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[SYSTEMDS-3636] Improved ultra-sparse tsmm left w/ sparse output #1955

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[SYSTEMDS-3636] Improved ultra-sparse tsmm left w/ sparse output #1955

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8f64326
add support for tsmm left for ultra sparse matrizes
christinadionysio Dec 7, 2023
5aeb9c6
Merge branch 'main' into leftTransposeSelfUltraSparseMatMul
christinadionysio Dec 7, 2023
815e051
review
christinadionysio Dec 8, 2023
9fe6897
remove redundant else and move logic into if branch
christinadionysio Dec 8, 2023
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117 changes: 72 additions & 45 deletions src/main/java/org/apache/sysds/runtime/matrix/data/LibMatrixMult.java
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Original file line number Diff line number Diff line change
Expand Up @@ -442,9 +442,9 @@ public static void matrixMultTransposeSelf(MatrixBlock m1, MatrixBlock ret, bool
//Timing time = new Timing(true);

//pre-processing
ret.sparse = isSparseOutputTSMM(m1, leftTranspose);
ret.sparse = isSparseOutputTSMM(m1);
ret.allocateBlock();
MatrixBlock m1t = isSparseOutputTSMM(m1, leftTranspose, true) ?
MatrixBlock m1t = isSparseOutputTSMM(m1, true) ?
LibMatrixReorg.transpose(m1) : null;

//core tsmm operation
Expand Down Expand Up @@ -484,9 +484,9 @@ public static void matrixMultTransposeSelf(MatrixBlock m1, MatrixBlock ret, bool
//Timing time = new Timing(true);

//pre-processing (no need to check isThreadSafe)
ret.sparse = isSparseOutputTSMM(m1, leftTranspose);
ret.sparse = isSparseOutputTSMM(m1);
ret.allocateBlock();
MatrixBlock m1t = isSparseOutputTSMM(m1, leftTranspose, true) ?
MatrixBlock m1t = isSparseOutputTSMM(m1, true) ?
LibMatrixReorg.transpose(m1, k) : null;

//core multi-threaded matrix mult computation
Expand Down Expand Up @@ -2506,39 +2506,60 @@ private static void matrixMultTransposeSelfSparse( MatrixBlock m1, MatrixBlock r
}

private static void matrixMultTransposeSelfUltraSparse( MatrixBlock m1, MatrixBlock ret, boolean leftTranspose, int rl, int ru ) {
if( leftTranspose )
throw new DMLRuntimeException("Left tsmm with sparse output not supported");

// Operation X%*%t(X), sparse input and output
SparseBlock a = m1.sparseBlock;
SparseBlock c = ret.sparseBlock;
SparseBlock a = m1.sparseBlock;
SparseBlock c = ret.sparseBlock;
int m = m1.rlen;

final int blocksize = 256;
for(int bi=rl; bi<ru; bi+=blocksize) { //blocking rows in X
int bimin = Math.min(bi+blocksize, ru);
for(int i=bi; i<bimin; i++) //preallocation
if( !a.isEmpty(i) )
c.allocate(i, 8*SparseRowVector.initialCapacity); //heuristic
for(int bj=bi; bj<m; bj+=blocksize ) { //blocking cols in t(X)
int bjmin = Math.min(bj+blocksize, m);
for(int i=bi; i<bimin; i++) { //rows in X
if( a.isEmpty(i) ) continue;
int apos = a.pos(i);
int alen = a.size(i);
int[] aix = a.indexes(i);
double[] avals = a.values(i);
for(int j=Math.max(bj,i); j<bjmin; j++) { //cols in t(X)
if( a.isEmpty(j) ) continue;
int bpos = a.pos(j);
int blen = a.size(j);
int[] bix = a.indexes(j);
double[] bvals = a.values(j);

//compute sparse dot product and append
double v = dotProduct(avals, aix, apos, alen, bvals, bix, bpos, blen);
if( v != 0 )
c.append(i, j, v);

if(leftTranspose) {
// Operation t(X)%*%X, sparse input and output
for(int i=0; i<m; i++)
c.allocate(i, 8*SparseRowVector.initialCapacity);
SparseRow[] sr = ((SparseBlockMCSR) c).getRows();
for( int r=0; r<a.numRows(); r++ ) {
if( a.isEmpty(r) ) continue;
final int alen = a.size(r);
final double[] avals = a.values(r);
final int apos = a.pos(r);
int[] aix = a.indexes(r);
int rlix = (rl==0) ? 0 : a.posFIndexGTE(r, rl);
if(rlix>=0) {
int len = apos + alen;
for(int i = rlix; i < len && aix[i] < ru; i++) {
for (int k = a.posFIndexGTE(r, aix[i]); k < len; k++) {
sr[aix[i]].add(c.pos(k) + aix[k], avals[i] * avals[k]);
}
}
}
}
}
else {
// Operation X%*%t(X), sparse input and output
final int blocksize = 256;
for(int bi=rl; bi<ru; bi+=blocksize) { //blocking rows in X
int bimin = Math.min(bi+blocksize, ru);
for(int i=bi; i<bimin; i++) //preallocation
if( !a.isEmpty(i) )
c.allocate(i, 8*SparseRowVector.initialCapacity); //heuristic
for(int bj=bi; bj<m; bj+=blocksize ) { //blocking cols in t(X)
int bjmin = Math.min(bj+blocksize, m);
for(int i=bi; i<bimin; i++) { //rows in X
if( a.isEmpty(i) ) continue;
int apos = a.pos(i);
int alen = a.size(i);
int[] aix = a.indexes(i);
double[] avals = a.values(i);
for(int j=Math.max(bj,i); j<bjmin; j++) { //cols in t(X)
if( a.isEmpty(j) ) continue;
int bpos = a.pos(j);
int blen = a.size(j);
int[] bix = a.indexes(j);
double[] bvals = a.values(j);

