cholmod.hh

// SPDX-FileCopyrightText: Copyright © DUNE Project contributors, see file LICENSE.md in module root
// SPDX-License-Identifier: LicenseRef-GPL-2.0-only-with-DUNE-exception
// -*- tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 2 -*-
// vi: set et ts=4 sw=2 sts=2:
#pragma once
 
#if HAVE_SUITESPARSE_CHOLMOD || defined DOXYGEN
 
#include <dune/common/fmatrix.hh>
#include <dune/common/fvector.hh>
#include <dune/istl/bcrsmatrix.hh>
#include <dune/istl/bvector.hh>
#include<dune/istl/solver.hh>
#include <dune/istl/solverregistry.hh>
#include <dune/istl/foreach.hh>
 
#include <vector>
#include <memory>
 
#include <cholmod.h>
 
namespace Dune {
 
namespace Impl{
 
  struct NoIgnore
  {
    const NoIgnore& operator[](std::size_t) const { return *this; }
    explicit operator bool() const { return false; }
    static constexpr std::size_t size() { return 0; }
 
  };
 
 
  template<class BlockedVector, class FlatVector>
  void copyToFlatVector(const BlockedVector& blockedVector, FlatVector& flatVector)
  {
    // traverse the vector once just to compute the size
    std::size_t len = flatVectorForEach(blockedVector, [&](auto&&, auto...){});
    flatVector.resize(len);
 
    flatVectorForEach(blockedVector, [&](auto&& entry, auto offset){
      flatVector[offset] = entry;
    });
  }
 
  // special (dummy) case for NoIgnore
  template<class FlatVector>
  void copyToFlatVector(const NoIgnore&, FlatVector&)
  {
    // just do nothing
    return;
  }
 
  template<class FlatVector, class BlockedVector>
  void copyToBlockedVector(const FlatVector& flatVector, BlockedVector& blockedVector)
  {
    flatVectorForEach(blockedVector, [&](auto& entry, auto offset){
      entry = flatVector[offset];
    });
  }
 
  // wrapper class for C function calls to CHOLMOD itself.
  // The CHOLMOD API has different functions for different index types.
  template <class Index>
  struct CholmodMethodChooser;
 
  // specialization using 'int' to store indices
  template <>
  struct CholmodMethodChooser<int>
  {
    [[nodiscard]]
    static cholmod_dense* allocate_dense(size_t nrow, size_t ncol, size_t d, int xtype, cholmod_common *c)
    {
      return ::cholmod_allocate_dense(nrow,ncol,d,xtype,c);
    }
 
    [[nodiscard]]
    static cholmod_sparse* allocate_sparse(size_t nrow, size_t ncol, size_t nzmax, int sorted, int packed, int stype, int xtype, cholmod_common *c)
    {
      return ::cholmod_allocate_sparse(nrow,ncol,nzmax,sorted,packed,stype,xtype,c);
    }
 
    [[nodiscard]]
    static cholmod_factor* analyze(cholmod_sparse *A, cholmod_common *c)
    {
      return ::cholmod_analyze(A,c);
    }
 
    static int defaults(cholmod_common *c)
    {
      return ::cholmod_defaults(c);
    }
 
    static int factorize(cholmod_sparse *A, cholmod_factor *L, cholmod_common *c)
    {
      return ::cholmod_factorize(A,L,c);
    }
 
    static int finish(cholmod_common *c)
    {
      return ::cholmod_finish(c);
    }
 
    static int free_dense (cholmod_dense **X, cholmod_common *c)
    {
      return ::cholmod_free_dense(X,c);
    }
 
    static int free_factor(cholmod_factor **L, cholmod_common *c)
    {
      return ::cholmod_free_factor(L,c);
    }
 
    static int free_sparse(cholmod_sparse **A, cholmod_common *c)
    {
      return ::cholmod_free_sparse(A,c);
    }
 
    [[nodiscard]]
    static cholmod_dense* solve(int sys, cholmod_factor *L, cholmod_dense *B, cholmod_common *c)
    {
      return ::cholmod_solve(sys,L,B,c);
    }
 
    static int start(cholmod_common *c)
    {
      return ::cholmod_start(c);
    }
  };
 
