diff --git a/src/main/java/org/apache/sysds/hops/estim/EstimatorRowWise.java b/src/main/java/org/apache/sysds/hops/estim/EstimatorRowWise.java new file mode 100644 index 00000000000..5fd85783ba8 --- /dev/null +++ b/src/main/java/org/apache/sysds/hops/estim/EstimatorRowWise.java @@ -0,0 +1,325 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.sysds.hops.estim; + +import org.apache.commons.lang3.ArrayUtils; +import org.apache.commons.lang3.NotImplementedException; +import org.apache.sysds.hops.OptimizerUtils; +import org.apache.sysds.runtime.data.SparseRow; +import org.apache.sysds.runtime.matrix.data.MatrixBlock; +import org.apache.sysds.runtime.meta.DataCharacteristics; +import org.apache.sysds.runtime.meta.MatrixCharacteristics; + +import java.util.stream.DoubleStream; +import java.util.stream.IntStream; + +/** + * This estimator implements an approach based on row-wise sparsity estimation, + * introduced in + * Lin, Chunxu, Wensheng Luo, Yixiang Fang, Chenhao Ma, Xilin Liu and Yuchi Ma: + * On Efficient Large Sparse Matrix Chain Multiplication. + * Proceedings of the ACM on Management of Data 2 (2024): 1 - 27. + */ +public class EstimatorRowWise extends SparsityEstimator { + @Override + public DataCharacteristics estim(MMNode root) { + estimInternChain(root); + double sparsity = DoubleStream.of((double[])root.getSynopsis()).average().orElse(0); + + DataCharacteristics outputCharacteristics = deriveOutputCharacteristics(root, sparsity); + return root.setDataCharacteristics(outputCharacteristics); + } + + @Override + public double estim(MatrixBlock m1, MatrixBlock m2) { + return estim(m1, m2, OpCode.MM); + } + + @Override + public double estim(MatrixBlock m1, MatrixBlock m2, OpCode op) { + if( isExactMetadataOp(op, m1.getNumColumns()) ) { + return estimExactMetaData(m1.getDataCharacteristics(), + m2.getDataCharacteristics(), op).getSparsity(); + } + + double[] rsOut = estimIntern(m1, m2, op); + return DoubleStream.of(rsOut).average().orElse(0); + } + + @Override + public double estim(MatrixBlock m1, OpCode op) { + if( isExactMetadataOp(op, m1.getNumColumns()) ) + return estimExactMetaData(m1.getDataCharacteristics(), null, op).getSparsity(); + + double[] rsOut = estimIntern(m1, op); + return DoubleStream.of(rsOut).average().orElse(0); + } + + private void estimInternChain(MMNode node) { + estimInternChain(node, null, null); + } + + private void estimInternChain(MMNode node, double[] rsRightNeighbor, OpCode opRightNeighbor) { + double[] rsOut; + if(node.isLeaf()) { + MatrixBlock mb = node.getData(); + if(rsRightNeighbor != null) + rsOut = estimIntern(mb, rsRightNeighbor, opRightNeighbor); + else + rsOut = getRowWiseSparsityVector(mb); + } + else { + switch(node.getOp()) { + case MM: + estimInternChain(node.getRight(), rsRightNeighbor, opRightNeighbor); + estimInternChain(node.getLeft(), (double[])(node.getRight().getSynopsis()), node.getOp()); + rsOut = (double[])node.getLeft().getSynopsis(); + break; + case CBIND: + /** NOTE: considering the current node as new DAG for estimation (cut), since the row sparsity of + * the right neighbor cannot be aggregated into a cbind operation when having only row sparsity vectors + */ + estimInternChain(node.getLeft()); + estimInternChain(node.getRight()); + double[] rsCBind = estimInternCBind((double[])(node.getLeft().getSynopsis()), (double[])(node.getRight().getSynopsis())); + if(rsRightNeighbor != null) { + rsOut = (double[])estimInternMMFallback(rsCBind, rsRightNeighbor); + if(opRightNeighbor != OpCode.MM) + throw new NotImplementedException("Fallback sparsity estimation has only been " + + "considered for MM operation w/ right neighbor yet."); + } + else + rsOut = (double[])rsCBind; + break; + case RBIND: + /** NOTE: considering the current node as new DAG for estimation (cut), since the row sparsity of + * the right neighbor cannot be aggregated into an rbind operation when having only row sparsity vectors + */ + estimInternChain(node.getLeft()); + estimInternChain(node.getRight()); + double[] rsRBind = estimInternRBind((double[])(node.getLeft().getSynopsis()), (double[])(node.getRight().getSynopsis())); + if(rsRightNeighbor != null) { + rsOut = (double[])estimInternMMFallback(rsRBind, rsRightNeighbor); + if(opRightNeighbor != OpCode.MM) + throw new NotImplementedException("Fallback sparsity estimation has only been " + + "considered for MM operation w/ right neighbor yet."); + } + else + rsOut = (double[])rsRBind; + break; + case PLUS: + /** NOTE: considering the current node as new DAG for estimation (cut), since the row sparsity of + * the right neighbor cannot be aggregated into an element-wise operation when having only row sparsity vectors + */ + estimInternChain(node.getLeft()); + estimInternChain(node.getRight()); + double[] rsPlus = estimInternPlus((double[])(node.getLeft().getSynopsis()), (double[])(node.getRight().getSynopsis())); + if(rsRightNeighbor != null) { + rsOut = (double[])estimInternMMFallback(rsPlus, rsRightNeighbor); + if(opRightNeighbor != OpCode.MM) + throw new NotImplementedException("Fallback sparsity estimation has only been " + + "considered for MM operation w/ right neighbor yet."); + } + else + rsOut = (double[])rsPlus; + break; + case MULT: + /** NOTE: considering the current node as new DAG for estimation (cut), since the row sparsity of + * the right neighbor cannot be aggregated into an element-wise operation when having only row sparsity vectors + */ + estimInternChain(node.getLeft()); + estimInternChain(node.getRight()); + double[] rsMult = estimInternMult((double[])(node.getLeft().getSynopsis()), (double[])(node.getRight().getSynopsis())); + if(rsRightNeighbor != null) { + rsOut = (double[])estimInternMMFallback(rsMult, rsRightNeighbor); + if(opRightNeighbor != OpCode.MM) + throw new NotImplementedException("Fallback sparsity estimation has only been " + + "considered for MM operation w/ right neighbor yet."); + } + else + rsOut = (double[])rsMult; + break; + default: + throw new NotImplementedException("Chain estimation for operator " + node.getOp().toString() + + " is not supported yet."); + } + } + node.setSynopsis(rsOut); + node.setDataCharacteristics(deriveOutputCharacteristics(node, DoubleStream.of(rsOut).average().orElse(0))); + return; + } + + private double[] estimIntern(MatrixBlock m1, MatrixBlock m2, OpCode op) { + double[] rsM2 = getRowWiseSparsityVector(m2); + return estimIntern(m1, rsM2, op); + } + + private double[] estimIntern(MatrixBlock m1, double[] rsM2, OpCode op) { + switch(op) { + case MM: + return estimInternMM(m1, rsM2); + case CBIND: + return estimInternCBind(getRowWiseSparsityVector(m1), rsM2); + case RBIND: + return estimInternRBind(getRowWiseSparsityVector(m1), rsM2); + case PLUS: + return estimInternPlus(getRowWiseSparsityVector(m1), rsM2); + case MULT: + return estimInternMult(getRowWiseSparsityVector(m1), rsM2); + default: + throw new NotImplementedException("Sparsity estimation for operation " + op.toString() + " not supported yet."); + } + } + + private double[] estimIntern(MatrixBlock mb, OpCode op) { + switch(op) { + case DIAG: + return estimInternDiag(mb); + default: + throw new NotImplementedException("Sparsity estimation for operation " + op.toString() + " not supported yet."); + } + } + + // Corresponds to Algorithm 1 in the publication + private double[] estimInternMM(MatrixBlock m1, double[] rsM2) { + double[] rsOut = IntStream.range(0, m1.getNumRows()).mapToDouble( + r -> (double) 1 - IntStream.of(getNonZeroColumnIndices(m1, r)).mapToDouble( + c -> (double) 1 - rsM2[c] + ).reduce((double) 1, (currentVal, val) -> currentVal * val)) + .toArray(); + return rsOut; + } + + // NOTE: this is the best estimation possible when we only have the two row sparsity vectors + private double[] estimInternMMFallback(double[] rsM1, double[] rsM2) { + // NOTE: Considering the average would probably not be far off while saving computing time + // double avgRsM2 = DoubleStream.of(rsM2).average().orElse(0); + // double[] rsOut = DoubleStream.of(rsM1).map( + // rsM1I -> (double) 1 - Math.pow((double) 1 - (rsM1I * avgRsM2), rsM2.length)).toArray(); + double[] rsOut = DoubleStream.of(rsM1).map( + rsM1I -> (double) 1 - DoubleStream.of(rsM2).reduce((double) 1, + (currentVal, rsM2J) -> currentVal * ((double) 1 - (rsM1I * rsM2J)))).toArray(); + return rsOut; + } + + private double[] estimInternCBind(double[] rsM1, double[] rsM2) { + // FIXME: this assumes that the number of columns is equivalent for both inputs + return IntStream.range(0, rsM1.length).mapToDouble( + idx -> (rsM1[idx] + rsM2[idx]) / (double) 2).toArray(); + } + + private double[] estimInternRBind(double[] rsM1, double[] rsM2) { + return ArrayUtils.addAll(rsM1, rsM2); + } + + private double[] estimInternPlus(double[] rsM1, double[] rsM2) { + // row-wise average case estimates + // rsM1 + rsM2 - (rsM1 * rsM2) + return IntStream.range(0, rsM1.length).mapToDouble( + idx -> rsM1[idx] + rsM2[idx] - (rsM1[idx] * rsM2[idx])).toArray(); + } + + private double[] estimInternMult(double[] rsM1, double[] rsM2) { + // row-wise average case estimates + // rsM1 * rsM2 + return IntStream.range(0, rsM1.length).mapToDouble( + idx -> rsM1[idx] * rsM2[idx]).toArray(); + } + + private double[] estimInternDiag(MatrixBlock mb) { + double[] rsOut = IntStream.range(0, mb.getNumRows()).mapToDouble( + rIdx -> (mb.get(rIdx, rIdx) == 0) ? 