MatrixCSR.h

// Copyright (C) 2021-2022 Garth N. Wells and Chris N. Richardson
//
// This file is part of DOLFINx (https://www.fenicsproject.org)
//
// SPDX-License-Identifier:    LGPL-3.0-or-later
 
#pragma once
 
#include "SparsityPattern.h"
#include <dolfinx/common/MPI.h>
#include <dolfinx/graph/AdjacencyList.h>
#include <mpi.h>
#include <numeric>
#include <vector>
#include <xtensor/xtensor.hpp>
#include <xtl/xspan.hpp>
 
namespace dolfinx::la
{
 
namespace impl
{
template <typename U, typename V, typename W, typename X>
void set_csr(U&& data, const V& cols, const V& row_ptr, const W& x,
             const X& xrows, const X& xcols,
             [[maybe_unused]] typename X::value_type local_size)
{
  assert(x.size() == xrows.size() * xcols.size());
  for (std::size_t r = 0; r < xrows.size(); ++r)
  {
    // Row index and current data row
    auto row = xrows[r];
    using T = typename W::value_type;
    const T* xr = x.data() + r * xcols.size();
 
#ifndef NDEBUG
    if (row >= local_size)
      throw std::runtime_error("Local row out of range");
#endif
 
    // Columns indices for row
    auto cit0 = std::next(cols.begin(), row_ptr[row]);
    auto cit1 = std::next(cols.begin(), row_ptr[row + 1]);
    for (std::size_t c = 0; c < xcols.size(); ++c)
    {
      // Find position of column index
      auto it = std::lower_bound(cit0, cit1, xcols[c]);
      assert(it != cit1);
      std::size_t d = std::distance(cols.begin(), it);
      assert(d < data.size());
      data[d] = xr[c];
    }
  }
}
 
template <typename U, typename V, typename W, typename X>
void add_csr(U&& data, const V& cols, const V& row_ptr, const W& x,
             const X& xrows, const X& xcols)
{
  assert(x.size() == xrows.size() * xcols.size());
  for (std::size_t r = 0; r < xrows.size(); ++r)
  {
    // Row index and current data row
    auto row = xrows[r];
    using T = typename W::value_type;
    const T* xr = x.data() + r * xcols.size();
 
#ifndef NDEBUG
    if (row >= (int)row_ptr.size())
      throw std::runtime_error("Local row out of range");
#endif
 
    // Columns indices for row
    auto cit0 = std::next(cols.begin(), row_ptr[row]);
    auto cit1 = std::next(cols.begin(), row_ptr[row + 1]);
    for (std::size_t c = 0; c < xcols.size(); ++c)
    {
      // Find position of column index
      auto it = std::lower_bound(cit0, cit1, xcols[c]);
      assert(it != cit1);
      std::size_t d = std::distance(cols.begin(), it);
      assert(d < data.size());
      data[d] += xr[c];
    }
  }
}
} // namespace impl
 
template <typename T, class Allocator = std::allocator<T>>
class MatrixCSR
{
public:
  using value_type = T;
 
  using allocator_type = Allocator;
 
  auto mat_set_values()
  {
    return [&](const xtl::span<const std::int32_t>& rows,
               const xtl::span<const std::int32_t>& cols,
               const xtl::span<const T>& data) -> int
    {
      this->set(data, rows, cols);
      return 0;
    };
  }
 
  auto mat_add_values()
  {
    return [&](const xtl::span<const std::int32_t>& rows,
               const xtl::span<const std::int32_t>& cols,
               const xtl::span<const T>& data) -> int
    {
      this->add(data, rows, cols);
      return 0;
    };
  }
 
  MatrixCSR(const SparsityPattern& p, const Allocator& alloc = Allocator())
      : _index_maps({p.index_map(0),
                     std::make_shared<common::IndexMap>(p.column_index_map())}),
        _bs({p.block_size(0), p.block_size(1)}),
        _data(p.num_nonzeros(), 0, alloc),
        _cols(p.graph().array().begin(), p.graph().array().end()),
        _row_ptr(p.graph().offsets().begin(), p.graph().offsets().end()),
        _comm(p.index_map(0)->comm(common::IndexMap::Direction::reverse)),
        _ghost_row_to_neighbor_rank(_index_maps[0]->ghost_owners())
  {
    // TODO: handle block sizes
    if (_bs[0] > 1 or _bs[1] > 1)
      throw std::runtime_error("Block size not yet supported");
 