  // specialization using 'SuiteSparse_long' to store indices
  template <>
  struct CholmodMethodChooser<SuiteSparse_long>
  {
    [[nodiscard]]
    static cholmod_dense* allocate_dense(size_t nrow, size_t ncol, size_t d, int xtype, cholmod_common *c)
    {
      return ::cholmod_l_allocate_dense(nrow,ncol,d,xtype,c);
    }
 
    [[nodiscard]]
    static cholmod_sparse* allocate_sparse(size_t nrow, size_t ncol, size_t nzmax, int sorted, int packed, int stype, int xtype, cholmod_common *c)
    {
      return ::cholmod_l_allocate_sparse(nrow,ncol,nzmax,sorted,packed,stype,xtype,c);
    }
 
    [[nodiscard]]
    static cholmod_factor* analyze(cholmod_sparse *A, cholmod_common *c)
    {
      return ::cholmod_l_analyze(A,c);
    }
 
    static int defaults(cholmod_common *c)
    {
      return ::cholmod_l_defaults(c);
    }
 
    static int factorize(cholmod_sparse *A, cholmod_factor *L, cholmod_common *c)
    {
      return ::cholmod_l_factorize(A,L,c);
    }
 
    static int finish(cholmod_common *c)
    {
      return ::cholmod_l_finish(c);
    }
 
    static int free_dense (cholmod_dense **X, cholmod_common *c)
    {
      return ::cholmod_l_free_dense(X,c);
    }
 
    static int free_factor (cholmod_factor **L, cholmod_common *c)
    {
      return ::cholmod_l_free_factor(L,c);
    }
 
    static int free_sparse(cholmod_sparse **A, cholmod_common *c)
    {
      return ::cholmod_l_free_sparse(A,c);
    }
 
    [[nodiscard]]
    static cholmod_dense* solve(int sys, cholmod_factor *L, cholmod_dense *B, cholmod_common *c)
    {
      return ::cholmod_l_solve(sys,L,B,c);
    }
 
    static int start(cholmod_common *c)
    {
      return ::cholmod_l_start(c);
    }
  };
 
} //namespace Impl
 
template<class Vector, class Index=int>
class Cholmod : public InverseOperator<Vector, Vector>
{
  static_assert(std::is_same_v<Index,int> || std::is_same_v<Index,SuiteSparse_long>,
                "Index type must be either 'int' or 'SuiteSparse_long'!");
 
  using CholmodMethod = Impl::CholmodMethodChooser<Index>;
 
public:
 
  Cholmod()
  {
    CholmodMethod::start(&c_);
  }
 
  ~Cholmod()
  {
    if (L_)
      CholmodMethod::free_factor(&L_, &c_);
    CholmodMethod::finish(&c_);
  }
 
  // forbid copying to avoid freeing memory twice
  Cholmod(const Cholmod&) = delete;
  Cholmod& operator=(const Cholmod&) = delete;
 
 
  void apply (Vector& x, Vector& b, [[maybe_unused]] double reduction, InverseOperatorResult& res) override
  {
    apply(x,b,res);
  }
 
  void apply(Vector& x, Vector& b, InverseOperatorResult& res) override
  {
    // do nothing if N=0
    if ( nIsZero_ )
    {
        return;
    }
 
    if (x.size() != b.size())
      DUNE_THROW(Exception, "Error in apply(): sizes of x and b do not match!");
 
    // cast to double array
    auto b2 = std::make_unique<double[]>(L_->n);
    auto x2 = std::make_unique<double[]>(L_->n);
 
    // copy to cholmod
    auto bp = b2.get();
 
    flatVectorForEach(b, [&](auto&& entry, auto&& flatIndex){
      if ( subIndices_.empty() )
        bp[ flatIndex ] = entry;
      else
        if( subIndices_[ flatIndex ] != std::numeric_limits<std::size_t>::max() )
          bp[ subIndices_[ flatIndex ] ] = entry;
    });
 
      // create a cholmod dense object
    auto b3 = make_cholmod_dense(CholmodMethod::allocate_dense(L_->n, 1, L_->n, CHOLMOD_REAL, &c_), &c_);
 