0d : 1d) + .toArray(); + return rsOut; + } + + private double[] getRowWiseSparsityVector(MatrixBlock mb) { + int numRows = mb.getNumRows(); + if(mb.isInSparseFormat()) { + double[] rsArray = new double[numRows]; + for(int counter = 0; counter < numRows; counter++) { + SparseRow sparseRow = mb.getSparseBlock().get(counter); + rsArray[counter] = (sparseRow == null) ? 0 : (double) sparseRow.size() / mb.getNumColumns(); + } + return rsArray; + } + else { + return IntStream.range(0, numRows).mapToDouble( + rIdx -> (double) mb.getDenseBlock().countNonZeros(rIdx) / mb.getNumColumns()) + .toArray(); + } + } + + private int[] getNonZeroColumnIndices(MatrixBlock mb, final int rIdx) { + int[] nonZeroCols; + if(mb.isInSparseFormat()) { + SparseRow sparseRow = mb.getSparseBlock().get(rIdx); + nonZeroCols = (sparseRow == null) ? new int[0] : sparseRow.indexes(); + } + else { + nonZeroCols = IntStream.range(0, mb.getNumColumns()) + .filter(cIdx -> mb.get(rIdx, cIdx) != 0).toArray(); + } + return nonZeroCols; + } + + public static DataCharacteristics deriveOutputCharacteristics(MMNode node, double spOut) { + if(node.isLeaf() || + (node.getDataCharacteristics() != null && node.getDataCharacteristics().getNonZeros() != -1)) { + return node.getDataCharacteristics(); + } + + MMNode nodeLeft = node.getLeft(); + MMNode nodeRight = node.getRight(); + int leftNRow = nodeLeft.getRows(); + int leftNCol = nodeLeft.getCols(); + int rightNRow = nodeRight.getRows(); + int rightNCol = nodeRight.getCols(); + switch(node.getOp()) { + case MM: + return new MatrixCharacteristics(leftNRow, rightNCol, + OptimizerUtils.getNnz(leftNRow, rightNCol, spOut)); + case MULT: + case PLUS: + case NEQZERO: + case EQZERO: + return new MatrixCharacteristics(leftNRow, leftNCol, + OptimizerUtils.getNnz(leftNRow, leftNCol, spOut)); + case RBIND: + return new MatrixCharacteristics(leftNRow+rightNRow, leftNCol, + OptimizerUtils.getNnz(leftNRow+rightNRow, leftNCol, spOut)); + case CBIND: + return new MatrixCharacteristics(leftNRow, leftNCol+rightNCol, + OptimizerUtils.getNnz(leftNRow, leftNCol+rightNCol, spOut)); + case DIAG: + int ncol = (leftNCol == 1) ? leftNRow : 1; + return new MatrixCharacteristics(leftNRow, ncol, + OptimizerUtils.getNnz(leftNRow, ncol, spOut)); + case TRANS: + return new MatrixCharacteristics(leftNCol, leftNRow, + OptimizerUtils.getNnz(leftNCol, leftNRow, spOut)); + case RESHAPE: + throw new NotImplementedException("Characteristics derivation for " + node.getOp() +" has not been " + + "implemented yet, but could be implemented similar to EstimatorMatrixHistogram.java"); + default: + throw new NotImplementedException(); + } + } +}; diff --git a/src/test/java/org/apache/sysds/test/component/estim/OpBindChainTest.java b/src/test/java/org/apache/sysds/test/component/estim/OpBindChainTest.java index 35efedaf625..9626f9eb74f 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/OpBindChainTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/OpBindChainTest.java @@ -19,11 +19,11 @@ package org.apache.sysds.test.component.estim; -import org.junit.Test; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.MMNode; import org.apache.sysds.hops.estim.SparsityEstimator; @@ -32,136 +32,146 @@ import org.apache.sysds.runtime.matrix.data.MatrixBlock; import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; + +import java.util.Arrays; +import java.util.Collection; + import org.apache.commons.lang3.NotImplementedException; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; /** - * this is the basic operation check for all estimators with single operations + * this is the basic operation check for all estimators with chains of operations including binding operations */ -public class OpBindChainTest extends AutomatedTestBase +@RunWith(value = Parameterized.class) +public class OpBindChainTest extends AutomatedTestBase { - private final static int m = 600; - private final static int k = 300; - private final static int n = 100; - private final static double[] sparsity = new double[]{0.2, 0.4}; -// private final static OpCode mult = OpCode.MULT; -// private final static OpCode plus = OpCode.PLUS; - private final static OpCode rbind = OpCode.RBIND; - private final static OpCode cbind = OpCode.CBIND; -// private final static OpCode eqzero = OpCode.EQZERO; -// private final static OpCode diag = OpCode.DIAG; -// private final static OpCode neqzero = OpCode.NEQZERO; -// private final static OpCode trans = OpCode.TRANS; -// private final static OpCode reshape = OpCode.RESHAPE; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int k; + @Parameterized.Parameter(2) + public int n; + @Parameterized.Parameter(3) + public double[] sparsity; @Override public void setUp() { //do nothing } - + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, k, n, sparsity} + {600, 300, 100, new double[]{0.2, 0.4}}, + {600, 200, 300, new double[]{0.1, 0.15}}, + }); + } + //Average Case @Test public void testAvgRbind() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.RBIND); } @Test public void testAvgCbind() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.CBIND); } //Worst Case @Test public void testWorstRbind() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.RBIND); } @Test public void testWorstCbind() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.CBIND); } //DensityMap /*@Test public void testDMCaserbind() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.RBIND); } @Test public void testDMCasecbind() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.CBIND); }*/ //MNC @Test public void testMNCRbind() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.RBIND); } @Test public void testMNCCbind() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.CBIND); } //Bitset @Test public void testBitsetCaserbind() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.RBIND); } @Test public void testBitsetCasecbind() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.CBIND); } //Layered Graph @Test public void testLGCaserbind() { runSparsityEstimateTest( - new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 7), - m, k, n, sparsity, rbind); + new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 7), OpCode.RBIND); } @Test public void testLGCasecbind() { runSparsityEstimateTest( - new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 3), - m, k, n, sparsity, cbind); + new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 3), OpCode.CBIND); } - - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int k, int n, double[] sp, OpCode op) { - MatrixBlock m1; + + // Row Wise Sparsity Estimator + @Test + public void testRowWiseRbind() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.RBIND); + } + + @Test + public void testRowWiseCbind() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.CBIND); + } + + + private void runSparsityEstimateTest(SparsityEstimator estim, OpCode op) { + MatrixBlock m1 = MatrixBlock.randOperations(m, k, sparsity[0], 1, 1, "uniform", 3); MatrixBlock m2; MatrixBlock m3 = new MatrixBlock(); MatrixBlock m4; - MatrixBlock m5 = new MatrixBlock(); - double est = 0; switch(op) { case RBIND: - m1 = MatrixBlock.randOperations(m, k, sp[0], 1, 1, "uniform", 3); - m2 = MatrixBlock.randOperations(n, k, sp[1], 1, 1, "uniform", 7); + m2 = MatrixBlock.randOperations(n, k, sparsity[1], 1, 1, "uniform", 7); m1.append(m2, m3, false); - m4 = MatrixBlock.randOperations(k, m, sp[1], 1, 1, "uniform", 5); - m5 = m3.aggregateBinaryOperations(m3, m4, - new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); - est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), op), new MMNode(m4), OpCode.MM)).getSparsity(); - //System.out.println(est); - //System.out.println(m5.getSparsity()); + m4 = MatrixBlock.randOperations(k, m, sparsity[1], 1, 1, "uniform", 5); break; case CBIND: - m1 = MatrixBlock.randOperations(m, k, sp[0], 1, 1, "uniform", 3); - m2 = MatrixBlock.randOperations(m, n, sp[1], 1, 1, "uniform", 7); + m2 = MatrixBlock.randOperations(m, n, sparsity[1], 1, 1, "uniform", 7); m1.append(m2, m3, true); - m4 = MatrixBlock.randOperations(k+n, m, sp[1], 1, 1, "uniform", 5); - m5 = m3.aggregateBinaryOperations(m3, m4, - new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); - est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), op), new MMNode(m4), OpCode.MM)).getSparsity(); - //System.out.println(est); - //System.out.println(m5.getSparsity()); + m4 = MatrixBlock.randOperations(k+n, m, sparsity[1], 1, 1, "uniform", 5); break; default: throw new NotImplementedException(); } + MatrixBlock m5 = m3.aggregateBinaryOperations(m3, m4, + new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); + double est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), op), new MMNode(m4), OpCode.MM)).getSparsity(); //compare estimated and real sparsity TestUtils.compareScalars(est, m5.getSparsity(), (estim instanceof EstimatorBasicWorst) ? 