//compute sparse dot product and append
double v = dotProduct(avals, aix, apos, alen, bvals, bix, bpos, blen);
if( v != 0 )
c.append(i, j, v);
}
}
}
}
Expand All @@ -2547,12 +2568,18 @@ private static void matrixMultTransposeSelfUltraSparse( MatrixBlock m1, MatrixBl

//alternative matrixMultTransposeSelfUltraSparse2 w/ IKJ iteration order and sparse updates
private static void matrixMultTransposeSelfUltraSparse2( MatrixBlock m1, MatrixBlock m1t, MatrixBlock ret, boolean leftTranspose, int rl, int ru ) {
if( leftTranspose )
throw new DMLRuntimeException("Left tsmm with sparse output not supported");
SparseBlock a;
SparseBlock b;
if( leftTranspose ) {
a = m1t.sparseBlock;
b = m1.sparseBlock;
}
else {
a = m1.sparseBlock;
b = m1t.sparseBlock;
}

// Operation X%*%t(X), sparse input and output
SparseBlock a = m1.sparseBlock;
SparseBlock b = m1t.sparseBlock;
SparseBlock c = ret.sparseBlock;
for(int i=rl; i<ru; i++) { //rows in X
if( a.isEmpty(i) ) continue;
Expand Down Expand Up @@ -4370,7 +4397,7 @@ public static MatrixBlock prepMatrixMultTransposeSelfInput( MatrixBlock m1, bool
MatrixBlock ret = m1;
final int rlen = m1.rlen;
final int clen = m1.clen;
boolean retSparse = isSparseOutputTSMM(m1, leftTranspose);
boolean retSparse = isSparseOutputTSMM(m1);

if( !leftTranspose && !retSparse && m1.sparse && rlen > 1) { //X%*%t(X) SPARSE MATRIX
//directly via LibMatrixReorg in order to prevent sparsity change
Expand Down Expand Up @@ -4489,16 +4516,16 @@ public static boolean isSparseOutputMatrixMult(MatrixBlock m1, MatrixBlock m2) {
return m2.clen < 4*1024 && sparseOut;
}

public static boolean isSparseOutputTSMM(MatrixBlock m1, boolean leftTranspose) {
return isSparseOutputTSMM(m1, leftTranspose, false);
public static boolean isSparseOutputTSMM(MatrixBlock m1) {
return isSparseOutputTSMM(m1, false);
}

public static boolean isSparseOutputTSMM(MatrixBlock m1, boolean leftTranspose, boolean ultraSparse) {
public static boolean isSparseOutputTSMM(MatrixBlock m1, boolean ultraSparse) {
double sp = m1.getSparsity();
double osp = OptimizerUtils.getMatMultSparsity(sp, sp, m1.rlen, m1.clen, m1.rlen, false);
double sp_threshold = ultraSparse ?
MatrixBlock.ULTRA_SPARSITY_TURN_POINT : MatrixBlock.ULTRA_SPARSITY_TURN_POINT2;
return !leftTranspose && m1.sparse && osp < sp_threshold;
return m1.sparse && osp < sp_threshold;
}

public static boolean isOuterProductTSMM(int rlen, int clen, boolean left) {
Expand Down
27 changes: 22 additions & 5 deletions ...ds/test/functions/binary/matrix_full_other/FullMatrixMultiplicationTransposeSelfTest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,12 @@ public void testVVLeftSparseCP() {
public void testVVRightSparseCP() {
runTransposeSelfVectorMultiplicationTest(MMTSJType.RIGHT, ExecType.CP, true);
}


@Test
public void testLeftUltraSparseCP() {
runTransposeSelfUltraSparseTest(MMTSJType.LEFT);
}

@Test
public void testRightUltraSparseCP() {
runTransposeSelfUltraSparseTest(MMTSJType.RIGHT);
Expand Down Expand Up @@ -282,15 +287,27 @@ private void runTransposeSelfUltraSparseTest( MMTSJType type )
TestUtils.compareMatrices(G, Gtt, 1e-16);

//single-threaded core operations
MatrixBlock R11 = G.transposeSelfMatrixMultOperations(new MatrixBlock(), MMTSJType.RIGHT);
MatrixBlock R12 = LibMatrixMult.matrixMult(G, Gt);
MatrixBlock R11 = G.transposeSelfMatrixMultOperations(new MatrixBlock(), type);
MatrixBlock R12;
if (type.isLeft()) {
R12 = LibMatrixMult.matrixMult(Gt, G);
}
else {
R12 = LibMatrixMult.matrixMult(G, Gt);
}
Assert.assertEquals(R11.getNonZeros(), R12.getNonZeros());
TestUtils.compareMatrices(R11, R12, 1e-8);

//multi-threaded core operations
int k = InfrastructureAnalyzer.getLocalParallelism();
MatrixBlock R21 = G.transposeSelfMatrixMultOperations(new MatrixBlock(), MMTSJType.RIGHT, k);
MatrixBlock R22 = LibMatrixMult.matrixMult(G, Gt, k);
MatrixBlock R21 = G.transposeSelfMatrixMultOperations(new MatrixBlock(), type, k);
MatrixBlock R22;
if (type.isLeft()) {
R22 = LibMatrixMult.matrixMult(Gt, G, k);
}
else {
R22 = LibMatrixMult.matrixMult(G, Gt, k);
}
Assert.assertEquals(R21.getNonZeros(), R22.getNonZeros());
TestUtils.compareMatrices(R21, R22, 1e-8);
}
Expand Down