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

Example 6 with DataType

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

the class BinaryOp method optFindExecType.

@Override
protected ExecType optFindExecType() {
    checkAndSetForcedPlatform();
    ExecType REMOTE = OptimizerUtils.isSparkExecutionMode() ? ExecType.SPARK : ExecType.MR;
    DataType dt1 = getInput().get(0).getDataType();
    DataType dt2 = getInput().get(1).getDataType();
    if (_etypeForced != null) {
        _etype = _etypeForced;
    } else {
        if (OptimizerUtils.isMemoryBasedOptLevel()) {
            _etype = findExecTypeByMemEstimate();
        } else {
            _etype = null;
            if (dt1 == DataType.MATRIX && dt2 == DataType.MATRIX) {
                // OR if both are vectors
                if ((getInput().get(0).areDimsBelowThreshold() && getInput().get(1).areDimsBelowThreshold()) || (getInput().get(0).isVector() && getInput().get(1).isVector())) {
                    _etype = ExecType.CP;
                }
            } else if (dt1 == DataType.MATRIX && dt2 == DataType.SCALAR) {
                if (getInput().get(0).areDimsBelowThreshold() || getInput().get(0).isVector()) {
                    _etype = ExecType.CP;
                }
            } else if (dt1 == DataType.SCALAR && dt2 == DataType.MATRIX) {
                if (getInput().get(1).areDimsBelowThreshold() || getInput().get(1).isVector()) {
                    _etype = ExecType.CP;
                }
            } else {
                _etype = ExecType.CP;
            }
            // if no CP condition applied
            if (_etype == null)
                _etype = REMOTE;
        }
        // check for valid CP dimensions and matrix size
        checkAndSetInvalidCPDimsAndSize();
    }
    // single parent also in spark because it's likely cheap and reduces intermediates)
    if (_etype == ExecType.CP && _etypeForced != ExecType.CP && getDataType().isMatrix() && (dt1.isScalar() || dt2.isScalar()) && // scalar operations
    supportsMatrixScalarOperations() && // input is not checkpoint
    !(getInput().get(dt1.isScalar() ? 1 : 0) instanceof DataOp) && // unary scalar is only parent
    getInput().get(dt1.isScalar() ? 1 : 0).getParent().size() == 1 && // single block triggered exec
    !HopRewriteUtils.isSingleBlock(getInput().get(dt1.isScalar() ? 1 : 0)) && getInput().get(dt1.isScalar() ? 1 : 0).optFindExecType() == ExecType.SPARK) {
        // pull unary scalar operation into spark
        _etype = ExecType.SPARK;
    }
    // mark for recompile (forever)
    setRequiresRecompileIfNecessary();
    // ensure cp exec type for single-node operations
    if (op == OpOp2.SOLVE) {
        if (isGPUEnabled())
            _etype = ExecType.GPU;
        else
            _etype = ExecType.CP;
    }
    return _etype;
}

Example 7 with DataType

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

the class ProgramConverter method serializeDataObject.

public static String serializeDataObject(String key, Data dat) {
    // SCHEMA: <name>|<datatype>|<valuetype>|value
    // (scalars are serialize by value, matrices by filename)
    StringBuilder sb = new StringBuilder();
    // prepare data for serialization
    String name = key;
    DataType datatype = dat.getDataType();
    ValueType valuetype = dat.getValueType();
    String value = null;
    String[] matrixMetaData = null;
    switch(datatype) {
        case SCALAR:
            ScalarObject so = (ScalarObject) dat;
            // name = so.getName();
            value = so.getStringValue();
            break;
        case MATRIX:
            MatrixObject mo = (MatrixObject) dat;
            MetaDataFormat md = (MetaDataFormat) dat.getMetaData();
            MatrixCharacteristics mc = md.getMatrixCharacteristics();
            value = mo.getFileName();
            PartitionFormat partFormat = (mo.getPartitionFormat() != null) ? new PartitionFormat(mo.getPartitionFormat(), mo.getPartitionSize()) : PartitionFormat.NONE;
            matrixMetaData = new String[9];
            matrixMetaData[0] = String.valueOf(mc.getRows());
            matrixMetaData[1] = String.valueOf(mc.getCols());
            matrixMetaData[2] = String.valueOf(mc.getRowsPerBlock());
            matrixMetaData[3] = String.valueOf(mc.getColsPerBlock());
            matrixMetaData[4] = String.valueOf(mc.getNonZeros());
            matrixMetaData[5] = InputInfo.inputInfoToString(md.getInputInfo());
            matrixMetaData[6] = OutputInfo.outputInfoToString(md.getOutputInfo());
            matrixMetaData[7] = String.valueOf(partFormat);
            matrixMetaData[8] = String.valueOf(mo.getUpdateType());
            break;
        default:
            throw new DMLRuntimeException("Unable to serialize datatype " + datatype);
    }
    // serialize data
    sb.append(name);
    sb.append(DATA_FIELD_DELIM);
    sb.append(datatype);
    sb.append(DATA_FIELD_DELIM);
    sb.append(valuetype);
    sb.append(DATA_FIELD_DELIM);
    sb.append(value);
    if (matrixMetaData != null)
        for (int i = 0; i < matrixMetaData.length; i++) {
            sb.append(DATA_FIELD_DELIM);
            sb.append(matrixMetaData[i]);
        }
    return sb.toString();
}

