Examples with RepMat org.apache.sysml.lops.RepMat used on opensource projects

Example 1 with RepMat

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

the class TernaryOp method constructLopsPlusMult.

private void constructLopsPlusMult() throws HopsException, LopsException {
    if (_op != OpOp3.PLUS_MULT && _op != OpOp3.MINUS_MULT)
        throw new HopsException("Unexpected operation: " + _op + ", expecting " + OpOp3.PLUS_MULT + " or" + OpOp3.MINUS_MULT);
    ExecType et = null;
    if (DMLScript.USE_ACCELERATOR && (DMLScript.FORCE_ACCELERATOR || getMemEstimate() < OptimizerUtils.GPU_MEMORY_BUDGET))
        et = ExecType.GPU;
    else
        et = optFindExecType();
    PlusMult plusmult = null;
    if (et == ExecType.CP || et == ExecType.SPARK || et == ExecType.GPU) {
        plusmult = new PlusMult(getInput().get(0).constructLops(), getInput().get(1).constructLops(), getInput().get(2).constructLops(), _op, getDataType(), getValueType(), et);
    } else {
        //MR
        Hop left = getInput().get(0);
        Hop right = getInput().get(2);
        boolean requiresRep = BinaryOp.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(left.constructLops(), Group.OperationTypes.Sort, getDataType(), getValueType());
        setLineNumbers(group1);
        setOutputDimensions(group1);
        Group group2 = new Group(rightLop, Group.OperationTypes.Sort, getDataType(), getValueType());
        setLineNumbers(group2);
        setOutputDimensions(group2);
        plusmult = new PlusMult(group1, getInput().get(1).constructLops(), group2, _op, getDataType(), getValueType(), et);
    }
    setOutputDimensions(plusmult);
    setLineNumbers(plusmult);
    setLops(plusmult);
}

Example 2 with RepMat

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

the class ParameterizedBuiltinOp method constructLopsRemoveEmpty.

private void constructLopsRemoveEmpty(HashMap<String, Lop> inputlops, ExecType et) {
    Hop targetHop = getTargetHop();
    Hop marginHop = getParameterHop("margin");
    Hop selectHop = getParameterHop("select");
    Hop emptyRet = getParameterHop("empty.return");
    if (et == ExecType.CP || et == ExecType.CP_FILE) {
        ParameterizedBuiltin pbilop = new ParameterizedBuiltin(inputlops, HopsParameterizedBuiltinLops.get(_op), getDataType(), getValueType(), et);
        setOutputDimensions(pbilop);
        setLineNumbers(pbilop);
        setLops(pbilop);
    /*DISABLED CP PMM (see for example, MDA Bivar test, requires size propagation on recompile)
			if( et == ExecType.CP && isTargetDiagInput() && marginHop instanceof LiteralOp 
					 && ((LiteralOp)marginHop).getStringValue().equals("rows")
					 && _outputPermutationMatrix ) //SPECIAL CASE SELECTION VECTOR
			{				
				//TODO this special case could be taken into account for memory estimates in order
				// to reduce the estimates for the input diag and subsequent matrix multiply
				
				//get input vector (without materializing diag())
				Hop input = targetHop.getInput().get(0);
				long brlen = input.getRowsInBlock();
				long bclen = input.getColsInBlock();
				MemoTable memo = new MemoTable();
			
				boolean isPPredInput = (input instanceof BinaryOp && ((BinaryOp)input).isPPredOperation());
				
				//step1: compute index vectors
				Hop ppred0 = input;
				if( !isPPredInput ) { //ppred only if required
					ppred0 = new BinaryOp("tmp1", DataType.MATRIX, ValueType.DOUBLE, OpOp2.NOTEQUAL, input, new LiteralOp("0",0));
					HopRewriteUtils.setOutputBlocksizes(ppred0, brlen, bclen);
					ppred0.refreshSizeInformation();
					ppred0.computeMemEstimate(memo); //select exec type
					HopRewriteUtils.copyLineNumbers(this, ppred0);
				}
				
