AccelerateSupport.h

// SPDX-FileCopyrightText: The Eigen Authors
// SPDX-License-Identifier: MPL-2.0
 
#ifndef EIGEN_ACCELERATESUPPORT_H
#define EIGEN_ACCELERATESUPPORT_H
 
#include <Accelerate/Accelerate.h>
 
#include <Eigen/Sparse>
 
namespace Eigen {
 
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
class AccelerateImpl;

template <typename MatrixType, int UpLo = Lower>
using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;

template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;

template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;

template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;

template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;

template <typename MatrixType>
using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;

template <typename MatrixType>
using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
 
namespace internal {
template <typename T>
struct AccelFactorizationDeleter {
  void operator()(T* sym) const {
    if (sym) {
      SparseCleanup(*sym);
      delete sym;
      sym = nullptr;
    }
  }
};
 
template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
struct SparseTypesTraitBase {
  typedef DenseVecT AccelDenseVector;
  typedef DenseMatT AccelDenseMatrix;
  typedef SparseMatT AccelSparseMatrix;
 
  typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
  typedef NumFactT NumericFactorization;
 
  typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
  typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
};
 
template <typename Scalar>
struct SparseTypesTrait {};
 
template <>
struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
                                                       SparseOpaqueFactorization_Double> {};
 
template <>
struct SparseTypesTrait<float>
    : SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
};
 
}  // end namespace internal
 
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
 protected:
  using Base = SparseSolverBase<AccelerateImpl>;
  using Base::derived;
  using Base::m_isInitialized;
 
 public:
  using Base::_solve_impl;
 
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
  enum { UpLo = UpLo_ };
 
  using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
  using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
  using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
  using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
  using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
  using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
  using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
 
  AccelerateImpl() {
    m_isInitialized = false;
 
    EIGEN_IF_CONSTEXPR ((UpLo_ & Symmetric) == Symmetric) {
      m_sparseKind = SparseSymmetric;
      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
    } else EIGEN_IF_CONSTEXPR ((UpLo_ & UnitLower) == UnitLower) {
      m_sparseKind = SparseUnitTriangular;
      m_triType = SparseLowerTriangle;
    } else EIGEN_IF_CONSTEXPR ((UpLo_ & UnitUpper) == UnitUpper) {
      m_sparseKind = SparseUnitTriangular;
      m_triType = SparseUpperTriangle;
    } else EIGEN_IF_CONSTEXPR ((UpLo_ & StrictlyLower) == StrictlyLower) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseLowerTriangle;
    } else EIGEN_IF_CONSTEXPR ((UpLo_ & StrictlyUpper) == StrictlyUpper) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseUpperTriangle;
    } else EIGEN_IF_CONSTEXPR ((UpLo_ & Lower) == Lower) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseLowerTriangle;
    } else EIGEN_IF_CONSTEXPR ((UpLo_ & Upper) == Upper) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseUpperTriangle;
    } else {
      m_sparseKind = SparseOrdinary;
      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
    }
 
    m_order = SparseOrderDefault;
  }
 
  explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
 
  inline Index cols() const { return m_nCols; }
  inline Index rows() const { return m_nRows; }
 
  ComputationInfo info() const {
    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
    return m_info;
  }
 
  void analyzePattern(const MatrixType& matrix);
 
  void factorize(const MatrixType& matrix);
 
  void compute(const MatrixType& matrix);
 
  template <typename Rhs, typename Dest>
  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;

  void setOrder(SparseOrder_t order) { m_order = order; }
 
 private:
  template <typename T>
  void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
    const Index nColumnsStarts = a.cols() + 1;
 
    columnStarts.resize(nColumnsStarts);
 
    for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
 
    SparseAttributes_t attributes{};
    attributes.transpose = false;
    attributes.triangle = m_triType;
    attributes.kind = m_sparseKind;
 
    SparseMatrixStructure structure{};
    structure.attributes = attributes;
    structure.rowCount = static_cast<int>(a.rows());
    structure.columnCount = static_cast<int>(a.cols());
    structure.blockSize = 1;
    structure.columnStarts = columnStarts.data();
    structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
 
