| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2018 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: alexs.mac@gmail.com (Alex Stewart) |
| |
| // This include must come before any #ifndef check on Ceres compile options. |
| #include "ceres/internal/port.h" |
| |
| #ifndef CERES_NO_ACCELERATE_SPARSE |
| |
| #include <algorithm> |
| #include <string> |
| #include <vector> |
| |
| #include "ceres/accelerate_sparse.h" |
| #include "ceres/compressed_col_sparse_matrix_utils.h" |
| #include "ceres/compressed_row_sparse_matrix.h" |
| #include "ceres/triplet_sparse_matrix.h" |
| #include "glog/logging.h" |
| |
| #define CASESTR(x) \ |
| case x: \ |
| return #x |
| |
| namespace ceres { |
| namespace internal { |
| |
| namespace { |
| const char* SparseStatusToString(SparseStatus_t status) { |
| switch (status) { |
| CASESTR(SparseStatusOK); |
| CASESTR(SparseFactorizationFailed); |
| CASESTR(SparseMatrixIsSingular); |
| CASESTR(SparseInternalError); |
| CASESTR(SparseParameterError); |
| CASESTR(SparseStatusReleased); |
| default: |
| return "UKNOWN"; |
| } |
| } |
| } // namespace. |
| |
| // Resizes workspace as required to contain at least required_size bytes |
| // aligned to kAccelerateRequiredAlignment and returns a pointer to the |
| // aligned start. |
| void* ResizeForAccelerateAlignment(const size_t required_size, |
| std::vector<uint8_t>* workspace) { |
| // As per the Accelerate documentation, all workspace memory passed to the |
| // sparse solver functions must be 16-byte aligned. |
| constexpr int kAccelerateRequiredAlignment = 16; |
| // Although malloc() on macOS should always be 16-byte aligned, it is unclear |
| // if this holds for new(), or on other Apple OSs (phoneOS, watchOS etc). |
| // As such we assume it is not and use std::align() to create a (potentially |
| // offset) 16-byte aligned sub-buffer of the specified size within workspace. |
| workspace->resize(required_size + kAccelerateRequiredAlignment); |
| size_t size_from_aligned_start = workspace->size(); |
| void* aligned_solve_workspace_start = |
| reinterpret_cast<void*>(workspace->data()); |
| aligned_solve_workspace_start = std::align(kAccelerateRequiredAlignment, |
| required_size, |
| aligned_solve_workspace_start, |
| size_from_aligned_start); |
| CHECK(aligned_solve_workspace_start != nullptr) |
| << "required_size: " << required_size |
| << ", workspace size: " << workspace->size(); |
| return aligned_solve_workspace_start; |
| } |
| |
| template <typename Scalar> |
| void AccelerateSparse<Scalar>::Solve(NumericFactorization* numeric_factor, |
| DenseVector* rhs_and_solution) { |
| // From SparseSolve() documentation in Solve.h |
| const int required_size = numeric_factor->solveWorkspaceRequiredStatic + |
| numeric_factor->solveWorkspaceRequiredPerRHS; |
| SparseSolve(*numeric_factor, |
| *rhs_and_solution, |
| ResizeForAccelerateAlignment(required_size, &solve_workspace_)); |
| } |
| |
| template <typename Scalar> |
| typename AccelerateSparse<Scalar>::ASSparseMatrix |
| AccelerateSparse<Scalar>::CreateSparseMatrixTransposeView( |
| CompressedRowSparseMatrix* A) { |
| // Accelerate uses CSC as its sparse storage format whereas Ceres uses CSR. |
| // As this method returns the transpose view we can flip rows/cols to map |
| // from CSR to CSC^T. |
| // |
| // Accelerate's columnStarts is a long*, not an int*. These types might be |
| // different (e.g. ARM on iOS) so always make a copy. |
| column_starts_.resize(A->num_rows() + 1); // +1 for final column length. |
| std::copy_n(A->rows(), column_starts_.size(), &column_starts_[0]); |
| |
| ASSparseMatrix At; |
| At.structure.rowCount = A->num_cols(); |
| At.structure.columnCount = A->num_rows(); |
| At.structure.columnStarts = &column_starts_[0]; |
| At.structure.rowIndices = A->mutable_cols(); |
| At.structure.attributes.transpose = false; |
| At.structure.attributes.triangle = SparseUpperTriangle; |
| At.structure.attributes.kind = SparseSymmetric; |
| At.structure.attributes._reserved = 0; |
| At.structure.attributes._allocatedBySparse = 0; |
| At.structure.blockSize = 1; |
| if (std::is_same<Scalar, double>::value) { |
| At.data = reinterpret_cast<Scalar*>(A->mutable_values()); |
| } else { |
| values_ = |
| ConstVectorRef(A->values(), A->num_nonzeros()).template cast<Scalar>(); |
| At.data = values_.data(); |
| } |
| return At; |
| } |
| |
| template <typename Scalar> |
| typename AccelerateSparse<Scalar>::SymbolicFactorization |
| AccelerateSparse<Scalar>::AnalyzeCholesky(ASSparseMatrix* A) { |
| return SparseFactor(SparseFactorizationCholesky, A->structure); |
| } |
| |
| template <typename Scalar> |
| typename AccelerateSparse<Scalar>::NumericFactorization |
| AccelerateSparse<Scalar>::Cholesky(ASSparseMatrix* A, |
| SymbolicFactorization* symbolic_factor) { |
| return SparseFactor(*symbolic_factor, *A); |
| } |
| |
| template <typename Scalar> |