				UnaryOp cumsum = new UnaryOp("tmp2", DataType.MATRIX, ValueType.DOUBLE, OpOp1.CUMSUM, ppred0); 
				HopRewriteUtils.setOutputBlocksizes(cumsum, brlen, bclen);
				cumsum.refreshSizeInformation(); 
				cumsum.computeMemEstimate(memo); //select exec type
				HopRewriteUtils.copyLineNumbers(this, cumsum);	
			
				BinaryOp sel = new BinaryOp("tmp3", DataType.MATRIX, ValueType.DOUBLE, OpOp2.MULT, ppred0, cumsum);
				HopRewriteUtils.setOutputBlocksizes(sel, brlen, bclen); 
				sel.refreshSizeInformation();
				sel.computeMemEstimate(memo); //select exec type
				HopRewriteUtils.copyLineNumbers(this, sel);
				Lop loutput = sel.constructLops();
				
				//Step 4: cleanup hops (allow for garbage collection)
				HopRewriteUtils.removeChildReference(ppred0, input);
				
				setLops( loutput );
			}
			else //GENERAL CASE
			{
				ParameterizedBuiltin pbilop = new ParameterizedBuiltin( et, inputlops,
						HopsParameterizedBuiltinLops.get(_op), getDataType(), getValueType());
				
