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[MINOR] Add functions and UnitTests for the Estimator LayeredGraph #1945

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[MINOR] Add functions and UnitTests for the Estimator LayeredGraph #1945

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b834916
implement functions and add UnitTests for the Estimator LayeredGraph
MRGSRT Nov 15, 2023
966f592
Merge branch 'apache:main' into layeredgraph
MRGSRT Nov 15, 2023
69ea151
Adjusted tests for the diagonal function
MRGSRT Nov 30, 2023
3a025d4
add additional functionalities and adjustments for the chain operatio…
MRGSRT Dec 1, 2023
f6df09b
Merge remote-tracking branch 'origin/layeredgraph' into layeredgraph
MRGSRT Dec 1, 2023
0f08528
added diag V2M M2V tests
MRGSRT Dec 3, 2023
aa2eac1
added additional tests, increased number of rounds and tolerate more …
MRGSRT Dec 3, 2023
3ff39f5
adjustments for some operators to work on a layeredgraph with more th…
MRGSRT Dec 16, 2023
a2aa29c
Merge branch 'apache:main' into layeredgraph
MRGSRT Dec 16, 2023
c2a8c91
index fix
MRGSRT Dec 18, 2023
b3b70af
Merge remote-tracking branch 'origin/layeredgraph' into layeredgraph
MRGSRT Dec 18, 2023
f7f8657
fix traverse function
MRGSRT Dec 18, 2023
215df46
optimization for traverse()
MRGSRT Dec 19, 2023
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334 changes: 328 additions & 6 deletions src/main/java/org/apache/sysds/hops/estim/EstimatorLayeredGraph.java
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@
import java.util.Arrays;
import java.util.List;
import java.util.stream.Collectors;
import java.util.stream.Stream;

/**
* This estimator implements an approach based on a so-called layered graph,
Expand All @@ -43,7 +44,7 @@
*/
public class EstimatorLayeredGraph extends SparsityEstimator {

private static final int ROUNDS = 32;
private static final int ROUNDS = 512;
private final int _rounds;

public EstimatorLayeredGraph() {
Expand All @@ -57,21 +58,47 @@ public EstimatorLayeredGraph(int rounds) {
@Override
public DataCharacteristics estim(MMNode root) {
List<MatrixBlock> leafs = getMatrices(root, new ArrayList<>());
long nnz = new LayeredGraph(leafs, _rounds).estimateNnz();
List<OpCode> ops = getOps(root, new ArrayList<>());
List<LayeredGraph> LGs = new ArrayList<>();
LayeredGraph ret = traverse(root);
long nnz = ret.estimateNnz();
return root.setDataCharacteristics(new MatrixCharacteristics(
leafs.get(0).getNumRows(), leafs.get(leafs.size()-1).getNumColumns(), nnz));
ret._nodes.get(0).length, ret._nodes.get(ret._nodes.size() - 1).length, nnz));
}

public LayeredGraph traverse(MMNode node) {
if(node.getLeft() == null || node.getRight() == null) return null;
LayeredGraph retL = traverse(node.getLeft());
LayeredGraph retR = traverse(node.getRight());
LayeredGraph ret, left, right;

left = (node.getLeft().getData() == null)
? retL : new LayeredGraph(node.getLeft().getData(), _rounds);
right = (node.getRight().getData() == null)
? retR : new LayeredGraph(node.getRight().getData(), _rounds);

ret = estimInternal(left, right, node.getOp());

return ret;
}

@Override
public double estim(MatrixBlock m1, MatrixBlock m2, OpCode op) {
if( op == OpCode.MM )
return estim(m1, m2);
throw new NotImplementedException();
LayeredGraph lg1 = new LayeredGraph(m1, _rounds);
LayeredGraph lg2 = new LayeredGraph(m2, _rounds);
LayeredGraph output = estimInternal(lg1, lg2, op);
return OptimizerUtils.getSparsity(
output._nodes.get(0).length, output._nodes.get(output._nodes.size() - 1).length, output.estimateNnz());
}