    // cast because void-ptr
    auto b4 = static_cast<double*>(b3->x);
    std::copy(b2.get(), b2.get() + L_->n, b4);
 
    // solve for a cholmod x object
    auto x3 = make_cholmod_dense(CholmodMethod::solve(CHOLMOD_A, L_, b3.get(), &c_), &c_);
    // cast because void-ptr
    auto xp = static_cast<double*>(x3->x);
 
    // copy into x
    flatVectorForEach(x, [&](auto&& entry, auto&& flatIndex){
      if ( subIndices_.empty() )
        entry = xp[ flatIndex ];
      else
        if( subIndices_[ flatIndex ] != std::numeric_limits<std::size_t>::max() )
          entry = xp[ subIndices_[ flatIndex ] ];
    });
 
    // statistics for a direct solver
    res.iterations = 1;
    res.converged = true;
  }
 
 
  template<class Matrix>
  void setMatrix(const Matrix& matrix)
  {
    const Impl::NoIgnore* noIgnore = nullptr;
    setMatrix(matrix, noIgnore);
  }
 
  template<class Matrix, class Ignore>
  void setMatrix(const Matrix& matrix, const Ignore* ignore)
  {
    // count the number of entries and diagonal entries
    size_t nonZeros = 0;
    size_t numberOfIgnoredDofs = 0;
 
 
    auto [flatRows,flatCols] = flatMatrixForEach( matrix, [&](auto&& /*entry*/, auto&& flatRowIndex, auto&& flatColIndex){
      if( flatRowIndex <= flatColIndex )
        nonZeros++;
    });
 
    std::vector<bool> flatIgnore;
 
    if ( ignore )
    {
      Impl::copyToFlatVector(*ignore,flatIgnore);
      numberOfIgnoredDofs = std::count(flatIgnore.begin(),flatIgnore.end(),true);
    }
 
    nIsZero_ = (size_t(flatRows) <= numberOfIgnoredDofs);
 
    if ( nIsZero_ )
    {
        return;
    }
 
    // Total number of rows
    size_t N = flatRows - numberOfIgnoredDofs;
 
    /*
    * CHOLMOD uses compressed-column sparse matrices, but for symmetric
    * matrices this is the same as the compressed-row sparse matrix used
    * by DUNE.  So we can just store Mᵀ instead of M (as M = Mᵀ).
    */
    const auto deleter = [c = &this->c_](auto* p) {
      CholmodMethod::free_sparse(&p, c);
    };
    auto M = std::unique_ptr<cholmod_sparse, decltype(deleter)>(
      CholmodMethod::allocate_sparse(N,             // # rows
                                     N,             // # cols
                                     nonZeros,      // # of nonzeroes
                                     1,             // indices are sorted ( 1 = true)
                                     1,             // matrix is "packed" ( 1 = true)
                                     -1,            // stype of matrix ( -1 = consider the lower part only )
                                     CHOLMOD_REAL,  // xtype of matrix ( CHOLMOD_REAL = single array, no complex numbers)
                                     &c_            // cholmod_common ptr
      ), deleter);
 
    // copy the data of BCRS matrix to Cholmod Sparse matrix
    Index* Ap = static_cast<Index*>(M->p);
    Index* Ai = static_cast<Index*>(M->i);
    double* Ax = static_cast<double*>(M->x);
 
 
    if ( ignore )
    {
      // init the mapping
      subIndices_.resize(flatRows,std::numeric_limits<std::size_t>::max());
 
      std::size_t subIndexCounter = 0;
 
      for ( std::size_t i=0; i<flatRows; i++ )
      {
        if ( not  flatIgnore[ i ] )
        {
          subIndices_[ i ] = subIndexCounter++;
        }
      }
    }
 
    // at first, we need to compute the row starts "Ap"
    // therefore, we count all (not ignored) entries in each row and in the end we accumulate everything
    flatMatrixForEach(matrix, [&](auto&& /*entry*/, auto&& flatRowIndex, auto&& flatColIndex){
 
      // stop if ignored
      if ( ignore and ( flatIgnore[flatRowIndex] or flatIgnore[flatColIndex] ) )
        return;
 