				pbilop.getOutputParameters().setDimensions(getDim1(),getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
				setLineNumbers(pbilop);
				setLops(pbilop);
			}
			*/
    } else if (et == ExecType.MR) {
        // special compile for mr removeEmpty-diag
        if (isTargetDiagInput() && HopRewriteUtils.isLiteralOfValue(marginHop, "rows")) {
            // get input vector (without materializing diag())
            Hop input = targetHop.getInput().get(0);
            int brlen = input.getRowsInBlock();
            int bclen = input.getColsInBlock();
            MemoTable memo = new MemoTable();
            boolean isPPredInput = (input instanceof BinaryOp && ((BinaryOp) input).isPPredOperation());
            // step1: compute index vectors
            Hop ppred0 = input;
            if (!isPPredInput) {
                // ppred only if required
                ppred0 = HopRewriteUtils.createBinary(input, new LiteralOp(0), OpOp2.NOTEQUAL);
                HopRewriteUtils.updateHopCharacteristics(ppred0, brlen, bclen, memo, this);
            }
            UnaryOp cumsum = HopRewriteUtils.createUnary(ppred0, OpOp1.CUMSUM);
            HopRewriteUtils.updateHopCharacteristics(cumsum, brlen, bclen, memo, this);
            Lop loutput = null;
            double mest = AggBinaryOp.getMapmmMemEstimate(input.getDim1(), 1, brlen, bclen, -1, brlen, bclen, brlen, bclen, -1, 1, true);
            double mbudget = OptimizerUtils.getRemoteMemBudgetMap(true);
            if (// SPECIAL CASE: SELECTION VECTOR
            _outputPermutationMatrix && mest < mbudget) {
                BinaryOp sel = HopRewriteUtils.createBinary(ppred0, cumsum, OpOp2.MULT);
                HopRewriteUtils.updateHopCharacteristics(sel, brlen, bclen, memo, this);
                loutput = sel.constructLops();
            } else // GENERAL CASE: GENERAL PERMUTATION MATRIX
            {
                // max ensures non-zero entries and at least one output row
                BinaryOp max = HopRewriteUtils.createBinary(cumsum, new LiteralOp(1), OpOp2.MAX);
                HopRewriteUtils.updateHopCharacteristics(max, brlen, bclen, memo, this);
                DataGenOp seq = HopRewriteUtils.createSeqDataGenOp(input);
                seq.setName("tmp4");
                HopRewriteUtils.updateHopCharacteristics(seq, brlen, bclen, memo, this);
                // step 2: compute removeEmpty(rows) output via table, seq guarantees right column dimension
                // note: weights always the input (even if isPPredInput) because input also includes 0s
                TernaryOp table = new TernaryOp("tmp5", DataType.MATRIX, ValueType.DOUBLE, OpOp3.CTABLE, max, seq, input);
                table.setOutputBlocksizes(brlen, bclen);
                table.refreshSizeInformation();
                // force MR
                table.setForcedExecType(ExecType.MR);
                HopRewriteUtils.copyLineNumbers(this, table);
                table.setDisjointInputs(true);
                table.setOutputEmptyBlocks(_outputEmptyBlocks);
                loutput = table.constructLops();
                HopRewriteUtils.removeChildReference(table, input);
            }
            // Step 4: cleanup hops (allow for garbage collection)
            HopRewriteUtils.removeChildReference(ppred0, input);
            setLops(loutput);
        } else // default mr remove empty
        if (et == ExecType.MR) {
            if (!(marginHop instanceof LiteralOp))
                throw new HopsException("Parameter 'margin' must be a literal argument.");
            Hop input = targetHop;
            long rlen = input.getDim1();
            long clen = input.getDim2();
            int brlen = input.getRowsInBlock();
            int bclen = input.getColsInBlock();
            long nnz = input.getNnz();
            boolean rmRows = ((LiteralOp) marginHop).getStringValue().equals("rows");
            // construct lops via new partial hop dag and subsequent lops construction
            // in order to reuse of operator selection decisions
            BinaryOp ppred0 = null;
            Hop emptyInd = null;
            if (selectHop == null) {
                // Step1: compute row/col non-empty indicators
                ppred0 = HopRewriteUtils.createBinary(input, new LiteralOp(0), OpOp2.NOTEQUAL);
                // always MR
