use of org.apache.sysml.lops.WeightedUnaryMMR in project incubator-systemml by apache.
the class QuaternaryOp method constructSparkLopsWeightedUMM.
private void constructSparkLopsWeightedUMM(WUMMType wtype) throws HopsException, LopsException {
//NOTE: the common case for wumm are factors U/V with a rank of 10s to 100s; the current runtime only
//supports single block outer products (U/V rank <= blocksize, i.e., 1000 by default); we enforce this
//by applying the hop rewrite for Weighted UnaryMM only if this constraint holds.
Unary.OperationTypes uop = _uop != null ? HopsOpOp1LopsU.get(_uop) : _sop == OpOp2.POW ? Unary.OperationTypes.POW2 : Unary.OperationTypes.MULTIPLY2;
//Notes: Any broadcast needs to fit twice in local memory because we partition the input in cp,
//and needs to fit once in executor broadcast memory. The 2GB broadcast constraint is no longer
//required because the max_int byte buffer constraint has been fixed in Spark 1.4
double memBudgetExec = SparkExecutionContext.getBroadcastMemoryBudget();
double memBudgetLocal = OptimizerUtils.getLocalMemBudget();
Hop X = getInput().get(0);
Hop U = getInput().get(1);
Hop V = getInput().get(2);
//MR operator selection, part1
//size U
double m1Size = OptimizerUtils.estimateSize(U.getDim1(), U.getDim2());
//size V
double m2Size = OptimizerUtils.estimateSize(V.getDim1(), V.getDim2());
boolean isMapWsloss = (m1Size + m2Size < memBudgetExec && 2 * m1Size < memBudgetLocal && 2 * m2Size < memBudgetLocal);
if (//broadcast
!FORCE_REPLICATION && isMapWsloss) {
//map-side wumm always with broadcast
Lop wumm = new WeightedUnaryMM(X.constructLops(), U.constructLops(), V.constructLops(), DataType.MATRIX, ValueType.DOUBLE, wtype, uop, ExecType.SPARK);
setOutputDimensions(wumm);
setLineNumbers(wumm);
setLops(wumm);
} else //general case
{
//MR operator selection part 2
boolean cacheU = !FORCE_REPLICATION && (m1Size < memBudgetExec && 2 * m1Size < memBudgetLocal);
boolean cacheV = !FORCE_REPLICATION && ((!cacheU && m2Size < memBudgetExec) || (cacheU && m1Size + m2Size < memBudgetExec)) && 2 * m2Size < memBudgetLocal;
//reduce-side wumm w/ or without broadcast
Lop wumm = new WeightedUnaryMMR(X.constructLops(), U.constructLops(), V.constructLops(), DataType.MATRIX, ValueType.DOUBLE, wtype, uop, cacheU, cacheV, ExecType.SPARK);
setOutputDimensions(wumm);
setLineNumbers(wumm);
setLops(wumm);
}
}
use of org.apache.sysml.lops.WeightedUnaryMMR in project incubator-systemml by apache.
the class QuaternaryOp method constructMRLopsWeightedUMM.
private void constructMRLopsWeightedUMM(WUMMType wtype) throws HopsException, LopsException {
//NOTE: the common case for wumm are factors U/V with a rank of 10s to 100s; the current runtime only
//supports single block outer products (U/V rank <= blocksize, i.e., 1000 by default); we enforce this
//by applying the hop rewrite for Weighted UnaryMM only if this constraint holds.
Unary.OperationTypes uop = _uop != null ? HopsOpOp1LopsU.get(_uop) : _sop == OpOp2.POW ? Unary.OperationTypes.POW2 : Unary.OperationTypes.MULTIPLY2;
Hop X = getInput().get(0);
Hop U = getInput().get(1);
Hop V = getInput().get(2);
//MR operator selection, part1
//size U
double m1Size = OptimizerUtils.estimateSize(U.getDim1(), U.getDim2());
//size V
double m2Size = OptimizerUtils.estimateSize(V.getDim1(), V.getDim2());
boolean isMapWumm = (m1Size + m2Size < OptimizerUtils.getRemoteMemBudgetMap(true));
if (//broadcast
!FORCE_REPLICATION && isMapWumm) {
//partitioning of U
boolean needPartU = !U.dimsKnown() || U.getDim1() * U.getDim2() > DistributedCacheInput.PARTITION_SIZE;
Lop lU = U.constructLops();
if (needPartU) {
//requires partitioning
lU = new DataPartition(lU, DataType.MATRIX, ValueType.DOUBLE, (m1Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
lU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), getRowsInBlock(), getColsInBlock(), U.getNnz());
setLineNumbers(lU);
}
//partitioning of V
boolean needPartV = !V.dimsKnown() || V.getDim1() * V.getDim2() > DistributedCacheInput.PARTITION_SIZE;
Lop 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);
}
//map-side wumm always with broadcast
Lop wumm = new WeightedUnaryMM(X.constructLops(), lU, lV, DataType.MATRIX, ValueType.DOUBLE, wtype, uop, ExecType.MR);
setOutputDimensions(wumm);
setLineNumbers(wumm);
setLops(wumm);
//in contrast to wsloss no aggregation required
} else //general case
{
//MR operator selection part 2
boolean cacheU = !FORCE_REPLICATION && (m1Size < OptimizerUtils.getRemoteMemBudgetReduce());
boolean cacheV = !FORCE_REPLICATION && ((!cacheU && m2Size < OptimizerUtils.getRemoteMemBudgetReduce()) || (cacheU && m1Size + m2Size < OptimizerUtils.getRemoteMemBudgetReduce()));
Group grpX = new Group(X.constructLops(), Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
grpX.getOutputParameters().setDimensions(X.getDim1(), X.getDim2(), X.getRowsInBlock(), X.getColsInBlock(), X.getNnz());
setLineNumbers(grpX);
Lop lU = null;
if (cacheU) {
//partitioning of U for read through distributed cache
boolean needPartU = !U.dimsKnown() || U.getDim1() * U.getDim2() > DistributedCacheInput.PARTITION_SIZE;
lU = U.constructLops();
if (needPartU) {
//requires partitioning
lU = new DataPartition(lU, DataType.MATRIX, ValueType.DOUBLE, (m1Size > OptimizerUtils.getLocalMemBudget()) ? ExecType.MR : ExecType.CP, PDataPartitionFormat.ROW_BLOCK_WISE_N);
lU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), getRowsInBlock(), getColsInBlock(), U.getNnz());
setLineNumbers(lU);
}
} else {
//replication of U for shuffle to target block
//ncol of t(V) -> nrow of V determines num replicates
Lop offset = createOffsetLop(V, false);
lU = new RepMat(U.constructLops(), offset, true, V.getDataType(), V.getValueType());
lU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), U.getRowsInBlock(), U.getColsInBlock(), U.getNnz());
setLineNumbers(lU);
Group grpU = new Group(lU, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
grpU.getOutputParameters().setDimensions(U.getDim1(), U.getDim2(), U.getRowsInBlock(), U.getColsInBlock(), -1);
setLineNumbers(grpU);
lU = grpU;
}
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;
}
//reduce-side wumm w/ or without broadcast
Lop wumm = new WeightedUnaryMMR(grpX, lU, lV, DataType.MATRIX, ValueType.DOUBLE, wtype, uop, cacheU, cacheV, ExecType.MR);
setOutputDimensions(wumm);
setLineNumbers(wumm);
setLops(wumm);
//in contrast to wsloss no aggregation required
}
}
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