| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #include "ceres/sparse_cholesky.h" |
| |
| #include <memory> |
| #include <numeric> |
| #include <random> |
| #include <vector> |
| |
| #include "Eigen/Dense" |
| #include "Eigen/SparseCore" |
| #include "absl/log/check.h" |
| #include "ceres/block_sparse_matrix.h" |
| #include "ceres/compressed_row_sparse_matrix.h" |
| #include "ceres/cuda_sparse_cholesky.h" |
| #include "ceres/inner_product_computer.h" |
| #include "ceres/internal/config.h" |
| #include "ceres/internal/eigen.h" |
| #include "ceres/iterative_refiner.h" |
| #include "gmock/gmock.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| namespace { |
| |
| std::unique_ptr<BlockSparseMatrix> CreateRandomFullRankMatrix( |
| const int num_col_blocks, |
| const int min_col_block_size, |
| const int max_col_block_size, |
| const double block_density, |
| std::mt19937& prng) { |
| // Create a random matrix |
| BlockSparseMatrix::RandomMatrixOptions options; |
| options.num_col_blocks = num_col_blocks; |
| options.min_col_block_size = min_col_block_size; |
| options.max_col_block_size = max_col_block_size; |
| |
| options.num_row_blocks = 2 * num_col_blocks; |
| options.min_row_block_size = 1; |
| options.max_row_block_size = max_col_block_size; |
| options.block_density = block_density; |
| auto random_matrix = BlockSparseMatrix::CreateRandomMatrix(options, prng); |
| |
| // Add a diagonal block sparse matrix to make it full rank. |
| Vector diagonal = Vector::Ones(random_matrix->num_cols()); |
| auto block_diagonal = BlockSparseMatrix::CreateDiagonalMatrix( |
| diagonal.data(), random_matrix->block_structure()->cols); |
| random_matrix->AppendRows(*block_diagonal); |
| return random_matrix; |
| } |
| |
| bool ComputeExpectedSolution(const CompressedRowSparseMatrix& lhs, |
| const Vector& rhs, |
| Vector* solution) { |
| Matrix eigen_lhs; |
| lhs.ToDenseMatrix(&eigen_lhs); |
| if (lhs.storage_type() == |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) { |
| Matrix full_lhs = eigen_lhs.selfadjointView<Eigen::Upper>(); |
| Eigen::LLT<Matrix, Eigen::Upper> llt = |
| eigen_lhs.selfadjointView<Eigen::Upper>().llt(); |
| if (llt.info() != Eigen::Success) { |
| return false; |
| } |
| *solution = llt.solve(rhs); |
| return (llt.info() == Eigen::Success); |
| } |
| |
| Matrix full_lhs = eigen_lhs.selfadjointView<Eigen::Lower>(); |
| Eigen::LLT<Matrix, Eigen::Lower> llt = |
| eigen_lhs.selfadjointView<Eigen::Lower>().llt(); |
| if (llt.info() != Eigen::Success) { |
| return false; |
| } |
| *solution = llt.solve(rhs); |
| return (llt.info() == Eigen::Success); |
| } |
| |
| void SparseCholeskySolverUnitTest( |
| const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type, |
| const OrderingType ordering_type, |
| const bool use_block_structure, |
| const int num_blocks, |
| const int min_block_size, |
| const int max_block_size, |
| const double block_density, |
| std::mt19937& prng) { |
| LinearSolver::Options sparse_cholesky_options; |
| sparse_cholesky_options.sparse_linear_algebra_library_type = |
| sparse_linear_algebra_library_type; |
| sparse_cholesky_options.ordering_type = ordering_type; |
| #ifndef CERES_NO_CUDSS |
| ContextImpl context; |
| sparse_cholesky_options.context = &context; |
| std::string error; |
| CHECK(context.InitCuda(&error)) << error; |
| #endif // CERES_NO_CUDSS |
| auto sparse_cholesky = SparseCholesky::Create(sparse_cholesky_options); |
| const CompressedRowSparseMatrix::StorageType storage_type = |
| sparse_cholesky->StorageType(); |
| |
| auto m = CreateRandomFullRankMatrix( |
| num_blocks, min_block_size, max_block_size, block_density, prng); |
| auto inner_product_computer = InnerProductComputer::Create(*m, storage_type); |
| inner_product_computer->Compute(); |
| CompressedRowSparseMatrix* lhs = inner_product_computer->mutable_result(); |
| |
| if (!use_block_structure) { |
| lhs->mutable_row_blocks()->clear(); |
| lhs->mutable_col_blocks()->clear(); |
| } |
| |
| Vector rhs = Vector::Random(lhs->num_rows()); |
| Vector expected(lhs->num_rows()); |
| Vector actual(lhs->num_rows()); |
| |
| EXPECT_TRUE(ComputeExpectedSolution(*lhs, rhs, &expected)); |
| std::string message; |
| EXPECT_EQ( |
| sparse_cholesky->FactorAndSolve(lhs, rhs.data(), actual.data(), &message), |
| LinearSolverTerminationType::SUCCESS); |
| Matrix eigen_lhs; |
| lhs->ToDenseMatrix(&eigen_lhs); |