                ppred0.setForcedExecType(ExecType.MR);
                emptyInd = ppred0;
                if (!((rmRows && clen == 1) || (!rmRows && rlen == 1))) {
                    emptyInd = HopRewriteUtils.createAggUnaryOp(ppred0, AggOp.MAX, rmRows ? Direction.Row : Direction.Col);
                    // always MR
                    emptyInd.setForcedExecType(ExecType.MR);
                    HopRewriteUtils.copyLineNumbers(this, emptyInd);
                }
            } else {
                emptyInd = selectHop;
            }
            // Step 2: compute row offsets for non-empty rows
            Hop cumsumInput = emptyInd;
            if (!rmRows) {
                cumsumInput = HopRewriteUtils.createTranspose(emptyInd);
                HopRewriteUtils.updateHopCharacteristics(cumsumInput, brlen, bclen, this);
            }
            UnaryOp cumsum = HopRewriteUtils.createUnary(cumsumInput, OpOp1.CUMSUM);
            HopRewriteUtils.updateHopCharacteristics(cumsum, brlen, bclen, this);
            Hop cumsumOutput = cumsum;
            if (!rmRows) {
                cumsumOutput = HopRewriteUtils.createTranspose(cumsum);
                HopRewriteUtils.updateHopCharacteristics(cumsumOutput, brlen, bclen, this);
            }
            // alternative: right indexing
            Hop maxDim = HopRewriteUtils.createAggUnaryOp(cumsumOutput, AggOp.MAX, Direction.RowCol);
            HopRewriteUtils.updateHopCharacteristics(maxDim, brlen, bclen, this);
            BinaryOp offsets = HopRewriteUtils.createBinary(cumsumOutput, emptyInd, OpOp2.MULT);
            HopRewriteUtils.updateHopCharacteristics(offsets, brlen, bclen, this);
            // Step 3: gather non-empty rows/cols into final results
            Lop linput = input.constructLops();
            Lop loffset = offsets.constructLops();
            Lop lmaxdim = maxDim.constructLops();
            double mestPM = OptimizerUtils.estimatePartitionedSizeExactSparsity(rlen, 1, brlen, bclen, 1.0);
            Lop rmEmpty = null;
            // a) broadcast-based PMM (permutation matrix mult)
            if (rmRows && rlen >= 0 && mestPM < OptimizerUtils.getRemoteMemBudgetMap() && HopRewriteUtils.isLiteralOfValue(emptyRet, false)) {
                boolean needPart = !offsets.dimsKnown() || offsets.getDim1() > DistributedCacheInput.PARTITION_SIZE;
                if (needPart) {
                    // requires partitioning
                    loffset = new DataPartition(loffset, DataType.MATRIX, ValueType.DOUBLE, (mestPM > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
                    loffset.getOutputParameters().setDimensions(rlen, 1, brlen, bclen, rlen);
                    setLineNumbers(loffset);
                }
                rmEmpty = new PMMJ(loffset, linput, lmaxdim, getDataType(), getValueType(), needPart, true, ExecType.MR);
                setOutputDimensions(rmEmpty);
                setLineNumbers(rmEmpty);
            } else // b) general case: repartition-based rmempty
            {
                boolean requiresRep = ((clen > bclen || clen <= 0) && rmRows) || ((rlen > brlen || rlen <= 0) && !rmRows);
                if (requiresRep) {
                    // ncol of left input (determines num replicates)
                    Lop pos = createOffsetLop(input, rmRows);
                    loffset = new RepMat(loffset, pos, rmRows, DataType.MATRIX, ValueType.DOUBLE);
                    loffset.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, nnz);
                    setLineNumbers(loffset);
                }
                Group group1 = new Group(linput, Group.OperationTypes.Sort, getDataType(), getValueType());
                setLineNumbers(group1);
                group1.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, nnz);
                Group group2 = new Group(loffset, Group.OperationTypes.Sort, getDataType(), getValueType());
                setLineNumbers(group2);
                group2.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, nnz);
                HashMap<String, Lop> inMap = new HashMap<>();
                inMap.put("target", group1);
                inMap.put("offset", group2);
                inMap.put("maxdim", lmaxdim);
                inMap.put("margin", inputlops.get("margin"));
                inMap.put("empty.return", inputlops.get("empty.return"));
