Examples with CompressedMatrixBlock org.apache.sysml.runtime.compress.CompressedMatrixBlock used on opensource projects

Example 61 with CompressedMatrixBlock

use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project systemml by apache.

the class SpoofOuterProduct method execute.

@Override
public ScalarObject execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects) {
    // sanity check
    if (inputs == null || inputs.size() < 3)
        throw new RuntimeException("Invalid input arguments.");
    if (inputs.get(0).isEmptyBlock(false))
        return new DoubleObject(0);
    // input preparation
    DenseBlock[] ab = getDenseMatrices(prepInputMatrices(inputs, 1, 2, true, false));
    SideInput[] b = prepInputMatrices(inputs, 3, false);
    double[] scalars = prepInputScalars(scalarObjects);
    // core sequential execute
    final int m = inputs.get(0).getNumRows();
    final int n = inputs.get(0).getNumColumns();
    // rank
    final int k = inputs.get(1).getNumColumns();
    MatrixBlock a = inputs.get(0);
    MatrixBlock out = new MatrixBlock(1, 1, false);
    out.allocateDenseBlock();
    if (a instanceof CompressedMatrixBlock)
        executeCellwiseCompressed((CompressedMatrixBlock) a, ab[0], ab[1], b, scalars, out, m, n, k, _outerProductType, 0, m, 0, n);
    else if (!a.isInSparseFormat())
        executeCellwiseDense(a.getDenseBlock(), ab[0], ab[1], b, scalars, out.getDenseBlock(), m, n, k, _outerProductType, 0, m, 0, n);
    else
        executeCellwiseSparse(a.getSparseBlock(), ab[0], ab[1], b, scalars, out, m, n, k, a.getNonZeros(), _outerProductType, 0, m, 0, n);
    return new DoubleObject(out.getDenseBlock().get(0, 0));
}

Example 62 with CompressedMatrixBlock

use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project systemml by apache.

the class SpoofOuterProduct method execute.