      // stop if in lower half
      if ( flatRowIndex > flatColIndex )
        return;
 
      // ok, count the entry
      auto idx = ignore ? subIndices_[flatRowIndex] : flatRowIndex;
      Ap[idx+1]++;
 
    });
 
    // now accumulate
    Ap[0] = 0;
    for ( size_t i=0; i<N; i++ )
    {
      Ap[i+1] += Ap[i];
    }
 
    // we need a compressed row position counter
    std::vector<std::size_t> rowPosition(N,0);
 
    // now we can set the entries
    flatMatrixForEach(matrix, [&](auto&& entry, auto&& flatRowIndex, auto&& flatColIndex){
 
      // stop if ignored
      if ( ignore and ( flatIgnore[flatRowIndex] or flatIgnore[flatColIndex] ) )
        return;
 
      // stop if in lower half
      if ( flatRowIndex > flatColIndex )
        return;
 
      // ok, set the entry
      auto rowIdx = ignore ? subIndices_[flatRowIndex] : flatRowIndex;
      auto colIdx = ignore ? subIndices_[flatColIndex] : flatColIndex;
      auto rowStart = Ap[rowIdx];
      auto rowPos   = rowPosition[rowIdx];
      Ai[ rowStart + rowPos ] = colIdx;
      Ax[ rowStart + rowPos ] = entry;
      rowPosition[rowIdx]++;
 
    });
 
    // Now analyse the pattern and optimal row order
    L_ = CholmodMethod::analyze(M.get(), &c_);
 
    // Do the factorization (this may take some time)
    CholmodMethod::factorize(M.get(), L_, &c_);
  }
 
  SolverCategory::Category category() const override
  {
    return SolverCategory::Category::sequential;
  }
 
  cholmod_common& cholmodCommonObject()
  {
      return c_;
  }
 
  cholmod_factor& cholmodFactor()
  {
    return *L_;
  }
 
  const cholmod_factor& cholmodFactor() const
  {
    return *L_;
  }
private:
 
  // create a std::unique_ptr to a cholmod_dense object with a deleter
  // that calls the appropriate cholmod cleanup routine
  auto make_cholmod_dense(cholmod_dense* x, cholmod_common* c)
  {
    const auto deleter = [c](auto* p) {
      CholmodMethod::free_dense(&p, c);
    };
    return std::unique_ptr<cholmod_dense, decltype(deleter)>(x, deleter);
  }
 
  cholmod_common c_;
  cholmod_factor* L_ = nullptr;
 
  // indicator for a 0x0 problem (due to ignore dof's)
  bool nIsZero_ = false;
 
  // vector mapping all indices in flat order to the not ignored indices
  std::vector<std::size_t> subIndices_;
};
 
  DUNE_REGISTER_SOLVER("cholmod",
                       [](auto opTraits, const auto& op, const Dune::ParameterTree& config)
                       -> std::shared_ptr<typename decltype(opTraits)::solver_type>
                       {
                         using OpTraits = decltype(opTraits);
                         using M = typename OpTraits::matrix_type;
                         using D = typename OpTraits::domain_type;
                         // works only for sequential operators
                         if constexpr (OpTraits::isParallel){
                           if(opTraits.getCommOrThrow(op).communicator().size() > 1)
                             DUNE_THROW(Dune::InvalidStateException, "CholMod works only for sequential operators.");
                         }
                         if constexpr (OpTraits::isAssembled &&
                                       // check whether the Matrix field_type is double or float
                                       (std::is_same_v<typename FieldTraits<D>::field_type, double> ||
                                       std::is_same_v<typename FieldTraits<D>::field_type, float>)){
                           const auto& A = opTraits.getAssembledOpOrThrow(op);
                           const M& mat = A->getmat();
                           auto solver = std::make_shared<Dune::Cholmod<D>>();
                           solver->setMatrix(mat);
                           return solver;
                         }
                         DUNE_THROW(UnsupportedType,
                                    "Unsupported Type in Cholmod (only double and float supported)");
                       });
 
} /* namespace Dune */
 
#endif // HAVE_SUITESPARSE_CHOLMOD