5e-1 : diff --git a/src/test/java/org/apache/sysds/test/component/estim/OpBindTest.java b/src/test/java/org/apache/sysds/test/component/estim/OpBindTest.java index 3e7ad24fe86..c943a06be15 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/OpBindTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/OpBindTest.java @@ -19,146 +19,163 @@ package org.apache.sysds.test.component.estim; -import org.junit.Test; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.SparsityEstimator; import org.apache.sysds.hops.estim.SparsityEstimator.OpCode; import org.apache.sysds.runtime.matrix.data.MatrixBlock; import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; + +import java.util.Arrays; +import java.util.Collection; + import org.apache.commons.lang3.NotImplementedException; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; /** - * this is the basic operation check for all estimators with single operations + * this is the basic operation check for all estimators with binding operations */ -public class OpBindTest extends AutomatedTestBase +@RunWith(value = Parameterized.class) +public class OpBindTest extends AutomatedTestBase { - private final static int m = 600; - private final static int k = 300; - private final static int n = 100; - private final static double[] sparsity = new double[]{0.2, 0.4}; -// private final static OpCode mult = OpCode.MULT; -// private final static OpCode plus = OpCode.PLUS; - private final static OpCode rbind = OpCode.RBIND; - private final static OpCode cbind = OpCode.CBIND; -// private final static OpCode eqzero = OpCode.EQZERO; -// private final static OpCode diag = OpCode.DIAG; -// private final static OpCode neqzero = OpCode.NEQZERO; -// private final static OpCode trans = OpCode.TRANS; -// private final static OpCode reshape = OpCode.RESHAPE; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int k; + @Parameterized.Parameter(2) + public int n; + @Parameterized.Parameter(3) + public double[] sparsity; @Override public void setUp() { //do nothing } - + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, k, n, sparsity} + {600, 300, 100, new double[]{0.2, 0.4}}, + {600, 200, 300, new double[]{0.1, 0.15}}, + }); + } + //Average Case @Test public void testAvgRbind() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.RBIND); } @Test public void testAvgCbind() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.CBIND); } //Worst Case @Test public void testWorstRbind() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.RBIND); } @Test public void testWorstCbind() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.CBIND); } //DensityMap /*@Test public void testDMCaserbind() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.RBIND); } @Test public void testDMCasecbind() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.CBIND); }*/ //MNC @Test public void testMNCRbind() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.RBIND); } @Test public void testMNCCbind() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.CBIND); } //Bitset @Test public void testBitsetCasecbind() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.CBIND); } @Test public void testBitsetCaserbind() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.RBIND); } //Layered Graph @Test public void testLGCaserbind() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorLayeredGraph(), OpCode.RBIND); } @Test public void testLGCasecbind() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorLayeredGraph(), OpCode.CBIND); } //Sample /*@Test public void testSampleCaserbind() { - runSparsityEstimateTest(new EstimatorSample(), m, k, n, sparsity, rbind); + runSparsityEstimateTest(new EstimatorSample(), OpCode.RBIND); } @Test public void testSampleCasecbind() { - runSparsityEstimateTest(new EstimatorSample(), m, k, n, sparsity, cbind); + runSparsityEstimateTest(new EstimatorSample(), OpCode.CBIND); }*/ + + // Row Wise Sparsity Estimator + @Test + public void testRowWiseRbind() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.RBIND); + } + + @Test + public void testRowWiseCbind() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.CBIND); + } + - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int k, int n, double[] sp, OpCode op) { + private void runSparsityEstimateTest(SparsityEstimator estim, OpCode op) { MatrixBlock m1; MatrixBlock m2; MatrixBlock m3 = new MatrixBlock(); - double est = 0; switch(op) { case RBIND: - m1 = MatrixBlock.randOperations(m, k, sp[0], 1, 1, "uniform", 3); - m2 = MatrixBlock.randOperations(n, k, sp[1], 1, 1, "uniform", 3); + m1 = MatrixBlock.randOperations(m, k, sparsity[0], 1, 1, "uniform", 3); + m2 = MatrixBlock.randOperations(n, k, sparsity[1], 1, 1, "uniform", 3); m1.append(m2, m3, false); - est = estim.estim(m1, m2, op); - // System.out.println(est); - // System.out.println(m3.getSparsity()); break; case CBIND: - m1 = MatrixBlock.randOperations(10, 130, sp[0], 1, 1, "uniform", 3); - m2 = MatrixBlock.randOperations(10, 70, sp[1], 1, 1, "uniform", 3); + m1 = MatrixBlock.randOperations(10, 130, sparsity[0], 1, 1, "uniform", 3); + m2 = MatrixBlock.randOperations(10, 70, sparsity[1], 1, 1, "uniform", 3); m1.append(m2, m3); - est = estim.estim(m1, m2, op); - // System.out.println(est); - // System.out.println(m3.getSparsity()); break; default: throw new NotImplementedException(); } + double est = estim.estim(m1, m2, op); //compare estimated and real sparsity TestUtils.compareScalars(est, m3.getSparsity(), (estim instanceof EstimatorBasicWorst) ? 5e-1 : diff --git a/src/test/java/org/apache/sysds/test/component/estim/OpElemWChainTest.java b/src/test/java/org/apache/sysds/test/component/estim/OpElemWChainTest.java index a1b6594a927..da18067867b 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/OpElemWChainTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/OpElemWChainTest.java @@ -19,12 +19,12 @@ package org.apache.sysds.test.component.estim; -import org.junit.Test; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorDensityMap; import org.apache.sysds.hops.estim.MMNode; import org.apache.sysds.hops.estim.SparsityEstimator; @@ -36,120 +36,140 @@ import org.apache.sysds.runtime.matrix.operators.BinaryOperator; import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; + +import java.util.Arrays; +import java.util.Collection; + import org.apache.commons.lang3.NotImplementedException; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; /** - * this is the basic operation check for all estimators with single operations + * this is the basic operation check for all estimators with chains of operations including element-wise operations */ +@RunWith(value = Parameterized.class) public class OpElemWChainTest extends AutomatedTestBase { - private final static int m = 1600; - private final static int n = 700; - private final static double[] sparsity = new double[]{0.1, 0.04}; - private final static OpCode mult = OpCode.MULT; - private final static OpCode plus = OpCode.PLUS; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int n; + @Parameterized.Parameter(2) + public double[] sparsity; @Override public void setUp() { //do nothing } + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, n, sparsity} + {1600, 700, new double[]{0.1, 0.04}}, + {900, 1200, new double[]{0.01, 0.125}}, + }); + } + //Average Case @Test public void testAvgMult() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.MULT); } @Test public void testAvgPlus() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.PLUS); } //Worst Case @Test public void testWorstMult() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.MULT); } @Test public void testWorstPlus() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.PLUS); } //DensityMap @Test public void testDMMult() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.MULT); } @Test public void testDMPlus() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.PLUS); } //MNC @Test public void testMNCMult() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.MULT); } @Test public void testMNCPlus() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.PLUS); } //Bitset @Test public void testBitsetMult() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.MULT); } @Test public void testBitsetPlus() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.PLUS); } //Layered Graph @Test public void testLGCasemult() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), OpCode.MULT); } @Test public void