| EXPECT_NEAR((actual - expected).norm() / actual.norm(), |
| 0.0, |
| std::numeric_limits<double>::epsilon() * 20) |
| << "\n" |
| << eigen_lhs; |
| } |
| |
| using Param = |
| ::testing::tuple<SparseLinearAlgebraLibraryType, OrderingType, bool>; |
| |
| std::string ParamInfoToString(testing::TestParamInfo<Param> info) { |
| Param param = info.param; |
| std::stringstream ss; |
| ss << SparseLinearAlgebraLibraryTypeToString(::testing::get<0>(param)) << "_" |
| << ::testing::get<1>(param) << "_" |
| << (::testing::get<2>(param) ? "UseBlockStructure" : "NoBlockStructure"); |
| return ss.str(); |
| } |
| |
| } // namespace |
| |
| class SparseCholeskyTest : public ::testing::TestWithParam<Param> {}; |
| |
| TEST_P(SparseCholeskyTest, FactorAndSolve) { |
| constexpr int kMinNumBlocks = 1; |
| constexpr int kMaxNumBlocks = 10; |
| constexpr int kNumTrials = 10; |
| constexpr int kMinBlockSize = 1; |
| constexpr int kMaxBlockSize = 5; |
| |
| Param param = GetParam(); |
| |
| std::mt19937 prng; |
| std::uniform_real_distribution<double> distribution(0.1, 1.0); |
| |
| for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks; |
| ++num_blocks) { |
| for (int trial = 0; trial < kNumTrials; ++trial) { |
| const double block_density = distribution(prng); |
| SparseCholeskySolverUnitTest(::testing::get<0>(param), |
| ::testing::get<1>(param), |
| ::testing::get<2>(param), |
| num_blocks, |
| kMinBlockSize, |
| kMaxBlockSize, |
| block_density, |
| prng); |
| } |
| } |
| } |
| |
| namespace { |
| |
| #ifndef CERES_NO_SUITESPARSE |
| INSTANTIATE_TEST_SUITE_P( |
| SuiteSparseCholesky, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(SUITE_SPARSE), |
| ::testing::Values(OrderingType::AMD, |
| OrderingType::NATURAL), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| #endif |
| |
| #if !defined(CERES_NO_SUITESPARSE) && !defined(CERES_NO_CHOLMOD_PARTITION) |
| INSTANTIATE_TEST_SUITE_P( |
| SuiteSparseCholeskyMETIS, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(SUITE_SPARSE), |
| ::testing::Values(OrderingType::NESDIS), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| #endif // !defined(CERES_NO_SUITESPARSE) && |
| // !defined(CERES_NO_CHOLMOD_PARTITION) |
| |
| #ifndef CERES_NO_ACCELERATE_SPARSE |
| INSTANTIATE_TEST_SUITE_P( |
| AccelerateSparseCholesky, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(ACCELERATE_SPARSE), |
| ::testing::Values(OrderingType::AMD, |
| OrderingType::NESDIS, |
| OrderingType::NATURAL), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| |
| INSTANTIATE_TEST_SUITE_P( |
| AccelerateSparseCholeskySingle, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(ACCELERATE_SPARSE), |
| ::testing::Values(OrderingType::AMD, |
| OrderingType::NESDIS, |
| OrderingType::NATURAL), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| #endif |
| |
| #ifdef CERES_USE_EIGEN_SPARSE |
| INSTANTIATE_TEST_SUITE_P( |
| EigenSparseCholesky, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(EIGEN_SPARSE), |
| ::testing::Values(OrderingType::AMD, |
| OrderingType::NATURAL), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| |
| INSTANTIATE_TEST_SUITE_P( |
| FloatEigenSparseCholesky, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(EIGEN_SPARSE), |
| ::testing::Values(OrderingType::AMD, |
| OrderingType::NATURAL), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| #endif // CERES_USE_EIGEN_SPARSE |
| |
| #ifndef CERES_NO_CUDSS |
| using CuDSSCholeskyDouble = CudaSparseCholesky<double>; |
| INSTANTIATE_TEST_SUITE_P( |
| CuDSSCholeskyDouble, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(CUDA_SPARSE), |
| ::testing::Values(OrderingType::AMD), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| |
| using CuDSSCholeskySingle = CudaSparseCholesky<float>; |
| INSTANTIATE_TEST_SUITE_P( |
| CuDSSCholeskySingle, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(CUDA_SPARSE), |
| ::testing::Values(OrderingType::AMD), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| #endif // CERES_NO_CUDSS |
| |
| #if defined(CERES_USE_EIGEN_SPARSE) && !defined(CERES_NO_EIGEN_METIS) |
| INSTANTIATE_TEST_SUITE_P( |
| EigenSparseCholeskyMETIS, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(EIGEN_SPARSE), |
| ::testing::Values(OrderingType::NESDIS), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| |
| INSTANTIATE_TEST_SUITE_P( |
| EigenSparseCholeskySingleMETIS, |