                rmEmpty = new ParameterizedBuiltin(inMap, HopsParameterizedBuiltinLops.get(_op), getDataType(), getValueType(), et);
                setOutputDimensions(rmEmpty);
                setLineNumbers(rmEmpty);
            }
            Group group3 = new Group(rmEmpty, Group.OperationTypes.Sort, getDataType(), getValueType());
            setLineNumbers(group3);
            group3.getOutputParameters().setDimensions(-1, -1, brlen, bclen, -1);
            Aggregate finalagg = new Aggregate(group3, Aggregate.OperationTypes.Sum, DataType.MATRIX, getValueType(), ExecType.MR);
            setOutputDimensions(finalagg);
            setLineNumbers(finalagg);
            // Step 4: cleanup hops (allow for garbage collection)
            if (selectHop == null)
                HopRewriteUtils.removeChildReference(ppred0, input);
            setLops(finalagg);
        }
    } else if (et == ExecType.SPARK) {
        if (!(marginHop instanceof LiteralOp))
            throw new HopsException("Parameter 'margin' must be a literal argument.");
        Hop input = targetHop;
        long rlen = input.getDim1();
        long clen = input.getDim2();
        int brlen = input.getRowsInBlock();
        int bclen = input.getColsInBlock();
        boolean rmRows = ((LiteralOp) marginHop).getStringValue().equals("rows");
        // construct lops via new partial hop dag and subsequent lops construction
        // in order to reuse of operator selection decisions
        BinaryOp ppred0 = null;
        Hop emptyInd = null;
        if (selectHop == null) {
            // Step1: compute row/col non-empty indicators
            ppred0 = HopRewriteUtils.createBinary(input, new LiteralOp(0), OpOp2.NOTEQUAL);
            // always Spark
            ppred0.setForcedExecType(ExecType.SPARK);
            emptyInd = ppred0;
            if (!((rmRows && clen == 1) || (!rmRows && rlen == 1))) {
                emptyInd = HopRewriteUtils.createAggUnaryOp(ppred0, AggOp.MAX, rmRows ? Direction.Row : Direction.Col);
                // always Spark
                emptyInd.setForcedExecType(ExecType.SPARK);
            }
        } else {
            emptyInd = selectHop;
        }
        // Step 2: compute row offsets for non-empty rows
        Hop cumsumInput = emptyInd;
        if (!rmRows) {
            cumsumInput = HopRewriteUtils.createTranspose(emptyInd);
            HopRewriteUtils.updateHopCharacteristics(cumsumInput, brlen, bclen, this);
        }
        UnaryOp cumsum = HopRewriteUtils.createUnary(cumsumInput, OpOp1.CUMSUM);
        HopRewriteUtils.updateHopCharacteristics(cumsum, brlen, bclen, this);
        Hop cumsumOutput = cumsum;
        if (!rmRows) {
            cumsumOutput = HopRewriteUtils.createTranspose(cumsum);
            HopRewriteUtils.updateHopCharacteristics(cumsumOutput, brlen, bclen, this);
        }
        // alternative: right indexing
        Hop maxDim = HopRewriteUtils.createAggUnaryOp(cumsumOutput, AggOp.MAX, Direction.RowCol);
        HopRewriteUtils.updateHopCharacteristics(maxDim, brlen, bclen, this);
        BinaryOp offsets = HopRewriteUtils.createBinary(cumsumOutput, emptyInd, OpOp2.MULT);
        HopRewriteUtils.updateHopCharacteristics(offsets, brlen, bclen, this);
        // Step 3: gather non-empty rows/cols into final results
        Lop linput = input.constructLops();
        Lop loffset = offsets.constructLops();
        Lop lmaxdim = maxDim.constructLops();
        HashMap<String, Lop> inMap = new HashMap<>();
        inMap.put("target", linput);
        inMap.put("offset", loffset);
        inMap.put("maxdim", lmaxdim);
        inMap.put("margin", inputlops.get("margin"));
        inMap.put("empty.return", inputlops.get("empty.return"));
        if (!FORCE_DIST_RM_EMPTY && isRemoveEmptyBcSP())
            _bRmEmptyBC = true;
        ParameterizedBuiltin pbilop = new ParameterizedBuiltin(inMap, HopsParameterizedBuiltinLops.get(_op), getDataType(), getValueType(), et, _bRmEmptyBC);
        setOutputDimensions(pbilop);
        setLineNumbers(pbilop);
        // Step 4: cleanup hops (allow for garbage collection)
        if (selectHop == null)
            HopRewriteUtils.removeChildReference(ppred0, input);
        setLops(pbilop);
    // NOTE: in contrast to mr, replication and aggregation handled instruction-local
    }
}