testLGCaseplus() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), OpCode.PLUS); } - - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int n, double[] sp, OpCode op) { - MatrixBlock m1 = MatrixBlock.randOperations(m, n, sp[0], 1, 1, "uniform", 3); - MatrixBlock m2 = MatrixBlock.randOperations(m, n, sp[1], 1, 1, "uniform", 5); - MatrixBlock m3 = MatrixBlock.randOperations(n, m, sp[1], 1, 1, "uniform", 7); + + // Row Wise Sparsity Estimator + @Test + public void testRowWiseCaseMult() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.MULT); + } + + @Test + public void testRowWiseCasePlus() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.PLUS); + } + + private void runSparsityEstimateTest(SparsityEstimator estim, OpCode op) { + MatrixBlock m1 = MatrixBlock.randOperations(m, n, sparsity[0], 1, 1, "uniform", 3); + MatrixBlock m2 = MatrixBlock.randOperations(m, n, sparsity[1], 1, 1, "uniform", 5); + MatrixBlock m3 = MatrixBlock.randOperations(n, m, sparsity[1], 1, 1, "uniform", 7); MatrixBlock m4 = new MatrixBlock(); - MatrixBlock m5 = new MatrixBlock(); BinaryOperator bOp; - double est = 0; switch(op) { case MULT: bOp = new BinaryOperator(Multiply.getMultiplyFnObject()); - m1.binaryOperations(bOp, m2, m4); - m5 = m4.aggregateBinaryOperations(m4, m3, - new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); - est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), op), new MMNode(m3), OpCode.MM)).getSparsity(); - // System.out.println(m5.getSparsity()); - // System.out.println(est); break; case PLUS: bOp = new BinaryOperator(Plus.getPlusFnObject()); - m1.binaryOperations(bOp, m2, m4); - m5 = m4.aggregateBinaryOperations(m4, m3, - new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); - est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), op), new MMNode(m3), OpCode.MM)).getSparsity(); - // System.out.println(m5.getSparsity()); - // System.out.println(est); break; default: throw new NotImplementedException(); } + m1.binaryOperations(bOp, m2, m4); + MatrixBlock m5 = m4.aggregateBinaryOperations(m4, m3, + new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); + double est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), op), new MMNode(m3), OpCode.MM)).getSparsity(); + //compare estimated and real sparsity TestUtils.compareScalars(est, m5.getSparsity(), (estim instanceof EstimatorBasicWorst) ? 9e-1 : (estim instanceof EstimatorLayeredGraph) ? 7e-2 : 1e-2); diff --git a/src/test/java/org/apache/sysds/test/component/estim/OpElemWTest.java b/src/test/java/org/apache/sysds/test/component/estim/OpElemWTest.java index f8ddb91bcef..311ae50cb59 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/OpElemWTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/OpElemWTest.java @@ -19,12 +19,12 @@ package org.apache.sysds.test.component.estim; -import org.junit.Test; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorDensityMap; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.EstimatorSample; import org.apache.sysds.hops.estim.SparsityEstimator; @@ -35,124 +35,148 @@ import org.apache.sysds.runtime.matrix.operators.BinaryOperator; import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; + +import java.util.Arrays; +import java.util.Collection; + import org.apache.commons.lang3.NotImplementedException; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + /** - * this is the basic operation check for all estimators with single operations + * this is the basic operation check for all estimators with element-wise operations */ +@RunWith(value = Parameterized.class) public class OpElemWTest extends AutomatedTestBase { - private final static int m = 1600; - private final static int n = 700; - private final static double[] sparsity = new double[]{0.2, 0.4}; - private final static OpCode mult = OpCode.MULT; - private final static OpCode plus = OpCode.PLUS; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int n; + @Parameterized.Parameter(2) + public double[] sparsity; @Override public void setUp() { //do nothing } + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, n, sparsity} + {1600, 700, new double[]{0.2, 0.4}}, + {900, 1200, new double[]{0.01, 0.125}}, + }); + } + //Average Case @Test public void testAvgMult() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.MULT); } @Test public void testAvgPlus() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorBasicAvg(), OpCode.PLUS); } //Worst Case @Test public void testWorstMult() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.MULT); } @Test public void testWorstPlus() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorBasicWorst(), OpCode.PLUS); } //DensityMap @Test public void testDMMult() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.MULT); } @Test public void testDMPlus() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorDensityMap(), OpCode.PLUS); } //MNC @Test public void testMNCMult() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.MULT); } @Test public void testMNCPlus() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorMatrixHistogram(), OpCode.PLUS); } //Bitset @Test public void testBitsetMult() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.MULT); } @Test public void testBitsetPlus() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorBitsetMM(), OpCode.PLUS); } //Layered Graph @Test public void testLGCasemult() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorLayeredGraph(), OpCode.MULT); } @Test public void testLGCaseplus() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorLayeredGraph(), OpCode.PLUS); } //Sample @Test public void testSampleMult() { - runSparsityEstimateTest(new EstimatorSample(), m, n, sparsity, mult); + runSparsityEstimateTest(new EstimatorSample(), OpCode.MULT); } @Test public void testSamplePlus() { - runSparsityEstimateTest(new EstimatorSample(), m, n, sparsity, plus); + runSparsityEstimateTest(new EstimatorSample(), OpCode.PLUS); } - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int n, double[] sp, OpCode op) { - MatrixBlock m1 = MatrixBlock.randOperations(m, n, sp[0], 1, 1, "uniform", 3); - MatrixBlock m2 = MatrixBlock.randOperations(m, n, sp[1], 1, 1, "uniform", 7); + + // Row Wise Sparsity Estimator + @Test + public void testRowWiseMult() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.MULT); + } + + @Test + public void testRowWisePlus() { + runSparsityEstimateTest(new EstimatorRowWise(), OpCode.PLUS); + } + + private void runSparsityEstimateTest(SparsityEstimator estim, OpCode op) { + MatrixBlock m1 = MatrixBlock.randOperations(m, n, sparsity[0], 1, 1, "uniform", 3); + MatrixBlock m2 = MatrixBlock.randOperations(m, n, sparsity[1], 1, 1, "uniform", 7); MatrixBlock m3 = new MatrixBlock(); BinaryOperator bOp; - double est = 0; switch(op) { case MULT: bOp = new BinaryOperator(Multiply.getMultiplyFnObject()); - m1.binaryOperations(bOp, m2, m3); - est = estim.estim(m1, m2, op); - // System.out.println(est); - // System.out.println(m3.getSparsity()); break; case PLUS: bOp = new BinaryOperator(Plus.getPlusFnObject()); - m1.binaryOperations(bOp, m2, m3); - est = estim.estim(m1, m2, op); - // System.out.println(est); - // System.out.println(m3.getSparsity()); break; - default: - throw new NotImplementedException(); + default: + throw new NotImplementedException(); } + m1.binaryOperations(bOp, m2, m3); + double est = estim.estim(m1, m2, op); //compare estimated and real sparsity TestUtils.compareScalars(est, m3.getSparsity(), (estim instanceof EstimatorBasicWorst) ? 5e-1 : (estim instanceof EstimatorLayeredGraph) ? 3e-2 : 5e-3); diff --git a/src/test/java/org/apache/sysds/test/component/estim/OpSingleTest.java b/src/test/java/org/apache/sysds/test/component/estim/OpSingleTest.java index d40f84c4fb3..14696fa5727 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/OpSingleTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/OpSingleTest.java @@ -19,255 +19,297 @@ package org.apache.sysds.test.component.estim; -import org.apache.sysds.runtime.matrix.data.LibMatrixReorg; -import org.junit.Test; import org.apache.commons.lang3.NotImplementedException; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.SparsityEstimator; import org.apache.sysds.hops.estim.SparsityEstimator.OpCode; import org.apache.sysds.runtime.matrix.data.MatrixBlock; import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; +import org.apache.sysds.runtime.matrix.data.LibMatrixReorg; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import java.util.Arrays; +import java.util.Collection; + /** * this is the basic operation check for all estimators with single operations */ +@RunWith(value = Parameterized.class) public class OpSingleTest extends AutomatedTestBase { - private final static int m = 600; - private final static int k = 300; - private final static double sparsity = 0.2; -// private final static OpCode eqzero = OpCode.EQZERO; - private final static OpCode diag = OpCode.DIAG; - private final static