@Override
public MatrixBlock execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects, MatrixBlock out) {
    // sanity check
    if (inputs == null || inputs.size() < 3 || out == null)
        throw new RuntimeException("Invalid input arguments.");
    // check empty result
    if (// U is empty
    (_outerProductType == OutProdType.LEFT_OUTER_PRODUCT && inputs.get(1).isEmptyBlock(false)) || // V is empty
    (_outerProductType == OutProdType.RIGHT_OUTER_PRODUCT && inputs.get(2).isEmptyBlock(false)) || inputs.get(0).isEmptyBlock(false)) {
        // X is empty
        // turn empty dense into sparse
        out.examSparsity();
        return out;
    }
    // input preparation and result allocation (Allocate the output that is set by Sigma2CPInstruction)
    if (_outerProductType == OutProdType.CELLWISE_OUTER_PRODUCT) {
        // assign it to the time and sparse representation of the major input matrix
        out.reset(inputs.get(0).getNumRows(), inputs.get(0).getNumColumns(), inputs.get(0).isInSparseFormat());
    } else {
        // if left outerproduct gives a value of k*n instead of n*k, change it back to n*k and then transpose the output
        if (_outerProductType == OutProdType.LEFT_OUTER_PRODUCT)
            // n*k
            out.reset(inputs.get(0).getNumColumns(), inputs.get(1).getNumColumns(), false);
        else if (_outerProductType == OutProdType.RIGHT_OUTER_PRODUCT)
            // m*k
            out.reset(inputs.get(0).getNumRows(), inputs.get(1).getNumColumns(), false);
    }
    // check for empty inputs; otherwise allocate result
    if (inputs.get(0).isEmptyBlock(false))
        return out;
    out.allocateBlock();
    // input preparation
    DenseBlock[] ab = getDenseMatrices(prepInputMatrices(inputs, 1, 2, true, false));
    SideInput[] b = prepInputMatrices(inputs, 3, false);
    double[] scalars = prepInputScalars(scalarObjects);
    // core sequential execute
    final int m = inputs.get(0).getNumRows();
    final int n = inputs.get(0).getNumColumns();
    // rank
    final int k = inputs.get(1).getNumColumns();
    MatrixBlock a = inputs.get(0);
    switch(_outerProductType) {
        case LEFT_OUTER_PRODUCT:
        case RIGHT_OUTER_PRODUCT:
            if (a instanceof CompressedMatrixBlock)
                executeCompressed((CompressedMatrixBlock) a, ab[0], ab[1], b, scalars, out.getDenseBlock(), m, n, k, _outerProductType, 0, m, 0, ((CompressedMatrixBlock) a).getNumColGroups());
            else if (!a.isInSparseFormat())
                executeDense(a.getDenseBlock(), ab[0], ab[1], b, scalars, out.getDenseBlock(), m, n, k, _outerProductType, 0, m, 0, n);
            else
                executeSparse(a.getSparseBlock(), ab[0], ab[1], b, scalars, out.getDenseBlock(), m, n, k, a.getNonZeros(), _outerProductType, 0, m, 0, n);
            break;
        case CELLWISE_OUTER_PRODUCT:
            if (a instanceof CompressedMatrixBlock)
                executeCellwiseCompressed((CompressedMatrixBlock) a, ab[0], ab[1], b, scalars, out, m, n, k, _outerProductType, 0, m, 0, n);
            else if (!a.isInSparseFormat())
                executeCellwiseDense(a.getDenseBlock(), ab[0], ab[1], b, scalars, out.getDenseBlock(), m, n, k, _outerProductType, 0, m, 0, n);
            else
                executeCellwiseSparse(a.getSparseBlock(), ab[0], ab[1], b, scalars, out, m, n, k, a.getNonZeros(), _outerProductType, 0, m, 0, n);
            break;
        case AGG_OUTER_PRODUCT:
            throw new DMLRuntimeException("Wrong codepath for aggregate outer product.");
    }
    // post-processing
    if (a instanceof CompressedMatrixBlock && out.isInSparseFormat() && _outerProductType == OutProdType.CELLWISE_OUTER_PRODUCT)
        out.sortSparseRows();
    out.recomputeNonZeros();
    out.examSparsity();
    return out;
}

Example 63 with CompressedMatrixBlock

use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project systemml by apache.

the class ParMatrixMultChainTest method runMatrixMultChainTest.

private static void runMatrixMultChainTest(SparsityType sptype, ValueType vtype, ChainType ctype, boolean compress) {
    try {
        // prepare sparsity for input data
        double sparsity = -1;
        switch(sptype) {
            case DENSE:
                sparsity = sparsity1;
                break;
            case SPARSE:
                sparsity = sparsity2;
                break;
            case EMPTY:
                sparsity = sparsity3;
                break;
        }
        // generate input data
        double min = (vtype == ValueType.CONST) ? 10 : -10;
        double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
        if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
            CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
            input = TestUtils.round(input);
        }
        MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
        MatrixBlock vector1 = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(cols, 1, 0, 1, 1.0, 3));
        MatrixBlock vector2 = (ctype == ChainType.XtwXv) ? DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(rows, 1, 0, 1, 1.0, 3)) : null;
        // compress given matrix block
        CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
        if (compress)
            cmb.compress();
        // matrix-vector uncompressed
        int k = InfrastructureAnalyzer.getLocalParallelism();
        MatrixBlock ret1 = (MatrixBlock) mb.chainMatrixMultOperations(vector1, vector2, new MatrixBlock(), ctype, k);
        // matrix-vector compressed
        MatrixBlock ret2 = (MatrixBlock) cmb.chainMatrixMultOperations(vector1, vector2, new MatrixBlock(), ctype, k);
        // compare result with input
        double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
        double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
        TestUtils.compareMatrices(d1, d2, cols, 1, 0.0000001);
    } catch (Exception ex) {
        throw new RuntimeException(ex);
    } finally {
        CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
    }
}

Example 64 with CompressedMatrixBlock

use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project systemml by apache.

the class ParMatrixVectorMultTest method runMatrixVectorMultTest.