Example 3 with RepMat

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

the class ParameterizedBuiltinOp method constructLopsGroupedAggregate.

private void constructLopsGroupedAggregate(HashMap<String, Lop> inputlops, ExecType et) {
    // reset reblock requirement (see MR aggregate / construct lops)
    setRequiresReblock(false);
    // determine output dimensions
    long outputDim1 = -1, outputDim2 = -1;
    Lop numGroups = inputlops.get(Statement.GAGG_NUM_GROUPS);
    if (!dimsKnown() && numGroups != null && numGroups instanceof Data && ((Data) numGroups).isLiteral()) {
        long ngroups = ((Data) numGroups).getLongValue();
        Lop input = inputlops.get(GroupedAggregate.COMBINEDINPUT);
        long inDim1 = input.getOutputParameters().getNumRows();
        long inDim2 = input.getOutputParameters().getNumCols();
        boolean rowwise = (inDim1 == 1 && inDim2 > 1);
        if (rowwise) {
            // vector
            outputDim1 = ngroups;
            outputDim2 = 1;
        } else {
            // vector or matrix
            outputDim1 = inDim2;
            outputDim2 = ngroups;
        }
    }
    // construct lops
    if (et == ExecType.MR) {
        Lop grp_agg = null;
        // construct necessary lops: combineBinary/combineTertiary and groupedAgg
        boolean isWeighted = (_paramIndexMap.get(Statement.GAGG_WEIGHTS) != null);
        if (isWeighted) {
            Lop append = BinaryOp.constructAppendLopChain(getInput().get(_paramIndexMap.get(Statement.GAGG_TARGET)), getInput().get(_paramIndexMap.get(Statement.GAGG_GROUPS)), getInput().get(_paramIndexMap.get(Statement.GAGG_WEIGHTS)), DataType.MATRIX, getValueType(), true, getInput().get(_paramIndexMap.get(Statement.GAGG_TARGET)));
            // add the combine lop to parameter list, with a new name "combinedinput"
            inputlops.put(GroupedAggregate.COMBINEDINPUT, append);
            inputlops.remove(Statement.GAGG_TARGET);
            inputlops.remove(Statement.GAGG_GROUPS);
            inputlops.remove(Statement.GAGG_WEIGHTS);
            grp_agg = new GroupedAggregate(inputlops, isWeighted, getDataType(), getValueType());
            grp_agg.getOutputParameters().setDimensions(outputDim1, outputDim2, getRowsInBlock(), getColsInBlock(), -1);
            setRequiresReblock(true);
        } else {
            Hop target = getInput().get(_paramIndexMap.get(Statement.GAGG_TARGET));
            Hop groups = getInput().get(_paramIndexMap.get(Statement.GAGG_GROUPS));
            Lop append = null;
            // physical operator selection
            double groupsSizeP = OptimizerUtils.estimatePartitionedSizeExactSparsity(groups.getDim1(), groups.getDim2(), groups.getRowsInBlock(), groups.getColsInBlock(), groups.getNnz());
            if (// mapgroupedagg
            groupsSizeP < OptimizerUtils.getRemoteMemBudgetMap(true) && getParameterHop(Statement.GAGG_FN) instanceof LiteralOp && ((LiteralOp) getParameterHop(Statement.GAGG_FN)).getStringValue().equals("sum") && inputlops.get(Statement.GAGG_NUM_GROUPS) != null) {
                // pre partitioning
                boolean needPart = (groups.dimsKnown() && groups.getDim1() * groups.getDim2() > DistributedCacheInput.PARTITION_SIZE);
                if (needPart) {
                    ExecType etPart = (OptimizerUtils.estimateSizeExactSparsity(groups.getDim1(), groups.getDim2(), 1.0) < OptimizerUtils.getLocalMemBudget()) ? ExecType.CP : // operator selection
                    ExecType.MR;
                    Lop dcinput = new DataPartition(groups.constructLops(), DataType.MATRIX, ValueType.DOUBLE, etPart, PDataPartitionFormat.ROW_BLOCK_WISE_N);
                    dcinput.getOutputParameters().setDimensions(groups.getDim1(), groups.getDim2(), target.getRowsInBlock(), target.getColsInBlock(), groups.getNnz());
                    setLineNumbers(dcinput);
                    inputlops.put(Statement.GAGG_GROUPS, dcinput);
                }
                Lop grp_agg_m = new GroupedAggregateM(inputlops, getDataType(), getValueType(), needPart, ExecType.MR);
                grp_agg_m.getOutputParameters().setDimensions(outputDim1, outputDim2, target.getRowsInBlock(), target.getColsInBlock(), -1);
                setLineNumbers(grp_agg_m);
                // post aggregation
                Group grp = new Group(grp_agg_m, Group.OperationTypes.Sort, getDataType(), getValueType());
                grp.getOutputParameters().setDimensions(outputDim1, outputDim2, target.getRowsInBlock(), target.getColsInBlock(), -1);
                setLineNumbers(grp);
                Aggregate agg1 = new Aggregate(grp, HopsAgg2Lops.get(AggOp.SUM), getDataType(), getValueType(), ExecType.MR);
                agg1.setupCorrectionLocation(CorrectionLocationType.NONE);