OpCode neqzero = OpCode.NEQZERO; - private final static OpCode trans = OpCode.TRANS; - private final static OpCode reshape = OpCode.RESHAPE; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int k_param; + @Parameterized.Parameter(2) + public double sparsity; @Override public void setUp() { //do nothing } - + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, k_param, sparsity} + {600, 300, 0.2}, + {200, 1200, 0.6}, + }); + } + //Average Case // @Test // public void testAvgEqzero() { -// runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorBasicAvg(), k_param, OpCode.EQZERO); // } // @Test // public void testAvgDiag() { -// runSparsityEstimateTest(new EstimatorBasicAvg(), m, m, sparsity, diag); +// runSparsityEstimateTest(new EstimatorBasicAvg(), m, OpCode.DIAG); // } @Test public void testAvgNeqzero() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, sparsity, neqzero); + runSparsityEstimateTest(new EstimatorBasicAvg(), k_param, OpCode.NEQZERO); } @Test public void testAvgTrans() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, sparsity, trans); + runSparsityEstimateTest(new EstimatorBasicAvg(), k_param, OpCode.TRANS); } @Test public void testAvgReshape() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, sparsity, reshape); + runSparsityEstimateTest(new EstimatorBasicAvg(), k_param, OpCode.RESHAPE); } //Worst Case // @Test // public void testWorstEqzero() { -// runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorBasicWorst(), k_param, OpCode.EQZERO); // } // @Test // public void testWCasediag() { -// runSparsityEstimateTest(new EstimatorBasicWorst(), m, m, sparsity, diag); +// runSparsityEstimateTest(new EstimatorBasicWorst(), m, OpCode.DIAG); // } @Test public void testWorstNeqzero() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, sparsity, neqzero); + runSparsityEstimateTest(new EstimatorBasicWorst(), k_param, OpCode.NEQZERO); } @Test public void testWoestTrans() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, sparsity, trans); + runSparsityEstimateTest(new EstimatorBasicWorst(), k_param, OpCode.TRANS); } @Test public void testWorstReshape() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, sparsity, reshape); + runSparsityEstimateTest(new EstimatorBasicWorst(), k_param, OpCode.RESHAPE); } // //DensityMap // @Test // public void testDMCaseeqzero() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.EQZERO); // } // // @Test // public void testDMCasediag() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, m, sparsity, diag); +// runSparsityEstimateTest(new EstimatorDensityMap(), m, OpCode.DIAG); // } // // @Test // public void testDMCaseneqzero() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, neqzero); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.NEQZERO); // } // // @Test // public void testDMCasetrans() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, trans); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.TRANS); // } // // @Test // public void testDMCasereshape() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, reshape); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.RESHAPE); // } // // //MNC // @Test // public void testMNCCaseeqzero() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.EQZERO); // } // // @Test // public void testMNCCasediag() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, m, sparsity, diag); +// runSparsityEstimateTest(new EstimatorDensityMap(), m, OpCode.DIAG); // } // // @Test // public void testMNCCaseneqzero() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, neqzero); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.NEQZERO); // } // // @Test // public void testMNCCasetrans() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, trans); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.TRANS); // } // // @Test // public void testMNCCasereshape() { -// runSparsityEstimateTest(new EstimatorDensityMap(), m, k, sparsity, reshape); +// runSparsityEstimateTest(new EstimatorDensityMap(), k_param, OpCode.RESHAPE); // } // //Bitset // @Test // public void testBitsetCaseeqzero() { -// runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorBitsetMM(), k_param, OpCode.EQZERO); // } // @Test // public void testBitsetCasediag() { -// runSparsityEstimateTest(new EstimatorBitsetMM(), m, m, sparsity, diag); +// runSparsityEstimateTest(new EstimatorBitsetMM(), m, OpCode.DIAG); // } @Test public void testBitsetNeqzero() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, sparsity, neqzero); + runSparsityEstimateTest(new EstimatorBitsetMM(), k_param, OpCode.NEQZERO); } @Test public void testBitsetTrans() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, sparsity, trans); + runSparsityEstimateTest(new EstimatorBitsetMM(), k_param, OpCode.TRANS); } @Test public void testBitsetReshape() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, sparsity, reshape); + runSparsityEstimateTest(new EstimatorBitsetMM(), k_param, OpCode.RESHAPE); } // //Layered Graph // @Test // public void testLGCaseeqzero() { -// runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), k_param, OpCode.EQZERO); // } // @Test public void testLGCasediagM() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, m, sparsity, diag); + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), m, OpCode.DIAG); } @Test public void testLGCasediagV() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, 1, sparsity, diag); + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), 1, OpCode.DIAG); } // // @Test // public void testLGCaseneqzero() { -// runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, sparsity, neqzero); +// runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), k_param, OpCode.NEQZERO); // } // @Test public void testLGCasetrans() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, sparsity, trans); + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), k_param, OpCode.TRANS); } // @Test // public void testLGCasereshape() { -// runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, sparsity, reshape); +// runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13), k_param, OpCode.RESHAPE); // } // // //Sample // @Test // public void testSampleCaseeqzero() { -// runSparsityEstimateTest(new EstimatorSample(), m, k, sparsity, eqzero); +// runSparsityEstimateTest(new EstimatorSample(), k_param, OpCode.EQZERO); // } // // @Test // public void testSampleCasediag() { -// runSparsityEstimateTest(new EstimatorSample(), m, m, sparsity, diag); +// runSparsityEstimateTest(new EstimatorSample(), m, OpCode.DIAG); // } // // @Test // public void testSampleCaseneqzero() { -// runSparsityEstimateTest(new EstimatorSample(), m, k, sparsity, neqzero); +// runSparsityEstimateTest(new EstimatorSample(), k_param, OpCode.NEQZERO); // } // // @Test // public void testSampleCasetrans() { -// runSparsityEstimateTest(new EstimatorSample(), m, k, sparsity, trans); +// runSparsityEstimateTest(new EstimatorSample(), k_param, OpCode.TRANS); // } // // @Test // public void testSampleCasereshape() { -// runSparsityEstimateTest(new EstimatorSample(), m, k, sparsity, reshape); +// runSparsityEstimateTest(new EstimatorSample(), k_param, OpCode.RESHAPE); // } - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int k, double sp, OpCode op) { - MatrixBlock m1 = MatrixBlock.randOperations(m, k, sp, 1, 1, "uniform", 3); - MatrixBlock m2 = new MatrixBlock(); - double est = 0; + + // Row Wise Sparsity Estimator + @Test + public void testRowWiseEqzero() { + runSparsityEstimateTest(new EstimatorRowWise(), k_param, OpCode.EQZERO); + } + + @Test + public void testRowWiseDiagM() { + runSparsityEstimateTest(new EstimatorRowWise(), m, OpCode.DIAG); + } + + @Test + public void testRowWiseDiagV() { + runSparsityEstimateTest(new EstimatorRowWise(), 1, OpCode.DIAG); + } + + @Test + public void testRowWiseNeqzero() { + runSparsityEstimateTest(new EstimatorRowWise(), k_param, OpCode.NEQZERO); + } + + @Test + public void testRowWiseTrans() { + runSparsityEstimateTest(new EstimatorRowWise(), k_param, OpCode.TRANS); + } + + @Test + public void testRowWiseReshape() { + runSparsityEstimateTest(new EstimatorRowWise(), k_param, OpCode.RESHAPE); + } + + private void runSparsityEstimateTest(SparsityEstimator estim, int k, OpCode op) { + MatrixBlock m1 = MatrixBlock.randOperations(m, k, sparsity, 1, 1, "uniform", 3); + MatrixBlock m2; + double ref = -1; switch(op) { case EQZERO: - //TODO find out how to do eqzero + ref = 1 - m1.getSparsity(); + break; case DIAG: m2 = m1.getNumColumns() == 1 ? LibMatrixReorg.diag(m1, new MatrixBlock(m1.getNumRows(), m1.getNumRows(), false)) : LibMatrixReorg.diag(m1, new MatrixBlock(m1.getNumRows(), 1, false)); - est = estim.estim(m1, op); + ref = m2.getSparsity(); break; case NEQZERO: - m2 = m1; - est = estim.estim(m1, op); - break; case TRANS: - m2 = m1; - est = estim.estim(m1, op); - break; case RESHAPE: m2 = m1; - est = estim.estim(m1, op); + ref = m2.getSparsity(); break; default: throw new NotImplementedException(); } + double est = estim.estim(m1, op); //compare estimated and real sparsity - TestUtils.compareScalars(est, m2.getSparsity(), + TestUtils.compareScalars(est, ref, (estim instanceof EstimatorBasicWorst) ? 