Example 8 with DataType

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

the class ArithmeticBinaryGPUInstruction method parseInstruction.

public static ArithmeticBinaryGPUInstruction parseInstruction(String str) {
    String[] parts = InstructionUtils.getInstructionPartsWithValueType(str);
    InstructionUtils.checkNumFields(parts, 3);
    String opcode = parts[0];
    CPOperand in1 = new CPOperand(parts[1]);
    CPOperand in2 = new CPOperand(parts[2]);
    CPOperand out = new CPOperand(parts[3]);
    DataType dt1 = in1.getDataType();
    DataType dt2 = in2.getDataType();
    DataType dt3 = out.getDataType();
    Operator operator = (dt1 != dt2) ? InstructionUtils.parseScalarBinaryOperator(opcode, (dt1 == DataType.SCALAR)) : InstructionUtils.parseBinaryOperator(opcode);
    if (dt1 == DataType.MATRIX && dt2 == DataType.MATRIX && dt3 == DataType.MATRIX) {
        return new MatrixMatrixArithmeticGPUInstruction(operator, in1, in2, out, opcode, str);
    } else if (dt3 == DataType.MATRIX && ((dt1 == DataType.SCALAR && dt2 == DataType.MATRIX) || (dt1 == DataType.MATRIX && dt2 == DataType.SCALAR))) {
        return new ScalarMatrixArithmeticGPUInstruction(operator, in1, in2, out, opcode, str);
    } else
        throw new DMLRuntimeException("Unsupported GPU ArithmeticInstruction.");
}

Example 9 with DataType

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

the class ScalarInstruction method isFirstArgumentScalar.

private static boolean isFirstArgumentScalar(String inst) {
    // get first argument
    String[] parts = InstructionUtils.getInstructionPartsWithValueType(inst);
    String arg1 = parts[1];
    // get data type of first argument
    String[] subparts = arg1.split(Lop.VALUETYPE_PREFIX);
    DataType dt = DataType.valueOf(subparts[1]);
    return (dt == DataType.SCALAR);
}

Example 10 with DataType

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

the class TemplateCell method isValidOperation.

protected static boolean isValidOperation(Hop hop) {
    // prepare indicators for binary operations
    boolean isBinaryMatrixScalar = false;
    boolean isBinaryMatrixVector = false;
    boolean isBinaryMatrixMatrix = false;
    if (hop instanceof BinaryOp && hop.getDataType().isMatrix()) {
        Hop left = hop.getInput().get(0);
        Hop right = hop.getInput().get(1);
        DataType ldt = left.getDataType();
        DataType rdt = right.getDataType();
        isBinaryMatrixScalar = (ldt.isScalar() || rdt.isScalar());
        isBinaryMatrixVector = hop.dimsKnown() && ((ldt.isMatrix() && TemplateUtils.isVectorOrScalar(right)) || (rdt.isMatrix() && TemplateUtils.isVectorOrScalar(left)));
        isBinaryMatrixMatrix = hop.dimsKnown() && HopRewriteUtils.isEqualSize(left, right) && ldt.isMatrix() && rdt.isMatrix();
    }
    // prepare indicators for ternary operations
    boolean isTernaryVectorScalarVector = false;
    boolean isTernaryMatrixScalarMatrixDense = false;
    boolean isTernaryIfElse = (HopRewriteUtils.isTernary(hop, OpOp3.IFELSE) && hop.getDataType().isMatrix());
    if (hop instanceof TernaryOp && hop.getInput().size() == 3 && hop.dimsKnown() && HopRewriteUtils.checkInputDataTypes(hop, DataType.MATRIX, DataType.SCALAR, DataType.MATRIX)) {
        Hop left = hop.getInput().get(0);
        Hop right = hop.getInput().get(2);
        isTernaryVectorScalarVector = TemplateUtils.isVector(left) && TemplateUtils.isVector(right);
        isTernaryMatrixScalarMatrixDense = HopRewriteUtils.isEqualSize(left, right) && !HopRewriteUtils.isSparse(left) && !HopRewriteUtils.isSparse(right);
    }
    // check supported unary, binary, ternary operations
    return hop.getDataType() == DataType.MATRIX && TemplateUtils.isOperationSupported(hop) && (hop instanceof UnaryOp || isBinaryMatrixScalar || isBinaryMatrixVector || isBinaryMatrixMatrix || isTernaryVectorScalarVector || isTernaryMatrixScalarMatrixDense || isTernaryIfElse || (hop instanceof ParameterizedBuiltinOp && ((ParameterizedBuiltinOp) hop).getOp() == ParamBuiltinOp.REPLACE));
}