                agg1.getOutputParameters().setDimensions(outputDim1, outputDim2, target.getRowsInBlock(), target.getColsInBlock(), -1);
                grp_agg = agg1;
            // note: no reblock required
            } else // general case: groupedagg
            {
                if (// multi-column-block result matrix
                target.getDim2() >= target.getColsInBlock() || // unkown
                target.getDim2() <= 0) {
                    long m1_dim1 = target.getDim1();
                    long m1_dim2 = target.getDim2();
                    long m2_dim1 = groups.getDim1();
                    long m2_dim2 = groups.getDim2();
                    long m3_dim1 = m1_dim1;
                    long m3_dim2 = ((m1_dim2 >= 0 && m2_dim2 >= 0) ? (m1_dim2 + m2_dim2) : -1);
                    long m3_nnz = (target.getNnz() > 0 && groups.getNnz() > 0) ? (target.getNnz() + groups.getNnz()) : -1;
                    long brlen = target.getRowsInBlock();
                    long bclen = target.getColsInBlock();
                    Lop offset = createOffsetLop(target, true);
                    Lop rep = new RepMat(groups.constructLops(), offset, true, groups.getDataType(), groups.getValueType());
                    setOutputDimensions(rep);
                    setLineNumbers(rep);
                    Group group1 = new Group(target.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, target.getValueType());
                    group1.getOutputParameters().setDimensions(m1_dim1, m1_dim2, brlen, bclen, target.getNnz());
                    setLineNumbers(group1);
                    Group group2 = new Group(rep, Group.OperationTypes.Sort, DataType.MATRIX, groups.getValueType());
                    group1.getOutputParameters().setDimensions(m2_dim1, m2_dim2, brlen, bclen, groups.getNnz());
                    setLineNumbers(group2);
                    append = new AppendR(group1, group2, DataType.MATRIX, ValueType.DOUBLE, true, ExecType.MR);
                    append.getOutputParameters().setDimensions(m3_dim1, m3_dim2, brlen, bclen, m3_nnz);
                    setLineNumbers(append);
                } else // single-column-block vector or matrix
                {
                    append = BinaryOp.constructMRAppendLop(target, groups, DataType.MATRIX, getValueType(), true, target);
                }
                // add the combine lop to parameter list, with a new name "combinedinput"
                inputlops.put(GroupedAggregate.COMBINEDINPUT, append);
                inputlops.remove(Statement.GAGG_TARGET);
                inputlops.remove(Statement.GAGG_GROUPS);
                grp_agg = new GroupedAggregate(inputlops, isWeighted, getDataType(), getValueType());
                grp_agg.getOutputParameters().setDimensions(outputDim1, outputDim2, getRowsInBlock(), getColsInBlock(), -1);
                setRequiresReblock(true);
            }
        }
        setLineNumbers(grp_agg);
        setLops(grp_agg);
    } else // CP/Spark
    {
        Lop grp_agg = null;
        if (et == ExecType.CP) {
            int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
            grp_agg = new GroupedAggregate(inputlops, getDataType(), getValueType(), et, k);
            grp_agg.getOutputParameters().setDimensions(outputDim1, outputDim2, getRowsInBlock(), getColsInBlock(), -1);
        } else if (et == ExecType.SPARK) {
            // physical operator selection
            Hop groups = getParameterHop(Statement.GAGG_GROUPS);
            boolean broadcastGroups = (_paramIndexMap.get(Statement.GAGG_WEIGHTS) == null && OptimizerUtils.checkSparkBroadcastMemoryBudget(groups.getDim1(), groups.getDim2(), groups.getRowsInBlock(), groups.getColsInBlock(), groups.getNnz()));
            if (// mapgroupedagg
            broadcastGroups && getParameterHop(Statement.GAGG_FN) instanceof LiteralOp && ((LiteralOp) getParameterHop(Statement.GAGG_FN)).getStringValue().equals("sum") && inputlops.get(Statement.GAGG_NUM_GROUPS) != null) {
                Hop target = getTargetHop();
                grp_agg = new GroupedAggregateM(inputlops, getDataType(), getValueType(), true, ExecType.SPARK);
                grp_agg.getOutputParameters().setDimensions(outputDim1, outputDim2, target.getRowsInBlock(), target.getColsInBlock(), -1);
            // no reblock required (directly output binary block)
            } else // groupedagg (w/ or w/o broadcast)
            {
                grp_agg = new GroupedAggregate(inputlops, getDataType(), getValueType(), et, broadcastGroups);
                grp_agg.getOutputParameters().setDimensions(outputDim1, outputDim2, -1, -1, -1);
                setRequiresReblock(true);
            }
        }
        setLineNumbers(grp_agg);
        setLops(grp_agg);
    }
}