5e-1 : (estim instanceof EstimatorLayeredGraph) ? 3e-2 : 2e-2); } diff --git a/src/test/java/org/apache/sysds/test/component/estim/OuterProductTest.java b/src/test/java/org/apache/sysds/test/component/estim/OuterProductTest.java index fdc33d878db..f0486a58cab 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/OuterProductTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/OuterProductTest.java @@ -20,12 +20,19 @@ package org.apache.sysds.test.component.estim; import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import java.util.Arrays; +import java.util.Collection; + import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorDensityMap; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorSample; import org.apache.sysds.hops.estim.SparsityEstimator; import org.apache.sysds.runtime.instructions.InstructionUtils; @@ -37,122 +44,90 @@ * This is a basic sanity check for all estimator, which need * to compute the exact sparsity for the special case of outer products. */ -public class OuterProductTest extends AutomatedTestBase +@RunWith(value = Parameterized.class) +public class OuterProductTest extends AutomatedTestBase { - private final static int m = 1154; - private final static int k = 1; - private final static int n = 900; - private final static double[] case1 = new double[]{0.1, 0.7}; - private final static double[] case2 = new double[]{0.6, 0.7}; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int k; + @Parameterized.Parameter(2) + public int n; + @Parameterized.Parameter(3) + public double[] sparsity; @Override public void setUp() { //do nothing } - @Test - public void testBasicAvgCase1() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, case1); + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, k, n, sparsity} + {1154, 1, 900, new double[]{0.1, 0.7}}, + {1154, 1, 900, new double[]{0.6, 0.7}}, + }); } - + @Test - public void testBasicAvgCase2() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, case2); + public void testBasicAvgCase1() { + runSparsityEstimateTest(new EstimatorBasicAvg()); } @Test public void testBasicWorstCase1() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, case1); - } - - @Test - public void testBasicWorstCase2() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, case2); + runSparsityEstimateTest(new EstimatorBasicWorst()); } @Test public void testDensityMapCase1() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, case1); - } - - @Test - public void testDensityMapCase2() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, case2); + runSparsityEstimateTest(new EstimatorDensityMap()); } @Test public void testDensityMap7Case1() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m, k, n, case1); - } - - @Test - public void testDensityMap7Case2() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m, k, n, case2); + runSparsityEstimateTest(new EstimatorDensityMap(7)); } @Test public void testBitsetMatrixCase1() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, case1); - } - - @Test - public void testBitsetMatrixCase2() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, case2); + runSparsityEstimateTest(new EstimatorBitsetMM()); } @Test public void testMatrixHistogramCase1() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, k, n, case1); - } - - @Test - public void testMatrixHistogramCase2() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, k, n, case2); + runSparsityEstimateTest(new EstimatorMatrixHistogram(false)); } @Test public void testMatrixHistogramExceptCase1() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, k, n, case1); - } - - @Test - public void testMatrixHistogramExceptCase2() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, k, n, case2); + runSparsityEstimateTest(new EstimatorMatrixHistogram(true)); } @Test public void testSamplingDefCase1() { - runSparsityEstimateTest(new EstimatorSample(), m, k, n, case1); - } - - @Test - public void testSamplingDefCase2() { - runSparsityEstimateTest(new EstimatorSample(), m, k, n, case2); + runSparsityEstimateTest(new EstimatorSample()); } @Test public void testSampling20Case1() { - runSparsityEstimateTest(new EstimatorSample(0.2), m, k, n, case1); - } - - @Test - public void testSampling20Case2() { - runSparsityEstimateTest(new EstimatorSample(0.2), m, k, n, case2); + runSparsityEstimateTest(new EstimatorSample(0.2)); } @Test public void testLayeredGraphCase1() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, case1); + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13)); } @Test - public void testLayeredGraphCase2() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, case2); + public void testRowWiseCase1() { + runSparsityEstimateTest(new EstimatorRowWise()); } - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int k, int n, double[] sp) { - MatrixBlock m1 = MatrixBlock.randOperations(m, k, sp[0], 1, 1, "uniform", 3); - MatrixBlock m2 = MatrixBlock.randOperations(k, n, sp[1], 1, 1, "uniform", 3); + private void runSparsityEstimateTest(SparsityEstimator estim) { + MatrixBlock m1 = MatrixBlock.randOperations(m, k, sparsity[0], 1, 1, "uniform", 3); + MatrixBlock m2 = MatrixBlock.randOperations(k, n, sparsity[1], 1, 1, "uniform", 3); MatrixBlock m3 = m1.aggregateBinaryOperations(m1, m2, new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); diff --git a/src/test/java/org/apache/sysds/test/component/estim/SelfProductTest.java b/src/test/java/org/apache/sysds/test/component/estim/SelfProductTest.java index d99f38d939b..58e7f2195c2 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/SelfProductTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/SelfProductTest.java @@ -19,7 +19,14 @@ package org.apache.sysds.test.component.estim; +import org.junit.Assume; import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import java.util.Arrays; +import java.util.Collection; + import org.apache.sysds.hops.OptimizerUtils; import org.apache.sysds.hops.estim.EstimationUtils; import org.apache.sysds.hops.estim.EstimatorBasicAvg; @@ -28,6 +35,7 @@ import org.apache.sysds.hops.estim.EstimatorDensityMap; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorSample; import org.apache.sysds.hops.estim.EstimatorSampleRa; import org.apache.sysds.hops.estim.SparsityEstimator; @@ -36,142 +44,118 @@ import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; -public class SelfProductTest extends AutomatedTestBase +@RunWith(value = Parameterized.class) +public class SelfProductTest extends AutomatedTestBase { - private final static int m = 2500; - private final static double sparsity0 = 0.5; - private final static double sparsity1 = 0.1; - private final static double sparsity2 = 0.0001; - private final static double sparsity3 = 0.000001; - private final static double eps1 = 0.05; - private final static double eps2 = 1e-4; - private final static double eps3 = 0; - + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public double sparsity; @Override public void setUp() { //do nothing } - - @Test - public void testBasicAvgCase() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorBasicAvg(), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorBasicAvg(), m, sparsity2); - runSparsityEstimateTest(new EstimatorBasicAvg(), m, sparsity3); + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, sparsity} + {625, 0.5}, + {1250, 0.1}, + {2500, 0.0001}, + {2500, 0.000001}, + }); } - + @Test - public void testDensityMapCase() { - runSparsityEstimateTest(new EstimatorDensityMap(), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorDensityMap(), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorDensityMap(), m, sparsity2); - runSparsityEstimateTest(new EstimatorDensityMap(), m, sparsity3); + public void testBasicAvg() { + runSparsityEstimateTest(new EstimatorBasicAvg()); } @Test - public void testDensityMap7Case() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorDensityMap(7), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorDensityMap(7), m, sparsity2); - runSparsityEstimateTest(new EstimatorDensityMap(7), m, sparsity3); + public void testDensityMap() { + runSparsityEstimateTest(new EstimatorDensityMap()); } @Test - public void testBitsetMatrixCase() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorBitsetMM(), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorBitsetMM(), m, sparsity2); - runSparsityEstimateTest(new EstimatorBitsetMM(), m, sparsity3); + public void testDensityMapBlocksize7() { + runSparsityEstimateTest(new EstimatorDensityMap(7)); } @Test - public void testBitset2MatrixCase() { - runSparsityEstimateTest(new