    A.structure = structure;
    A.data = const_cast<T*>(a.valuePtr());
  }
 
  void doAnalysis(AccelSparseMatrix& A) {
    m_numericFactorization.reset();
 
    SparseSymbolicFactorOptions opts{};
    opts.control = SparseDefaultControl;
    opts.orderMethod = m_order;
    opts.order = nullptr;
    opts.ignoreRowsAndColumns = nullptr;
    opts.malloc = malloc;
    opts.free = free;
    opts.reportError = nullptr;
 
    m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
 
    SparseStatus_t status = m_symbolicFactorization->status;
 
    updateInfoStatus(status);
 
    if (status != SparseStatusOK) m_symbolicFactorization.reset();
  }
 
  void doFactorization(AccelSparseMatrix& A) {
    SparseStatus_t status = SparseStatusReleased;
 
    if (m_symbolicFactorization) {
      m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
 
      status = m_numericFactorization->status;
 
      if (status != SparseStatusOK) m_numericFactorization.reset();
    }
 
    updateInfoStatus(status);
  }
 
 protected:
  void updateInfoStatus(SparseStatus_t status) const {
    switch (status) {
      case SparseStatusOK:
        m_info = Success;
        break;
      case SparseFactorizationFailed:
      case SparseMatrixIsSingular:
        m_info = NumericalIssue;
        break;
      case SparseInternalError:
      case SparseParameterError:
      case SparseStatusReleased:
      default:
        m_info = InvalidInput;
        break;
    }
  }
 
  mutable ComputationInfo m_info;
  Index m_nRows, m_nCols;
  std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
  std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
  SparseKind_t m_sparseKind;
  SparseTriangle_t m_triType;
  SparseOrder_t m_order;
};

template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
  EIGEN_IF_CONSTEXPR (EnforceSquare_) eigen_assert(a.rows() == a.cols());
 
  m_nRows = a.rows();
  m_nCols = a.cols();
 
  AccelSparseMatrix A{};
  std::vector<long> columnStarts;
 
  buildAccelSparseMatrix(a, A, columnStarts);
 
  doAnalysis(A);
 
  if (m_symbolicFactorization) doFactorization(A);
 
  m_isInitialized = true;
}

template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
  EIGEN_IF_CONSTEXPR (EnforceSquare_) eigen_assert(a.rows() == a.cols());
 
  m_nRows = a.rows();
  m_nCols = a.cols();
 
  AccelSparseMatrix A{};
  std::vector<long> columnStarts;
 
  buildAccelSparseMatrix(a, A, columnStarts);
 
  doAnalysis(A);
 
  m_isInitialized = true;
}

template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
  eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
  eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
 
  EIGEN_IF_CONSTEXPR (EnforceSquare_) eigen_assert(a.rows() == a.cols());
 
  AccelSparseMatrix A{};
  std::vector<long> columnStarts;
 
  buildAccelSparseMatrix(a, A, columnStarts);
 
  doFactorization(A);
}
 
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
template <typename Rhs, typename Dest>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
                                                                              MatrixBase<Dest>& x) const {
  if (!m_numericFactorization) {
    m_info = InvalidInput;
    return;
  }
 
  eigen_assert(m_nRows == b.rows());
  eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
 
  SparseStatus_t status = SparseStatusOK;
 
  Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
  Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
 
  AccelDenseMatrix xmat{};
  xmat.attributes = SparseAttributes_t();
  xmat.columnCount = static_cast<int>(x.cols());
  xmat.rowCount = static_cast<int>(x.rows());
  xmat.columnStride = xmat.rowCount;
  xmat.data = x_ptr;
 
  AccelDenseMatrix bmat{};
  bmat.attributes = SparseAttributes_t();
  bmat.columnCount = static_cast<int>(b.cols());
  bmat.rowCount = static_cast<int>(b.rows());
  bmat.columnStride = bmat.rowCount;
  bmat.data = b_ptr;
 
  SparseSolve(*m_numericFactorization, bmat, xmat);
 
  updateInfoStatus(status);
}
 
}  // end namespace Eigen
 
#endif  // EIGEN_ACCELERATESUPPORT_H