@Override
public double estim(MatrixBlock m, OpCode op) {
throw new NotImplementedException();
LayeredGraph lg1 = new LayeredGraph(m, _rounds);
LayeredGraph output = estimInternal(lg1, null, op);
return OptimizerUtils.getSparsity(
output._nodes.get(0).length, output._nodes.get(output._nodes.size() - 1).length, output.estimateNnz());
}

@Override
Expand All @@ -80,6 +107,23 @@ public double estim(MatrixBlock m1, MatrixBlock m2) {
return OptimizerUtils.getSparsity(
m1.getNumRows(), m2.getNumColumns(), graph.estimateNnz());
}

private static LayeredGraph estimInternal(LayeredGraph lg1, LayeredGraph lg2, OpCode op) {
switch(op) {
case MM: return lg1.matMult(lg2);
case MULT: return lg1.and(lg2);
case PLUS: return lg1.or(lg2);
case RBIND: return lg1.rbind(lg2);
case CBIND: return lg1.cbind(lg2);
// case NEQZERO:
// case EQZERO:
case TRANS: return lg1.transpose();
case DIAG: return lg1.diag();
// case RESHAPE:
default:
throw new NotImplementedException();
}
}

private List<MatrixBlock> getMatrices(MMNode node, List<MatrixBlock> leafs) {
//NOTE: this extraction is only correct and efficient for chains, no DAGs
Expand All @@ -92,6 +136,18 @@ private List<MatrixBlock> getMatrices(MMNode node, List<MatrixBlock> leafs) {
return leafs;
}

private List<OpCode> getOps(MMNode node, List<OpCode> ops) {
//NOTE: this extraction is only correct and efficient for chains, no DAGs
if(node.isLeaf()) {
}
else {
getOps(node.getLeft(), ops);
getOps(node.getRight(), ops);
ops.add(node.getOp());
}
return ops;
}

public static class LayeredGraph {
private final List<Node[]> _nodes; //nodes partitioned by graph level
private final int _rounds; //length of propagated r-vectors
Expand All @@ -101,6 +157,12 @@ public LayeredGraph(List<MatrixBlock> chain, int r) {
_rounds = r;
chain.forEach(i -> buildNext(i));
}

public LayeredGraph(MatrixBlock m, int r) {
_nodes = new ArrayList<>();
_rounds = r;
buildNext(m);
}

public void buildNext(MatrixBlock mb) {
if( mb.isEmpty() )
Expand Down Expand Up @@ -168,7 +230,267 @@ private static double calcNNZ(double[] inpvec, int rounds) {
return (inpvec != null && inpvec.length > 0) ?
(rounds - 1) / Arrays.stream(inpvec).sum() : 0;
}


public LayeredGraph rbind(LayeredGraph lg) {
LayeredGraph ret = new LayeredGraph(List.of(), _rounds);

Node[] rows = new Node[_nodes.get(0).length + lg._nodes.get(0).length];
Node[] columns = _nodes.get(1).clone();

System.arraycopy(_nodes.get(0), 0, rows, 0, _nodes.get(0).length);

for (int i = _nodes.get(0).length; i < rows.length; i++)
rows[i] = new Node();

for(int i = 0; i < lg._nodes.get(0).length; i++) {
for(int j = 0; j < columns.length; j++) {
List<Node> edges = lg._nodes.get(1)[j].getInput();
if(edges.contains(lg._nodes.get(0)[i])) {
columns[j].addInput(rows[i + _nodes.get(0).length]);
}
}
}
ret._nodes.add(rows);
ret._nodes.add(columns);
return ret;
}

public LayeredGraph cbind(LayeredGraph lg) {
LayeredGraph ret = new LayeredGraph(List.of(), _rounds);
int colLength = _nodes.get(1).length + lg._nodes.get(1).length;

Node[] rows = _nodes.get(0).clone();
Node[] columns = new Node[colLength];

