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Example 1 with BinaryM

use of org.apache.sysml.lops.BinaryM in project incubator-systemml by apache.

the class BinaryOp method constructLopsBinaryDefault.

private void constructLopsBinaryDefault() throws HopsException, LopsException {
    /* Default behavior for BinaryOp */
    // it depends on input data types
    DataType dt1 = getInput().get(0).getDataType();
    DataType dt2 = getInput().get(1).getDataType();
    if (dt1 == dt2 && dt1 == DataType.SCALAR) {
        // Both operands scalar
        BinaryScalar binScalar1 = new BinaryScalar(getInput().get(0).constructLops(), getInput().get(1).constructLops(), HopsOpOp2LopsBS.get(op), getDataType(), getValueType());
        binScalar1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
        setLineNumbers(binScalar1);
        setLops(binScalar1);
    } else if ((dt1 == DataType.MATRIX && dt2 == DataType.SCALAR) || (dt1 == DataType.SCALAR && dt2 == DataType.MATRIX)) {
        // One operand is Matrix and the other is scalar
        ExecType et = optFindExecType();
        //select specific operator implementations
        Unary.OperationTypes ot = null;
        Hop right = getInput().get(1);
        if (op == OpOp2.POW && right instanceof LiteralOp && ((LiteralOp) right).getDoubleValue() == 2.0)
            ot = Unary.OperationTypes.POW2;
        else if (op == OpOp2.MULT && right instanceof LiteralOp && ((LiteralOp) right).getDoubleValue() == 2.0)
            ot = Unary.OperationTypes.MULTIPLY2;
        else
            //general case
            ot = HopsOpOp2LopsU.get(op);
        if (DMLScript.USE_ACCELERATOR && (DMLScript.FORCE_ACCELERATOR || getMemEstimate() < OptimizerUtils.GPU_MEMORY_BUDGET) && (op == OpOp2.MULT || op == OpOp2.PLUS || op == OpOp2.MINUS || op == OpOp2.DIV || op == OpOp2.POW)) {
            et = ExecType.GPU;
        }
        Unary unary1 = new Unary(getInput().get(0).constructLops(), getInput().get(1).constructLops(), ot, getDataType(), getValueType(), et);
        setOutputDimensions(unary1);
        setLineNumbers(unary1);
        setLops(unary1);
    } else {
        // Both operands are Matrixes
        ExecType et = optFindExecType();
        if (et == ExecType.CP) {
            if (DMLScript.USE_ACCELERATOR && (DMLScript.FORCE_ACCELERATOR || getMemEstimate() < OptimizerUtils.GPU_MEMORY_BUDGET) && (op == OpOp2.MULT || op == OpOp2.PLUS || op == OpOp2.MINUS || op == OpOp2.DIV || op == OpOp2.POW || op == OpOp2.SOLVE)) {
                et = ExecType.GPU;
            }
            Lop binary = null;
            boolean isLeftXGt = (getInput().get(0) instanceof BinaryOp) && ((BinaryOp) getInput().get(0)).getOp() == OpOp2.GREATER;
            Hop potentialZero = isLeftXGt ? ((BinaryOp) getInput().get(0)).getInput().get(1) : null;
            boolean isLeftXGt0 = isLeftXGt && potentialZero != null && potentialZero instanceof LiteralOp && ((LiteralOp) potentialZero).getDoubleValue() == 0;
            if (op == OpOp2.MULT && isLeftXGt0 && !getInput().get(0).isVector() && !getInput().get(1).isVector() && getInput().get(0).dimsKnown() && getInput().get(1).dimsKnown()) {
                binary = new ConvolutionTransform(getInput().get(0).getInput().get(0).constructLops(), getInput().get(1).constructLops(), ConvolutionTransform.OperationTypes.RELU_BACKWARD, getDataType(), getValueType(), et, -1);
            } else
                binary = new Binary(getInput().get(0).constructLops(), getInput().get(1).constructLops(), HopsOpOp2LopsB.get(op), getDataType(), getValueType(), et);
            setOutputDimensions(binary);
            setLineNumbers(binary);
            setLops(binary);
        } else if (et == ExecType.SPARK) {
            Hop left = getInput().get(0);
            Hop right = getInput().get(1);
            MMBinaryMethod mbin = optFindMMBinaryMethodSpark(left, right);
            Lop binary = null;
            if (mbin == MMBinaryMethod.MR_BINARY_UAGG_CHAIN) {
                AggUnaryOp uRight = (AggUnaryOp) right;
                binary = new BinaryUAggChain(left.constructLops(), HopsOpOp2LopsB.get(op), HopsAgg2Lops.get(uRight.getOp()), HopsDirection2Lops.get(uRight.getDirection()), getDataType(), getValueType(), et);
            } else if (mbin == MMBinaryMethod.MR_BINARY_M) {
                boolean partitioned = false;
                boolean isColVector = (right.getDim2() == 1 && left.getDim1() == right.getDim1());
                binary = new BinaryM(left.constructLops(), right.constructLops(), HopsOpOp2LopsB.get(op), getDataType(), getValueType(), et, partitioned, isColVector);
            } else {
                binary = new Binary(left.constructLops(), right.constructLops(), HopsOpOp2LopsB.get(op), getDataType(), getValueType(), et);
            }
            setOutputDimensions(binary);
            setLineNumbers(binary);
            setLops(binary);
        } else //MR
        {
            Hop left = getInput().get(0);
            Hop right = getInput().get(1);
            MMBinaryMethod mbin = optFindMMBinaryMethod(left, right);
            if (mbin == MMBinaryMethod.MR_BINARY_M) {
                boolean needPart = requiresPartitioning(right);
                Lop dcInput = right.constructLops();
                if (needPart) {
                    //right side in distributed cache
