParams();
    String[] outputVars = fop.getOutputVariableNames();
    String fkey = fop.getFunctionKey();
    try {
        for (int i = 0; i < foutputOps.size(); i++) {
            DataIdentifier di = foutputOps.get(i);
            // name in function signature
            String fvarname = di.getName();
            // name in calling program
            String pvarname = outputVars[i];
            // output, remove that variable from the calling program's variable map.
            if (callVars.keySet().contains(pvarname)) {
                DataType fdataType = di.getDataType();
                DataType pdataType = callVars.get(pvarname).getDataType();
                if (fdataType != pdataType) {
                    // datatype has changed, and the calling program is reassigning the
                    // the variable, so remove it from the calling variable map
                    callVars.remove(pvarname);
                }
            }
            // Update or add to the calling program's variable map.
            if (di.getDataType() == DataType.MATRIX && tmpVars.keySet().contains(fvarname)) {
                MatrixObject moIn = (MatrixObject) tmpVars.get(fvarname);
                if (// not existing so far
                !callVars.keySet().contains(pvarname) || overwrite) {
                    MatrixObject moOut = createOutputMatrix(moIn.getNumRows(), moIn.getNumColumns(), moIn.getNnz());
                    callVars.put(pvarname, moOut);
                } else // already existing: take largest
                {
                    Data dat = callVars.get(pvarname);
                    if (dat instanceof MatrixObject) {
                        MatrixObject moOut = (MatrixObject) dat;
                        MatrixCharacteristics mc = moOut.getMatrixCharacteristics();
                        if (OptimizerUtils.estimateSizeExactSparsity(mc.getRows(), mc.getCols(), (mc.getNonZeros() > 0) ? OptimizerUtils.getSparsity(mc) : 1.0) < OptimizerUtils.estimateSize(moIn.getNumRows(), moIn.getNumColumns())) {
                            // update statistics if necessary
                            mc.setDimension(moIn.getNumRows(), moIn.getNumColumns());
                            mc.setNonZeros(moIn.getNnz());
                        }
                    }
                }
            }
        }
    } catch (Exception ex) {
        throw new HopsException("Failed to extract output statistics of function " + fkey + ".", ex);
    }
}

Example 25 with DataType

use of org.apache.sysml.parser.Expression.DataType in project systemml by apache.

the class ProgramConverter method parseDataObject.

/**
 * NOTE: MRJobConfiguration cannot be used for the general case because program blocks and
 * related symbol tables can be hierarchically structured.
 *
 * @param in data object as string
 * @return array of objects
 */
public static Object[] parseDataObject(String in) {
    Object[] ret = new Object[2];
    StringTokenizer st = new StringTokenizer(in, DATA_FIELD_DELIM);
    String name = st.nextToken();
    DataType datatype = DataType.valueOf(st.nextToken());
    ValueType valuetype = ValueType.valueOf(st.nextToken());
    String valString = st.hasMoreTokens() ? st.nextToken() : "";
    Data dat = null;
    switch(datatype) {
        case SCALAR:
            {
                switch(valuetype) {
                    case INT:
                        dat = new IntObject(Long.parseLong(valString));
                        break;
                    case DOUBLE:
                        dat = new DoubleObject(Double.parseDouble(valString));
                        break;
                    case BOOLEAN:
                        dat = new BooleanObject(Boolean.parseBoolean(valString));
                        break;
                    case STRING:
                        dat = new StringObject(valString);
                        break;
                    default:
                        throw new DMLRuntimeException("Unable to parse valuetype " + valuetype);
                }
                break;
            }
        case MATRIX:
            {
                MatrixObject mo = new MatrixObject(valuetype, valString);
                long rows = Long.parseLong(st.nextToken());
                long cols = Long.parseLong(st.nextToken());
                int brows = Integer.parseInt(st.nextToken());
                int bcols = Integer.parseInt(st.nextToken());
                long nnz = Long.parseLong(st.nextToken());
                InputInfo iin = InputInfo.stringToInputInfo(st.nextToken());
                OutputInfo oin = OutputInfo.stringToOutputInfo(st.nextToken());
                PartitionFormat partFormat = PartitionFormat.valueOf(st.nextToken());
                UpdateType inplace = UpdateType.valueOf(st.nextToken());
                MatrixCharacteristics mc = new MatrixCharacteristics(rows, cols, brows, bcols, nnz);
                MetaDataFormat md = new MetaDataFormat(mc, oin, iin);
                mo.setMetaData(md);
                if (partFormat._dpf != PDataPartitionFormat.NONE)
                    mo.setPartitioned(partFormat._dpf, partFormat._N);
                mo.setUpdateType(inplace);
                dat = mo;
                break;
            }
        default:
            throw new DMLRuntimeException("Unable to parse datatype " + datatype);
    }
    ret[0] = name;
    ret[1] = dat;
    return ret;
}