System.arraycopy(_nodes.get(1), 0, columns, 0, _nodes.get(1).length);

for (int i = _nodes.get(1).length; i < columns.length; i++)
columns[i] = new Node();

for(int i = 0; i < rows.length; i++) {
for(int j = 0; j < lg._nodes.get(1).length; j++) {
List<Node> edges = lg._nodes.get(1)[j].getInput();
if(edges.contains(lg._nodes.get(0)[i])) {
columns[j + _nodes.get(1).length].addInput(rows[i]);
}
}
}
ret._nodes.add(rows);
ret._nodes.add(columns);
return ret;
}

public LayeredGraph matMult(LayeredGraph lg) {
List<MatrixBlock> m = Stream.concat(
this.toMatrixBlockList().stream(), lg.toMatrixBlockList().stream())
.collect(Collectors.toList());
return new LayeredGraph(m, _rounds);
}

public LayeredGraph or(LayeredGraph lg) {
LayeredGraph ret = new LayeredGraph(List.of(), _rounds);
Node[] rows = new Node[_nodes.get(0).length];
for (int i = 0; i < _nodes.get(0).length; i++)
rows[i] = new Node();
ret._nodes.add(rows);

for(int x = 0; x < _nodes.size() - 1; x++) {
int y = x + 1;
rows = ret._nodes.get(x);
Node[] columns = new Node[_nodes.get(y).length];
for (int i = 0; i < _nodes.get(y).length; i++)
columns[i] = new Node();

for(int i = 0; i < _nodes.get(x).length; i++) {
for(int j = 0; j < _nodes.get(y).length; j++) {
List<Node> edges1 = _nodes.get(y)[j].getInput();
List<Node> edges2 = lg._nodes.get(y)[j].getInput();
if(edges1.contains(_nodes.get(x)[i]) || edges2.contains(lg._nodes.get(x)[i]))
{
columns[j].addInput(rows[i]);
}
}
}
ret._nodes.add(columns);
}
return ret;
}

public LayeredGraph and(LayeredGraph lg) {
LayeredGraph ret = new LayeredGraph(List.of(), _rounds);
Node[] rows = new Node[_nodes.get(0).length];
for (int i = 0; i < _nodes.get(0).length; i++)
rows[i] = new Node();
ret._nodes.add(rows);

for(int x = 0; x < _nodes.size() - 1; x++) {
int y = x + 1;
rows = ret._nodes.get(x);
Node[] columns = new Node[_nodes.get(y).length];
for (int i = 0; i < _nodes.get(y).length; i++)
columns[i] = new Node();

for(int i = 0; i < _nodes.get(x).length; i++) {
for(int j = 0; j < _nodes.get(y).length; j++) {
List<Node> edges1 = _nodes.get(y)[j].getInput();
List<Node> edges2 = lg._nodes.get(y)[j].getInput();
if(edges1.contains(_nodes.get(x)[i]) && edges2.contains(lg._nodes.get(x)[i]))
{
columns[j].addInput(rows[i]);
}
}
}
ret._nodes.add(columns);
}
return ret;
}

public LayeredGraph transpose() {
LayeredGraph ret = new LayeredGraph(List.of(), _rounds);
Node[] rows = new Node[_nodes.get(_nodes.size() - 1).length];
for (int i = 0; i < rows.length; i++)
rows[i] = new Node();
ret._nodes.add(rows);

for(int x = _nodes.size() - 1; x > 0; x--) {
rows = ret._nodes.get(ret._nodes.size() - 1);
Node[] columnsOld = _nodes.get(x);
Node[] rowsOld = _nodes.get(x - 1);
Node[] columns = new Node[rowsOld.length];

for (int i = 0; i < rowsOld.length; i++)
columns[i] = new Node();

for(int i = 0; i < rowsOld.length; i++) {
for(int j = 0; j < columnsOld.length; j++) {
List<Node> edges = columnsOld[j].getInput();
if(edges.contains(rowsOld[i])) {
columns[i].addInput(rows[j]);
}
}
}
ret._nodes.add(columns);
}
return ret;
}

public LayeredGraph diag() {
LayeredGraph ret = new LayeredGraph(List.of(), _rounds);
Node[] rowsOld = _nodes.get(0);
Node[] columnsOld = _nodes.get(1);

if(_nodes.get(1).length == 1) {
Node[] rows = new Node[rowsOld.length];
Node[] columns = new Node[rowsOld.length];

for (int i = 0; i < rowsOld.length; i++)
rows[i] = new Node();
for (int i = 0; i < rowsOld.length; i++)
columns[i] = new Node();