Example 4 with RepMat

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

the class QuaternaryOp method constructRightFactorMRLop.

private Lop constructRightFactorMRLop(Hop U, Hop V, boolean cacheV, double m2Size) {
    Lop lV = null;
    if (cacheV) {
        // partitioning of V for read through distributed cache
        boolean needPartV = !V.dimsKnown() || V.getDim1() * V.getDim2() > DistributedCacheInput.PARTITION_SIZE;
        lV = V.constructLops();
        if (needPartV) {
            // requires partitioning
            lV = new DataPartition(lV, DataType.MATRIX, ValueType.DOUBLE, (m2Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
            lV.getOutputParameters().setDimensions(V.getDim1(), V.getDim2(), getRowsInBlock(), getColsInBlock(), V.getNnz());
            setLineNumbers(lV);
        }
    } else {
        // replication of t(V) for shuffle to target block
        Transform ltV = new Transform(V.constructLops(), HopsTransf2Lops.get(ReOrgOp.TRANSPOSE), getDataType(), getValueType(), ExecType.MR);
        ltV.getOutputParameters().setDimensions(V.getDim2(), V.getDim1(), V.getColsInBlock(), V.getRowsInBlock(), V.getNnz());
        setLineNumbers(ltV);
        // nrow of U determines num replicates
        Lop offset = createOffsetLop(U, false);
        lV = new RepMat(ltV, offset, false, V.getDataType(), V.getValueType());
        lV.getOutputParameters().setDimensions(V.getDim2(), V.getDim1(), V.getColsInBlock(), V.getRowsInBlock(), V.getNnz());
        setLineNumbers(lV);
        Group grpV = new Group(lV, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        grpV.getOutputParameters().setDimensions(V.getDim2(), V.getDim1(), V.getColsInBlock(), V.getRowsInBlock(), -1);
        setLineNumbers(grpV);
        lV = grpV;
    }
    return lV;
}

Example 5 with RepMat

use of org.apache.sysml.lops.RepMat in project systemml by apache.

the class BinaryOp method constructLopsBinaryDefault.

private void constructLopsBinaryDefault() {
    /* 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);
        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();
        boolean isGPUSoftmax = et == ExecType.GPU && op == Hop.OpOp2.DIV && getInput().get(0) instanceof UnaryOp && getInput().get(1) instanceof AggUnaryOp && ((UnaryOp) getInput().get(0)).getOp() == OpOp1.EXP && ((AggUnaryOp) getInput().get(1)).getOp() == AggOp.SUM && ((AggUnaryOp) getInput().get(1)).getDirection() == Direction.Row && getInput().get(0) == getInput().get(1).getInput().get(0);
        if (isGPUSoftmax) {
            UnaryCP softmax = new UnaryCP(getInput().get(0).getInput().get(0).constructLops(), UnaryCP.OperationTypes.SOFTMAX, getDataType(), getValueType(), et);
            setOutputDimensions(softmax);
            setLineNumbers(softmax);
            setLops(softmax);
        } else if (et == ExecType.CP || 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.getFilename(), 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);
            }
        }
    }
}