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

Example 11 with DataType

use of org.apache.sysml.parser.Expression.DataType in project incubator-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 12 with DataType

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

the class ParForStatementBlock method rCheckCandidates.

/**
 * This method recursively checks a candidate against StatementBlocks for anti, data and output dependencies.
 * A LanguageException is raised if at least one dependency is found, where it is guaranteed that no false negatives
 * (undetected dependency) but potentially false positives (misdetected dependency) can appear.
 *
 * @param c candidate
 * @param cdt candidate data type
 * @param asb list of statement blocks
 * @param sCount statement count
 * @param dep array of boolean potential output dependencies
 */
private void rCheckCandidates(Candidate c, DataType cdt, ArrayList<StatementBlock> asb, Integer sCount, boolean[] dep) {
    // check candidate only (output dependency if scalar or constant matrix subscript)
    if (cdt == DataType.SCALAR || // dat2 checked for other candidate
    cdt == DataType.OBJECT) {
        // every write to a scalar or complete data object is an output dependency
        dep[0] = true;
        if (ABORT_ON_FIRST_DEPENDENCY)
            return;
    } else if (cdt == DataType.MATRIX) {
        if (runConstantCheck(c._dat) && !c._isAccum) {
            if (LOG.isTraceEnabled())
                LOG.trace("PARFOR: Possible output dependency detected via constant self-check: var '" + c._var + "'.");
            dep[0] = true;
            if (ABORT_ON_FIRST_DEPENDENCY)
                return;
        }
    }
    // check candidate against all statements
    for (StatementBlock sb : asb) for (Statement s : sb._statements) {
        sCount++;
        if (s instanceof ForStatement) {
            // incl parfor
            // despite separate dependency analysis for each nested parfor, we need to
            // recursively check nested parfor as well in order to ensure correcteness
            // of constantChecks with regard to outer indexes
            rCheckCandidates(c, cdt, ((ForStatement) s).getBody(), sCount, dep);
        } else if (s instanceof WhileStatement) {
            rCheckCandidates(c, cdt, ((WhileStatement) s).getBody(), sCount, dep);
        } else if (s instanceof IfStatement) {
            rCheckCandidates(c, cdt, ((IfStatement) s).getIfBody(), sCount, dep);
            rCheckCandidates(c, cdt, ((IfStatement) s).getElseBody(), sCount, dep);
        } else if (s instanceof FunctionStatement) {
            rCheckCandidates(c, cdt, ((FunctionStatement) s).getBody(), sCount, dep);
        } else {
            // CHECK output dependencies
            List<DataIdentifier> datsUpdated = getDataIdentifiers(s, true);
            if (datsUpdated != null) {
                for (DataIdentifier write : datsUpdated) {
                    if (!c._var.equals(write.getName()))
                        continue;
                    if (cdt != DataType.MATRIX) {
                        // cannot infer type, need to exit (conservative approach)
                        throw new LanguageException("PARFOR loop dependency analysis: " + "cannot check for dependencies due to unknown datatype of var '" + c._var + "'.");
                    }
                    DataIdentifier dat2 = write;
                    // skip self-check
                    if (c._dat == dat2)
                        continue;
                    if (runEqualsCheck(c._dat, dat2)) {
                    // intra-iteration output dependencies (same index function) are OK
                    } else if (runBanerjeeGCDTest(c._dat, dat2)) {
                        LOG.trace("PARFOR: Possible output dependency detected via GCD/Banerjee: var '" + write + "'.");
                        dep[0] = true;
                        if (ABORT_ON_FIRST_DEPENDENCY)
                            return;
                    }
                }
            }
            List<DataIdentifier> datsRead = getDataIdentifiers(s, false);
            if (datsRead == null)
                continue;
            // check data and anti dependencies
            for (DataIdentifier read : datsRead) {
                if (!c._var.equals(read.getName()))
                    continue;
                DataIdentifier dat2 = read;
                DataType dat2dt = _vsParent.getVariables().get(read.getName()).getDataType();
                if (cdt == DataType.SCALAR || cdt == DataType.OBJECT || dat2dt == DataType.SCALAR || dat2dt == DataType.OBJECT) {
                    // every write, read combination involving a scalar is a data dependency
                    dep[1] = true;
                    if (ABORT_ON_FIRST_DEPENDENCY)
                        return;
                } else if (cdt == DataType.MATRIX && dat2dt == DataType.MATRIX) {
                    boolean invalid = false;
                    if (runEqualsCheck(c._dat, dat2))
                        // read/write on same index, and not constant (checked for output) is OK
                        invalid = runConstantCheck(dat2);
                    else if (runBanerjeeGCDTest(c._dat, dat2))
                        invalid = true;
                    else if (!(dat2 instanceof IndexedIdentifier))
                        // non-indexed access to candidate result variable -> always a dependency
                        invalid = true;
                    if (invalid) {
                        LOG.trace("PARFOR: Possible data/anti dependency detected via GCD/Banerjee: var '" + read + "'.");
                        dep[1] = true;
                        dep[2] = true;
                        if (ABORT_ON_FIRST_DEPENDENCY)
                            return;
                    }
                } else {
                    // cannot infer type, need to exit (conservative approach)
                    throw new LanguageException("PARFOR loop dependency analysis: " + "cannot check for dependencies due to unknown datatype of var '" + c._var + "'.");
                }
            }
        }
    }
}

Example 13 with DataType

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

the class ParForProgramBlock method createEmptyUnscopedVariables.

/**
 * Create empty matrix objects and scalars for all unscoped vars
 * (created within the parfor).
 *
 * NOTE: parfor gives no guarantees on the values of those objects - hence
 * we return -1 for sclars and empty matrix objects.
 *
 * @param out local variable map
 * @param sb statement block
 */
private static void createEmptyUnscopedVariables(LocalVariableMap out, StatementBlock sb) {
    VariableSet updated = sb.variablesUpdated();
    VariableSet livein = sb.liveIn();
    // for all vars IN <updated> AND NOT IN <livein>
    for (String var : updated.getVariableNames()) if (!livein.containsVariable(var)) {
        // create empty output
        DataIdentifier dat = updated.getVariable(var);
        DataType datatype = dat.getDataType();
        ValueType valuetype = dat.getValueType();
        Data dataObj = null;
        switch(datatype) {
            case SCALAR:
                switch(valuetype) {
                    case BOOLEAN:
                        dataObj = new BooleanObject(false);
                        break;
                    case INT:
                        dataObj = new IntObject(-1);
                        break;
                    case DOUBLE:
                        dataObj = new DoubleObject(-1d);
                        break;
                    case STRING:
                        dataObj = new StringObject("-1");
                        break;
                    default:
                        throw new DMLRuntimeException("Value type not supported: " + valuetype);
                }
                break;
            case MATRIX:
            case FRAME:
                // because metadata (e.g., outputinfo) not known at this place.
                break;
            case UNKNOWN:
                break;
            default:
                throw new DMLRuntimeException("Data type not supported: " + datatype);
        }
        if (dataObj != null)
            out.put(var, dataObj);
    }
}

Example 14 with DataType

use of org.apache.sysml.parser.Expression.DataType in project incubator-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 15 with DataType

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

the class GenerateClassesForMLContext method getSimpleParamTypeAsString.

/**
 * Obtain a string representation of a parameter type, where a Matrix or
 * Frame is represented by its simple class name.
 *
 * @param param
 *            the function parameter
 * @return string representation of a parameter type
 */
public static String getSimpleParamTypeAsString(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 "Matrix";
    } else if (dt == DataType.FRAME) {
        return "Frame";
    }
    return null;
}