List<Node> edges = columnsOld[0].getInput();
for(int i = 0; i < rowsOld.length; i++) {
for(int j = 0; j < rowsOld.length; j++) {
if(edges.contains(rowsOld[i]) && i == j) {
columns[j].addInput(rows[i]);
}
}
}
ret._nodes.add(rows);
ret._nodes.add(columns);
return ret;
}
else if(_nodes.get(0).length == 1){
Node[] rows = new Node[columnsOld.length];
Node[] columns = new Node[columnsOld.length];

for (int i = 0; i < columnsOld.length; i++)
rows[i] = new Node();
for (int i = 0; i < columnsOld.length; i++)
columns[i] = new Node();

for(int i = 0; i < columnsOld.length; i++) {
for(int j = 0; j < columnsOld.length; j++) {
List<Node> edges = columnsOld[j].getInput();
if(edges.contains(rowsOld[0]) && i == j) {
columns[j].addInput(rows[i]);
}
}
}
ret._nodes.add(rows);
ret._nodes.add(columns);
return ret;
}
else {
Node[] rows = new Node[rowsOld.length];
Node[] columns = new Node[1];
for (int i = 0; i < rowsOld.length; i++)
rows[i] = new Node();
for (int i = 0; i < 1; i++)
columns[i] = new Node();
for(int i = 0; i < rowsOld.length; i++) {
for(int j = 0; j < columnsOld.length; j++) {
List<Node> edges = columnsOld[j].getInput();
if(edges.contains(rowsOld[i]) && i == j) {
columns[0].addInput(rows[i]);
}
}
}
ret._nodes.add(rows);
ret._nodes.add(columns);
return ret;
}
}

public MatrixBlock toMatrixBlock() {
List<Double> a = new ArrayList<>();
int rows = _nodes.get(0).length;
int cols = _nodes.get(1).length;
for(int i = 0; i < rows * cols; i++) {
a.add(0.);
}
for(int i = 0; i < rows; i++) {
for(int j = 0; j < cols; j++) {
List<Node> edges = _nodes.get(1)[j].getInput();
if(edges.contains(_nodes.get(0)[i])) {
a.set(i * cols + j, 1. + a.get(i * cols + j));
}
else {
a.set(i * cols + j, 0.);
}
}
}
double[] arr = a.stream().mapToDouble(d -> d).toArray();
return new MatrixBlock(rows, cols, arr);
}

public List<MatrixBlock> toMatrixBlockList() {
List<MatrixBlock> m = new ArrayList<>();
for(int x = 0; x < _nodes.size() - 1; x++) {
int y = x + 1;
List<Double> a = new ArrayList<>();
int rows = _nodes.get(x).length;
int cols = _nodes.get(y).length;
for(int i = 0; i < rows * cols; i++) {
a.add(0.);
}
for(int i = 0; i < rows; i++) {
for(int j = 0; j < cols; j++) {
List<Node> edges = _nodes.get(y)[j].getInput();
if(edges.contains(_nodes.get(x)[i])) {
a.set(i * cols + j, 1. + a.get(i * cols + j));
}
else {
a.set(i * cols + j, 0.);
}
}
}
double[] arr = a.stream().mapToDouble(d -> d).toArray();
m.add(new MatrixBlock(rows, cols, arr));
}
return m;
}

private static class Node {
private List<Node> _input = new ArrayList<>();
private double[] _rvect;
Expand Down
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