                    ExecType etPart = (OptimizerUtils.estimateSizeExactSparsity(right.getDim1(), right.getDim2(), OptimizerUtils.getSparsity(right.getDim1(), right.getDim2(), right.getNnz())) < OptimizerUtils.getLocalMemBudget()) ? ExecType.CP : //operator selection
                    ExecType.MR;
                    dcInput = new DataPartition(dcInput, DataType.MATRIX, ValueType.DOUBLE, etPart, (right.getDim2() == 1) ? PDataPartitionFormat.ROW_BLOCK_WISE_N : PDataPartitionFormat.COLUMN_BLOCK_WISE_N);
                    dcInput.getOutputParameters().setDimensions(right.getDim1(), right.getDim2(), right.getRowsInBlock(), right.getColsInBlock(), right.getNnz());
                    dcInput.setAllPositions(right.getBeginLine(), right.getBeginColumn(), right.getEndLine(), right.getEndColumn());
                }
                BinaryM binary = new BinaryM(left.constructLops(), dcInput, HopsOpOp2LopsB.get(op), getDataType(), getValueType(), ExecType.MR, needPart, (right.getDim2() == 1 && left.getDim1() == right.getDim1()));
                setOutputDimensions(binary);
                setLineNumbers(binary);
                setLops(binary);
            } else if (mbin == MMBinaryMethod.MR_BINARY_UAGG_CHAIN) {
                AggUnaryOp uRight = (AggUnaryOp) right;
                BinaryUAggChain bin = new BinaryUAggChain(left.constructLops(), HopsOpOp2LopsB.get(op), HopsAgg2Lops.get(uRight.getOp()), HopsDirection2Lops.get(uRight.getDirection()), getDataType(), getValueType(), et);
                setOutputDimensions(bin);
                setLineNumbers(bin);
                setLops(bin);
            } else if (mbin == MMBinaryMethod.MR_BINARY_OUTER_R) {
                boolean requiresRepLeft = (!right.dimsKnown() || right.getDim2() > right.getColsInBlock());
                boolean requiresRepRight = (!left.dimsKnown() || left.getDim1() > right.getRowsInBlock());
                Lop leftLop = left.constructLops();
                Lop rightLop = right.constructLops();
                if (requiresRepLeft) {
                    //ncol of right determines rep of left
                    Lop offset = createOffsetLop(right, true);
                    leftLop = new RepMat(leftLop, offset, true, left.getDataType(), left.getValueType());
                    setOutputDimensions(leftLop);
                    setLineNumbers(leftLop);
                }
                if (requiresRepRight) {
                    //nrow of right determines rep of right
                    Lop offset = createOffsetLop(left, false);
                    rightLop = new RepMat(rightLop, offset, false, right.getDataType(), right.getValueType());
                    setOutputDimensions(rightLop);
                    setLineNumbers(rightLop);
                }
                Group group1 = new Group(leftLop, Group.OperationTypes.Sort, getDataType(), getValueType());
                setLineNumbers(group1);
                setOutputDimensions(group1);
                Group group2 = new Group(rightLop, Group.OperationTypes.Sort, getDataType(), getValueType());
                setLineNumbers(group2);
                setOutputDimensions(group2);
                Binary binary = new Binary(group1, group2, HopsOpOp2LopsB.get(op), getDataType(), getValueType(), et);
                setOutputDimensions(binary);
                setLineNumbers(binary);
                setLops(binary);
            } else //MMBinaryMethod.MR_BINARY_R
            {
                boolean requiresRep = requiresReplication(left, right);
                Lop rightLop = right.constructLops();
                if (requiresRep) {
                    //ncol of left input (determines num replicates)
                    Lop offset = createOffsetLop(left, (right.getDim2() <= 1));
                    rightLop = new RepMat(rightLop, offset, (right.getDim2() <= 1), right.getDataType(), right.getValueType());
                    setOutputDimensions(rightLop);
                    setLineNumbers(rightLop);
                }
                Group group1 = new Group(getInput().get(0).constructLops(), Group.OperationTypes.Sort, getDataType(), getValueType());
                setLineNumbers(group1);
                setOutputDimensions(group1);
                Group group2 = new Group(rightLop, Group.OperationTypes.Sort, getDataType(), getValueType());
                setLineNumbers(group2);
                setOutputDimensions(group2);
                Binary binary = new Binary(group1, group2, HopsOpOp2LopsB.get(op), getDataType(), getValueType(), et);
                setLineNumbers(binary);
                setOutputDimensions(binary);
                setLops(binary);
            }
        }
    }
}
Also used : Group(org.apache.sysml.lops.Group) BinaryM(org.apache.sysml.lops.BinaryM) BinaryUAggChain(org.apache.sysml.lops.BinaryUAggChain) Lop(org.apache.sysml.lops.Lop) Unary(org.apache.sysml.lops.Unary) CombineUnary(org.apache.sysml.lops.CombineUnary) RepMat(org.apache.sysml.lops.RepMat) OperationTypes(org.apache.sysml.lops.CombineBinary.OperationTypes) DataType(org.apache.sysml.parser.Expression.DataType) ExecType(org.apache.sysml.lops.LopProperties.ExecType) Binary(org.apache.sysml.lops.Binary) CombineBinary(org.apache.sysml.lops.CombineBinary) BinaryScalar(org.apache.sysml.lops.BinaryScalar) ConvolutionTransform(org.apache.sysml.lops.ConvolutionTransform) DataPartition(org.apache.sysml.lops.DataPartition)