| void AccelerateSparse<Scalar>::Cholesky(ASSparseMatrix* A, |
| NumericFactorization* numeric_factor) { |
| // From SparseRefactor() documentation in Solve.h |
| const int required_size = |
| std::is_same<Scalar, double>::value |
| ? numeric_factor->symbolicFactorization.workspaceSize_Double |
| : numeric_factor->symbolicFactorization.workspaceSize_Float; |
| return SparseRefactor( |
| *A, |
| numeric_factor, |
| ResizeForAccelerateAlignment(required_size, &factorization_workspace_)); |
| } |
| |
| // Instantiate only for the specific template types required/supported s/t the |
| // definition can be in the .cc file. |
| template class AccelerateSparse<double>; |
| template class AccelerateSparse<float>; |
| |
| template <typename Scalar> |
| std::unique_ptr<SparseCholesky> AppleAccelerateCholesky<Scalar>::Create( |
| OrderingType ordering_type) { |
| return std::unique_ptr<SparseCholesky>( |
| new AppleAccelerateCholesky<Scalar>(ordering_type)); |
| } |
| |
| template <typename Scalar> |
| AppleAccelerateCholesky<Scalar>::AppleAccelerateCholesky( |
| const OrderingType ordering_type) |
| : ordering_type_(ordering_type) {} |
| |
| template <typename Scalar> |
| AppleAccelerateCholesky<Scalar>::~AppleAccelerateCholesky() { |
| FreeSymbolicFactorization(); |
| FreeNumericFactorization(); |
| } |
| |
| template <typename Scalar> |
| CompressedRowSparseMatrix::StorageType |
| AppleAccelerateCholesky<Scalar>::StorageType() const { |
| return CompressedRowSparseMatrix::LOWER_TRIANGULAR; |
| } |
| |
| template <typename Scalar> |
| LinearSolverTerminationType AppleAccelerateCholesky<Scalar>::Factorize( |
| CompressedRowSparseMatrix* lhs, std::string* message) { |
| CHECK_EQ(lhs->storage_type(), StorageType()); |
| if (lhs == NULL) { |
| *message = "Failure: Input lhs is NULL."; |
| return LINEAR_SOLVER_FATAL_ERROR; |
| } |
| typename SparseTypesTrait<Scalar>::SparseMatrix as_lhs = |
| as_.CreateSparseMatrixTransposeView(lhs); |
| |
| if (!symbolic_factor_) { |
| symbolic_factor_.reset( |
| new typename SparseTypesTrait<Scalar>::SymbolicFactorization( |
| as_.AnalyzeCholesky(&as_lhs))); |
| if (symbolic_factor_->status != SparseStatusOK) { |
| *message = StringPrintf( |
| "Apple Accelerate Failure : Symbolic factorisation failed: %s", |
| SparseStatusToString(symbolic_factor_->status)); |
| FreeSymbolicFactorization(); |
| return LINEAR_SOLVER_FATAL_ERROR; |
| } |
| } |
| |
| if (!numeric_factor_) { |
| numeric_factor_.reset( |
| new typename SparseTypesTrait<Scalar>::NumericFactorization( |
| as_.Cholesky(&as_lhs, symbolic_factor_.get()))); |
| } else { |
| // Recycle memory from previous numeric factorization. |
| as_.Cholesky(&as_lhs, numeric_factor_.get()); |
| } |
| if (numeric_factor_->status != SparseStatusOK) { |
| *message = StringPrintf( |
| "Apple Accelerate Failure : Numeric factorisation failed: %s", |
| SparseStatusToString(numeric_factor_->status)); |
| FreeNumericFactorization(); |
| return LINEAR_SOLVER_FAILURE; |
| } |
| |
| return LINEAR_SOLVER_SUCCESS; |
| } |
| |
| template <typename Scalar> |
| LinearSolverTerminationType AppleAccelerateCholesky<Scalar>::Solve( |
| const double* rhs, double* solution, std::string* message) { |
| CHECK_EQ(numeric_factor_->status, SparseStatusOK) |
| << "Solve called without a call to Factorize first (" |
| << SparseStatusToString(numeric_factor_->status) << ")."; |
| const int num_cols = numeric_factor_->symbolicFactorization.columnCount; |
| |
| typename SparseTypesTrait<Scalar>::DenseVector as_rhs_and_solution; |
| as_rhs_and_solution.count = num_cols; |
| if (std::is_same<Scalar, double>::value) { |
| as_rhs_and_solution.data = reinterpret_cast<Scalar*>(solution); |
| std::copy_n(rhs, num_cols, solution); |
| } else { |
| scalar_rhs_and_solution_ = |
| ConstVectorRef(rhs, num_cols).template cast<Scalar>(); |
| as_rhs_and_solution.data = scalar_rhs_and_solution_.data(); |
| } |
| as_.Solve(numeric_factor_.get(), &as_rhs_and_solution); |
| if (!std::is_same<Scalar, double>::value) { |
| VectorRef(solution, num_cols) = |
| scalar_rhs_and_solution_.template cast<double>(); |
| } |
| return LINEAR_SOLVER_SUCCESS; |
| } |
| |
| template <typename Scalar> |
| void AppleAccelerateCholesky<Scalar>::FreeSymbolicFactorization() { |
| if (symbolic_factor_) { |
| SparseCleanup(*symbolic_factor_); |
| symbolic_factor_.reset(); |
| } |
| } |
| |
| template <typename Scalar> |
| void AppleAccelerateCholesky<Scalar>::FreeNumericFactorization() { |
| if (numeric_factor_) { |
| SparseCleanup(*numeric_factor_); |
| numeric_factor_.reset(); |
| } |
| } |
| |
| // Instantiate only for the specific template types required/supported s/t the |
| // definition can be in the .cc file. |
| template class AppleAccelerateCholesky<double>; |
| template class AppleAccelerateCholesky<float>; |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| #endif // CERES_NO_ACCELERATE_SPARSE |