| SparseCholeskyTest, |
| ::testing::Combine(::testing::Values(EIGEN_SPARSE), |
| ::testing::Values(OrderingType::NESDIS), |
| ::testing::Values(true, false)), |
| ParamInfoToString); |
| #endif // defined(CERES_USE_EIGEN_SPARSE) && !defined(CERES_NO_EIGEN_METIS) |
| |
| class MockSparseCholesky : public SparseCholesky { |
| public: |
| MOCK_CONST_METHOD0(StorageType, CompressedRowSparseMatrix::StorageType()); |
| MOCK_METHOD2(Factorize, |
| LinearSolverTerminationType(CompressedRowSparseMatrix* lhs, |
| std::string* message)); |
| MOCK_METHOD3(Solve, |
| LinearSolverTerminationType(const double* rhs, |
| double* solution, |
| std::string* message)); |
| }; |
| |
| class MockSparseIterativeRefiner : public SparseIterativeRefiner { |
| public: |
| MockSparseIterativeRefiner() : SparseIterativeRefiner(1) {} |
| MOCK_METHOD4(Refine, |
| void(const SparseMatrix& lhs, |
| const double* rhs, |
| SparseCholesky* sparse_cholesky, |
| double* solution)); |
| }; |
| |
| using testing::_; |
| using testing::Return; |
| |
| TEST(RefinedSparseCholesky, StorageType) { |
| auto sparse_cholesky = std::make_unique<MockSparseCholesky>(); |
| auto iterative_refiner = std::make_unique<MockSparseIterativeRefiner>(); |
| EXPECT_CALL(*sparse_cholesky, StorageType()) |
| .Times(1) |
| .WillRepeatedly( |
| Return(CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR)); |
| EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _)).Times(0); |
| RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky), |
| std::move(iterative_refiner)); |
| EXPECT_EQ(refined_sparse_cholesky.StorageType(), |
| CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR); |
| }; |
| |
| TEST(RefinedSparseCholesky, Factorize) { |
| auto* mock_sparse_cholesky = new MockSparseCholesky; |
| auto* mock_iterative_refiner = new MockSparseIterativeRefiner; |
| EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS)); |
| EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0); |
| std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky); |
| std::unique_ptr<SparseIterativeRefiner> iterative_refiner( |
| mock_iterative_refiner); |
| RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky), |
| std::move(iterative_refiner)); |
| CompressedRowSparseMatrix m(1, 1, 1); |
| std::string message; |
| EXPECT_EQ(refined_sparse_cholesky.Factorize(&m, &message), |
| LinearSolverTerminationType::SUCCESS); |
| }; |
| |
| TEST(RefinedSparseCholesky, FactorAndSolveWithUnsuccessfulFactorization) { |
| auto* mock_sparse_cholesky = new MockSparseCholesky; |
| auto* mock_iterative_refiner = new MockSparseIterativeRefiner; |
| EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::FAILURE)); |
| EXPECT_CALL(*mock_sparse_cholesky, Solve(_, _, _)).Times(0); |
| EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0); |
| std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky); |
| std::unique_ptr<SparseIterativeRefiner> iterative_refiner( |
| mock_iterative_refiner); |
| RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky), |
| std::move(iterative_refiner)); |
| CompressedRowSparseMatrix m(1, 1, 1); |
| std::string message; |
| double rhs; |
| double solution; |
| EXPECT_EQ( |
| refined_sparse_cholesky.FactorAndSolve(&m, &rhs, &solution, &message), |
| LinearSolverTerminationType::FAILURE); |
| }; |
| |
| TEST(RefinedSparseCholesky, FactorAndSolveWithSuccess) { |
| auto* mock_sparse_cholesky = new MockSparseCholesky; |
| std::unique_ptr<MockSparseIterativeRefiner> mock_iterative_refiner( |
| new MockSparseIterativeRefiner); |
| EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS)); |
| EXPECT_CALL(*mock_sparse_cholesky, Solve(_, _, _)) |
| .Times(1) |
| .WillRepeatedly(Return(LinearSolverTerminationType::SUCCESS)); |
| EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(1); |
| |
| std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky); |
| std::unique_ptr<SparseIterativeRefiner> iterative_refiner( |
| std::move(mock_iterative_refiner)); |
| RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky), |
| std::move(iterative_refiner)); |
| CompressedRowSparseMatrix m(1, 1, 1); |
| std::string message; |
| double rhs; |
| double solution; |
| EXPECT_EQ( |
| refined_sparse_cholesky.FactorAndSolve(&m, &rhs, &solution, &message), |
| LinearSolverTerminationType::SUCCESS); |
| }; |
| |
| } // namespace |
| |
| } // namespace ceres::internal |