Example 2 with BinaryM

use of org.apache.sysml.lops.BinaryM in project incubator-systemml by apache.

the class Dag method computeFootprintInMapper.

/**
	 * Computes the memory footprint required to execute <code>node</code> in the mapper.
	 * It is used only for those nodes that use inputs from distributed cache. The returned 
	 * value is utilized in limiting the number of instructions piggybacked onto a single GMR mapper.
	 * 
	 * @param node low-level operator
	 * @return memory footprint
	 */
private static double computeFootprintInMapper(Lop node) {
    // Memory limits must be checked only for nodes that use distributed cache
    if (!node.usesDistributedCache())
        // default behavior
        return 0.0;
    OutputParameters in1dims = node.getInputs().get(0).getOutputParameters();
    OutputParameters in2dims = node.getInputs().get(1).getOutputParameters();
    double footprint = 0;
    if (node instanceof MapMult) {
        int dcInputIndex = node.distributedCacheInputIndex()[0];
        footprint = AggBinaryOp.getMapmmMemEstimate(in1dims.getNumRows(), in1dims.getNumCols(), in1dims.getRowsInBlock(), in1dims.getColsInBlock(), in1dims.getNnz(), in2dims.getNumRows(), in2dims.getNumCols(), in2dims.getRowsInBlock(), in2dims.getColsInBlock(), in2dims.getNnz(), dcInputIndex, false);
    } else if (node instanceof PMMJ) {
        int dcInputIndex = node.distributedCacheInputIndex()[0];
        footprint = AggBinaryOp.getMapmmMemEstimate(in1dims.getNumRows(), 1, in1dims.getRowsInBlock(), in1dims.getColsInBlock(), in1dims.getNnz(), in2dims.getNumRows(), in2dims.getNumCols(), in2dims.getRowsInBlock(), in2dims.getColsInBlock(), in2dims.getNnz(), dcInputIndex, true);
    } else if (node instanceof AppendM) {
        footprint = BinaryOp.footprintInMapper(in1dims.getNumRows(), in1dims.getNumCols(), in2dims.getNumRows(), in2dims.getNumCols(), in1dims.getRowsInBlock(), in1dims.getColsInBlock());
    } else if (node instanceof BinaryM) {
        footprint = BinaryOp.footprintInMapper(in1dims.getNumRows(), in1dims.getNumCols(), in2dims.getNumRows(), in2dims.getNumCols(), in1dims.getRowsInBlock(), in1dims.getColsInBlock());
    } else {
        // default behavior
        return 0.0;
    }
    return footprint;
}
Also used : OutputParameters(org.apache.sysml.lops.OutputParameters) MapMult(org.apache.sysml.lops.MapMult) BinaryM(org.apache.sysml.lops.BinaryM) AppendM(org.apache.sysml.lops.AppendM) PMMJ(org.apache.sysml.lops.PMMJ)

Aggregations

BinaryM (org.apache.sysml.lops.BinaryM)2 AppendM (org.apache.sysml.lops.AppendM)1 Binary (org.apache.sysml.lops.Binary)1 BinaryScalar (org.apache.sysml.lops.BinaryScalar)1 BinaryUAggChain (org.apache.sysml.lops.BinaryUAggChain)1 CombineBinary (org.apache.sysml.lops.CombineBinary)1 OperationTypes (org.apache.sysml.lops.CombineBinary.OperationTypes)1 CombineUnary (org.apache.sysml.lops.CombineUnary)1 ConvolutionTransform (org.apache.sysml.lops.ConvolutionTransform)1 DataPartition (org.apache.sysml.lops.DataPartition)1 Group (org.apache.sysml.lops.Group)1 Lop (org.apache.sysml.lops.Lop)1 ExecType (org.apache.sysml.lops.LopProperties.ExecType)1 MapMult (org.apache.sysml.lops.MapMult)1 OutputParameters (org.apache.sysml.lops.OutputParameters)1 PMMJ (org.apache.sysml.lops.PMMJ)1 RepMat (org.apache.sysml.lops.RepMat)1 Unary (org.apache.sysml.lops.Unary)1 DataType (org.apache.sysml.parser.Expression.DataType)1