private static void runMatrixVectorMultTest(SparsityType sptype, ValueType vtype, boolean compress) {
    try {
        // prepare sparsity for input data
        double sparsity = -1;
        switch(sptype) {
            case DENSE:
                sparsity = sparsity1;
                break;
            case SPARSE:
                sparsity = sparsity2;
                break;
            case EMPTY:
                sparsity = sparsity3;
                break;
        }
        // generate input data
        double min = (vtype == ValueType.CONST) ? 10 : -10;
        double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
        if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
            CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
            input = TestUtils.round(input);
        }
        MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
        MatrixBlock vector = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(cols, 1, 1, 1, 1.0, 3));
        // compress given matrix block
        CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
        if (compress)
            cmb.compress();
        // matrix-vector uncompressed
        AggregateOperator aop = new AggregateOperator(0, Plus.getPlusFnObject());
        AggregateBinaryOperator abop = new AggregateBinaryOperator(Multiply.getMultiplyFnObject(), aop, InfrastructureAnalyzer.getLocalParallelism());
        MatrixBlock ret1 = mb.aggregateBinaryOperations(mb, vector, new MatrixBlock(), abop);
        // matrix-vector compressed
        MatrixBlock ret2 = cmb.aggregateBinaryOperations(cmb, vector, new MatrixBlock(), abop);
        // compare result with input
        double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
        double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
        TestUtils.compareMatrices(d1, d2, rows, 1, 0.0000001);
    } catch (Exception ex) {
        throw new RuntimeException(ex);
    } finally {
        CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
    }
}

Example 65 with CompressedMatrixBlock

use of org.apache.sysml.runtime.compress.CompressedMatrixBlock in project systemml by apache.

the class ParVectorMatrixMultTest method runMatrixVectorMultTest.

private static void runMatrixVectorMultTest(SparsityType sptype, ValueType vtype, boolean compress) {
    try {
        // prepare sparsity for input data
        double sparsity = -1;
        switch(sptype) {
            case DENSE:
                sparsity = sparsity1;
                break;
            case SPARSE:
                sparsity = sparsity2;
                break;
            case EMPTY:
                sparsity = sparsity3;
                break;
        }
        // generate input data
        double min = (vtype == ValueType.CONST) ? 10 : -10;
        double[][] input = TestUtils.generateTestMatrix(rows, cols, min, 10, sparsity, 7);
        if (vtype == ValueType.RAND_ROUND_OLE || vtype == ValueType.RAND_ROUND_DDC) {
            CompressedMatrixBlock.ALLOW_DDC_ENCODING = (vtype == ValueType.RAND_ROUND_DDC);
            input = TestUtils.round(input);
        }
        MatrixBlock mb = DataConverter.convertToMatrixBlock(input);
        MatrixBlock vector = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(1, rows, 1, 1, 1.0, 3));
        // compress given matrix block
        CompressedMatrixBlock cmb = new CompressedMatrixBlock(mb);
        if (compress)
            cmb.compress();
        // matrix-vector uncompressed
        AggregateOperator aop = new AggregateOperator(0, Plus.getPlusFnObject());
        AggregateBinaryOperator abop = new AggregateBinaryOperator(Multiply.getMultiplyFnObject(), aop, InfrastructureAnalyzer.getLocalParallelism());
        MatrixBlock ret1 = vector.aggregateBinaryOperations(vector, mb, new MatrixBlock(), abop);
        // matrix-vector compressed
        MatrixBlock ret2 = cmb.aggregateBinaryOperations(vector, cmb, new MatrixBlock(), abop);
        // compare result with input
        double[][] d1 = DataConverter.convertToDoubleMatrix(ret1);
        double[][] d2 = DataConverter.convertToDoubleMatrix(ret2);
        TestUtils.compareMatrices(d1, d2, 1, cols, 0.0000001);
    } catch (Exception ex) {
        throw new RuntimeException(ex);
    } finally {
        CompressedMatrixBlock.ALLOW_DDC_ENCODING = true;
    }
}