EstimatorBitsetMM(2), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorBitsetMM(2), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorBitsetMM(2), m, sparsity2); - runSparsityEstimateTest(new EstimatorBitsetMM(2), m, sparsity3); + public void testBitsetMatrix() { + runSparsityEstimateTest(new EstimatorBitsetMM()); } @Test - public void testMatrixHistogramCase() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, sparsity2); - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, sparsity3); + public void testBitsetMatrixType2() { + runSparsityEstimateTest(new EstimatorBitsetMM(2)); } @Test - public void testMatrixHistogramExceptCase() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m/2, sparsity1); - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, sparsity2); - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, sparsity3); + public void testMatrixHistogram() { + runSparsityEstimateTest(new EstimatorMatrixHistogram(false)); } @Test - public void testSamplingDefCase() { - runSparsityEstimateTest(new EstimatorSample(), m, sparsity2); - runSparsityEstimateTest(new EstimatorSample(), m, sparsity3); + public void testMatrixHistogramExtended() { + runSparsityEstimateTest(new EstimatorMatrixHistogram(true)); } @Test - public void testSampling20Case() { - runSparsityEstimateTest(new EstimatorSample(0.2), m, sparsity2); - runSparsityEstimateTest(new EstimatorSample(0.2), m, sparsity3); + public void testSampling() { + Assume.assumeTrue(sparsity < 0.1); + runSparsityEstimateTest(new EstimatorSample()); } @Test - public void testSamplingRaDefCase() { - runSparsityEstimateTest(new EstimatorSampleRa(), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorSampleRa(), m, sparsity2); - runSparsityEstimateTest(new EstimatorSampleRa(), m, sparsity3); + public void testSamplingFrac20() { + Assume.assumeTrue(sparsity < 0.1); + runSparsityEstimateTest(new EstimatorSample(0.2)); } @Test - public void testSamplingRa20Case() { - runSparsityEstimateTest(new EstimatorSampleRa(0.2), m/4, sparsity0); - runSparsityEstimateTest(new EstimatorSampleRa(0.2), m, sparsity2); - runSparsityEstimateTest(new EstimatorSampleRa(0.2), m, sparsity3); + public void testSamplingRa() { + runSparsityEstimateTest(new EstimatorSampleRa()); } @Test - public void testLayeredGraphDefCase() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, sparsity2); - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, sparsity3); + public void testSamplingRaFrac20() { + runSparsityEstimateTest(new EstimatorSampleRa(0.2)); } @Test - public void testLayeredGraph64Case() { - runSparsityEstimateTest(new EstimatorLayeredGraph(64), m, sparsity2); - runSparsityEstimateTest(new EstimatorLayeredGraph(64), m, sparsity3); + public void testLayeredGraph() { + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13)); } @Test - public void testLayeredGraphCase1() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, sparsity1); + public void testLayeredGraph64Rounds() { + runSparsityEstimateTest(new EstimatorLayeredGraph(64, 13)); } @Test - public void testLayeredGraphCase2() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, sparsity2); + public void testRowWise() { + runSparsityEstimateTest(new EstimatorRowWise()); } - - private static void runSparsityEstimateTest(SparsityEstimator estim, int n, double sp) { - MatrixBlock m1 = MatrixBlock.randOperations(n, n, sp, 1, 1, "uniform", 3); + + private void runSparsityEstimateTest(SparsityEstimator estim) { + MatrixBlock m1 = MatrixBlock.randOperations(m, m, sparsity, 1, 1, "uniform", 3); MatrixBlock m3 = m1.aggregateBinaryOperations(m1, m1, new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); - double spExact1 = OptimizerUtils.getSparsity(n, n, + double spExact1 = OptimizerUtils.getSparsity(m, m, EstimationUtils.getSelfProductOutputNnz(m1)); - double spExact2 = sp<0.4 ? OptimizerUtils.getSparsity(n, n, + double spExact2 = sparsity<0.4 ? OptimizerUtils.getSparsity(m, m, EstimationUtils.getSparseProductOutputNnz(m1, m1)) : spExact1; //compare estimated and real sparsity double est = estim.estim(m1, m1); TestUtils.compareScalars(est, m3.getSparsity(), - (estim instanceof EstimatorBitsetMM) ? eps3 : //exact - (estim instanceof EstimatorBasicWorst || estim instanceof EstimatorLayeredGraph) ? eps1 : eps2); - TestUtils.compareScalars(m3.getSparsity(), spExact1, eps3); - TestUtils.compareScalars(m3.getSparsity(), spExact2, eps3); + (estim instanceof EstimatorBitsetMM) ? 0 : //exact + (estim instanceof EstimatorBasicWorst || estim instanceof EstimatorLayeredGraph) ? 0.05 : + (sparsity == 0.1 && estim instanceof EstimatorSampleRa) ? 0.12 : 1e-4); + TestUtils.compareScalars(m3.getSparsity(), spExact1, 0); + TestUtils.compareScalars(m3.getSparsity(), spExact2, 0); } } diff --git a/src/test/java/org/apache/sysds/test/component/estim/SquaredProductChainTest.java b/src/test/java/org/apache/sysds/test/component/estim/SquaredProductChainTest.java index f799b02c96d..a2b04b34df1 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/SquaredProductChainTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/SquaredProductChainTest.java @@ -19,13 +19,13 @@ package org.apache.sysds.test.component.estim; -import org.junit.Test; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorDensityMap; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.MMNode; import org.apache.sysds.hops.estim.SparsityEstimator; import org.apache.sysds.hops.estim.SparsityEstimator.OpCode; @@ -34,123 +34,99 @@ import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import java.util.Arrays; +import java.util.Collection; + /** * This is a basic sanity check for all estimator, which need * to compute a reasonable estimate for uniform data. */ -public class SquaredProductChainTest extends AutomatedTestBase +@RunWith(value = Parameterized.class) +public class SquaredProductChainTest extends AutomatedTestBase { - private final static int m = 1000; - private final static int k = 1000; - private final static int n = 1000; - private final static int n2 = 1000; - private final static double[] case1 = new double[]{0.0001, 0.00007, 0.001}; - private final static double[] case2 = new double[]{0.0006, 0.00007, 0.001}; + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int k; + @Parameterized.Parameter(2) + public int n; + @Parameterized.Parameter(3) + public int n2; + @Parameterized.Parameter(4) + public double[] sparsity; - private final static double eps1 = 1.0; - private final static double eps2 = 1e-4; - private final static double eps3 = 0; - - @Override public void setUp() { //do nothing } - - @Test - public void testBasicAvgCase1() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, n2, case1); - } - - @Test - public void testBasicAvgCase2() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, n2, case2); - } - - @Test - public void testBasicWorstCase1() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, n2, case1); - } - - @Test - public void testBasicWorstCase2() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, n2, case2); + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, k, n, n2, sparsity} + {1000, 1000, 1000, 1000, new double[]{0.0001, 0.00007, 0.001}}, + {1000, 1000, 1000, 1000, new double[]{0.0006, 0.00007, 0.001}}, + }); } @Test - public void testDensityMapCase1() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, n2, case1); + public void testBasicAvg() { + runSparsityEstimateTest(new EstimatorBasicAvg()); } @Test - public void testDensityMapCase2() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, n2, case2); + public void testBasicWorst() { + runSparsityEstimateTest(new EstimatorBasicWorst()); } @Test - public void testDensityMap7Case1() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m, k, n, n2, case1); + public void testDensityMap() { + runSparsityEstimateTest(new EstimatorDensityMap()); } @Test - public void testDensityMap7Case2() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m, k, n, n2, case2); + public void testDensityMapBlocksize7() { + runSparsityEstimateTest(new EstimatorDensityMap(7)); } @Test - public void testBitsetMatrixCase1() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, n2, case1); + public void testBitsetMatrix() { + runSparsityEstimateTest(new EstimatorBitsetMM()); } @Test - public void testBitsetMatrixCase2() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, n2, case2); + public void testMatrixHistogram() { + runSparsityEstimateTest(new EstimatorMatrixHistogram(false)); } @Test - public void testMatrixHistogramCase1() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, k, n, n2, case1); + public void testMatrixHistogramExcept() { + runSparsityEstimateTest(new EstimatorMatrixHistogram(true)); } @Test - public void testMatrixHistogramCase2() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, k, n, n2, case2); + public void testLayeredGraph() { + runSparsityEstimateTest(new EstimatorLayeredGraph(EstimatorLayeredGraph.ROUNDS, 13)); } @Test - public