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// 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 "ceres/accelerate_sparse.h"
#include <algorithm>
#include <string>
#include <vector>
#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>;
}
}
#endif // CERES_NO_ACCELERATE_SPARSE