void testMatrixHistogramExceptCase1() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, k, n, n2, case1); + public void testLayeredGraph32Rounds() { + runSparsityEstimateTest(new EstimatorLayeredGraph(32, 13)); } @Test - public void testMatrixHistogramExceptCase2() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, k, n, n2, case2); - } - - @Test - public void testLayeredGraphCase1() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, n2, case1); + public void testRowWise() { + runSparsityEstimateTest(new EstimatorRowWise()); } - @Test - public void testLayeredGraphCase2() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, n2, case2); - } - - @Test - public void testLayeredGraph32Case1() { - runSparsityEstimateTest(new EstimatorLayeredGraph(32), m, k, n, n2, case1); - } - - @Test - public void testLayeredGraph32Case2() { - runSparsityEstimateTest(new EstimatorLayeredGraph(32), m, k, n, n2, case2); - } - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int k, int n, int n2, double[] sp) { - MatrixBlock m1 = MatrixBlock.randOperations(m, k, sp[0], 1, 1, "uniform", 1); - MatrixBlock m2 = MatrixBlock.randOperations(k, n, sp[1], 1, 1, "uniform", 2); - MatrixBlock m3 = MatrixBlock.randOperations(n, n2, sp[2], 1, 1, "uniform", 3); + private void runSparsityEstimateTest(SparsityEstimator estim) { + MatrixBlock m1 = MatrixBlock.randOperations(m, k, sparsity[0], 1, 1, "uniform", 1); + MatrixBlock m2 = MatrixBlock.randOperations(k, n, sparsity[1], 1, 1, "uniform", 2); + MatrixBlock m3 = MatrixBlock.randOperations(n, n2, sparsity[2], 1, 1, "uniform", 3); MatrixBlock m4 = m1.aggregateBinaryOperations(m1, m2, new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); MatrixBlock m5 = m4.aggregateBinaryOperations(m4, m3, @@ -160,7 +136,7 @@ private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int double est = estim.estim(new MMNode(new MMNode(new MMNode(m1), new MMNode(m2), OpCode.MM), new MMNode(m3), OpCode.MM)).getSparsity(); TestUtils.compareScalars(est, m5.getSparsity(), - (estim instanceof EstimatorBitsetMM) ? eps3 : //exact - (estim instanceof EstimatorBasicWorst) ? eps1 : eps2); + (estim instanceof EstimatorBitsetMM) ? 0 : //exact + (estim instanceof EstimatorBasicWorst) ? 1.0 : 1e-4); } } diff --git a/src/test/java/org/apache/sysds/test/component/estim/SquaredProductTest.java b/src/test/java/org/apache/sysds/test/component/estim/SquaredProductTest.java index 2a898f9c39f..d117b98c1c4 100644 --- a/src/test/java/org/apache/sysds/test/component/estim/SquaredProductTest.java +++ b/src/test/java/org/apache/sysds/test/component/estim/SquaredProductTest.java @@ -19,12 +19,12 @@ package org.apache.sysds.test.component.estim; -import org.junit.Test; import org.apache.sysds.hops.estim.EstimatorBasicAvg; import org.apache.sysds.hops.estim.EstimatorBasicWorst; import org.apache.sysds.hops.estim.EstimatorBitsetMM; import org.apache.sysds.hops.estim.EstimatorDensityMap; import org.apache.sysds.hops.estim.EstimatorMatrixHistogram; +import org.apache.sysds.hops.estim.EstimatorRowWise; import org.apache.sysds.hops.estim.EstimatorLayeredGraph; import org.apache.sysds.hops.estim.EstimatorSample; import org.apache.sysds.hops.estim.SparsityEstimator; @@ -33,138 +33,108 @@ import org.apache.sysds.test.AutomatedTestBase; import org.apache.sysds.test.TestUtils; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import java.util.Arrays; +import java.util.Collection; + /** * This is a basic sanity check for all estimator, which need * to compute a reasonable estimate for uniform data. */ -public class SquaredProductTest extends AutomatedTestBase +@RunWith(value = Parameterized.class) +public class SquaredProductTest extends AutomatedTestBase { - private final static int m = 1000; - private final static int k = 1000; - private final static int n = 1000; - private final static double[] case1 = new double[]{0.0001, 0.00007}; - private final static double[] case2 = new double[]{0.0006, 0.00007}; - - private final static double eps1 = 0.05; - private final static double eps2 = 1e-4; - private final static double eps3 = 0; - + @Parameterized.Parameter(0) + public int m; + @Parameterized.Parameter(1) + public int k; + @Parameterized.Parameter(2) + public int n; + @Parameterized.Parameter(3) + public double[] sparsity; @Override public void setUp() { //do nothing } - - @Test - public void testBasicAvgCase1() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, case1); - } - - @Test - public void testBasicAvgCase2() { - runSparsityEstimateTest(new EstimatorBasicAvg(), m, k, n, case2); - } - - @Test - public void testBasicWorstCase1() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, case1); - } - - @Test - public void testBasicWorstCase2() { - runSparsityEstimateTest(new EstimatorBasicWorst(), m, k, n, case2); - } - - @Test - public void testDensityMapCase1() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, case1); - } - - @Test - public void testDensityMapCase2() { - runSparsityEstimateTest(new EstimatorDensityMap(), m, k, n, case2); - } - - @Test - public void testDensityMap7Case1() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m, k, n, case1); + + @Parameterized.Parameters + public static Collection data() { + return Arrays.asList(new Object[][] { + // {m, k, n, sparsity} + {1000, 1000, 1000, new double[]{0.0001, 0.00007}}, + {1000, 1000, 1000, new double[]{0.0006, 0.00007}}, + }); } @Test - public void testDensityMap7Case2() { - runSparsityEstimateTest(new EstimatorDensityMap(7), m, k, n, case2); + public void testBasicAvg() { + runSparsityEstimateTest(new EstimatorBasicAvg()); } @Test - public void testBitsetMatrixCase1() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, case1); + public void testBasicWorst() { + runSparsityEstimateTest(new EstimatorBasicWorst()); } @Test - public void testBitsetMatrixCase2() { - runSparsityEstimateTest(new EstimatorBitsetMM(), m, k, n, case2); + public void testDensityMap() { + runSparsityEstimateTest(new EstimatorDensityMap()); } @Test - public void testMatrixHistogramCase1() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, k, n, case1); + public void testDensityMapBlocksize7() { + runSparsityEstimateTest(new EstimatorDensityMap(7)); } @Test - public void testMatrixHistogramCase2() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(false), m, k, n, case2); + public void testBitsetMatrix() { + runSparsityEstimateTest(new EstimatorBitsetMM()); } @Test - public void testMatrixHistogramExceptCase1() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, k, n, case1); + public void testMatrixHistogram() { + runSparsityEstimateTest(new EstimatorMatrixHistogram(false)); } @Test - public void testMatrixHistogramExceptCase2() { - runSparsityEstimateTest(new EstimatorMatrixHistogram(true), m, k, n, case2); + public void testMatrixHistogramExcept() { + runSparsityEstimateTest(new EstimatorMatrixHistogram(true)); } @Test - public void testSamplingDefCase1() { - runSparsityEstimateTest(new EstimatorSample(), m, k, n, case1); + public void testSampling() { + runSparsityEstimateTest(new EstimatorSample()); } @Test - public void testSamplingDefCase2() { - runSparsityEstimateTest(new EstimatorSample(), m, k, n, case2); - } - - @Test - public void testSampling20Case1() { - runSparsityEstimateTest(new EstimatorSample(0.2), m, k, n, case1); - } - - @Test - public void testSampling20Case2() { - runSparsityEstimateTest(new EstimatorSample(0.2), m, k, n, case2); + public void testSamplingFrac20() { + runSparsityEstimateTest(new EstimatorSample(0.2)); } @Test - public void testLayeredGraphCase1() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, case1); + public void testLayeredGraph() { + runSparsityEstimateTest(new EstimatorLayeredGraph()); } @Test - public void testLayeredGraphCase2() { - runSparsityEstimateTest(new EstimatorLayeredGraph(), m, k, n, case2); + public void testRowWise() { + runSparsityEstimateTest(new EstimatorRowWise()); } - - private static void runSparsityEstimateTest(SparsityEstimator estim, int m, int k, int n, double[] sp) { - MatrixBlock m1 = MatrixBlock.randOperations(m, k, sp[0], 1, 1, "uniform", 3); - MatrixBlock m2 = MatrixBlock.randOperations(k, n, sp[1], 1, 1, "uniform", 7); + + private void runSparsityEstimateTest(SparsityEstimator estim) { + MatrixBlock m1 = MatrixBlock.randOperations(m, k, sparsity[0], 1, 1, "uniform", 3); + MatrixBlock m2 = MatrixBlock.randOperations(k, n, sparsity[1], 1, 1, "uniform", 7); MatrixBlock m3 = m1.aggregateBinaryOperations(m1, m2, new MatrixBlock(), InstructionUtils.getMatMultOperator(1)); //compare estimated and real sparsity double est = estim.estim(m1, m2); TestUtils.compareScalars(est, m3.getSparsity(), - (estim instanceof EstimatorBitsetMM) ? eps3 : //exact - (estim instanceof EstimatorBasicWorst) ? eps1 : eps2); + (estim instanceof EstimatorBitsetMM) ? 0 : //exact + (estim instanceof EstimatorBasicWorst) ? 0.05 : 1e-4); } }