    // Compute off-diagonal offset for each row
    xtl::span<const std::int32_t> num_diag_nnz = p.off_diagonal_offset();
    _off_diagonal_offset.reserve(num_diag_nnz.size());
    std::transform(num_diag_nnz.begin(), num_diag_nnz.end(), _row_ptr.begin(),
                   std::back_inserter(_off_diagonal_offset),
                   std::plus<std::int32_t>());
 
    // Precompute some data for ghost updates via MPI
    const std::array local_size
        = {_index_maps[0]->size_local(), _index_maps[1]->size_local()};
    const std::array local_range
        = {_index_maps[0]->local_range(), _index_maps[1]->local_range()};
 
    const std::vector<std::int64_t>& ghosts0 = _index_maps[0]->ghosts();
    const std::vector<std::int64_t>& ghosts1 = _index_maps[1]->ghosts();
 
    int outdegree(-1);
    {
      int indegree(-1), weighted(-1);
      MPI_Dist_graph_neighbors_count(_comm.comm(), &indegree, &outdegree,
                                     &weighted);
    }
 
    // Compute size of data to send to each neighbor
    std::vector<std::int32_t> data_per_proc(outdegree, 0);
    for (std::size_t i = 0; i < _ghost_row_to_neighbor_rank.size(); ++i)
    {
      assert(_ghost_row_to_neighbor_rank[i] < data_per_proc.size());
      data_per_proc[_ghost_row_to_neighbor_rank[i]]
          += _row_ptr[local_size[0] + i + 1] - _row_ptr[local_size[0] + i];
    }
 
    // Compute send displacements for values and indices (x2)
    _val_send_disp.resize(outdegree + 1, 0);
    std::partial_sum(data_per_proc.begin(), data_per_proc.end(),
                     std::next(_val_send_disp.begin()));
 
    std::vector<int> index_send_disp(outdegree + 1);
    std::transform(_val_send_disp.begin(), _val_send_disp.end(),
                   index_send_disp.begin(), [](int d) { return d * 2; });
 
    // For each ghost row, pack and send indices to neighborhood
    std::vector<int> insert_pos(index_send_disp);
    std::vector<std::int64_t> ghost_index_data(index_send_disp.back());
    for (std::size_t i = 0; i < _ghost_row_to_neighbor_rank.size(); ++i)
    {
      const int neighbor_rank = _ghost_row_to_neighbor_rank[i];
      int row_id = local_size[0] + i;
      for (int j = _row_ptr[row_id]; j < _row_ptr[row_id + 1]; ++j)
      {
        // Get position in send buffer to place data for this neighbour
        const std::int32_t idx_pos = insert_pos[neighbor_rank];
 
        // Pack send data (row, col) as global indices
        ghost_index_data[idx_pos] = ghosts0[i];
        if (const std::int32_t col_local = _cols[j]; col_local < local_size[1])
          ghost_index_data[idx_pos + 1] = col_local + local_range[1][0];
        else
          ghost_index_data[idx_pos + 1] = ghosts1[col_local - local_size[1]];
 
        insert_pos[neighbor_rank] += 2;
      }
    }
 
    // Create and communicate adjacency list to neighborhood
    const graph::AdjacencyList<std::int64_t> ghost_index_data_out(
        std::move(ghost_index_data), std::move(index_send_disp));
    const graph::AdjacencyList<std::int64_t> ghost_index_data_in
        = dolfinx::MPI::neighbor_all_to_all(_comm.comm(), ghost_index_data_out);
 
    // Store received offsets for future use, when transferring data values.
    _val_recv_disp.resize(ghost_index_data_in.offsets().size());
    std::transform(ghost_index_data_in.offsets().begin(),
                   ghost_index_data_in.offsets().end(), _val_recv_disp.begin(),
                   [](int d) { return d / 2; });
 
    // Global to local map for ghost columns
    std::map<std::int64_t, std::int32_t> global_to_local;
    std::int32_t local_i = local_size[1];
    for (std::int64_t global_i : ghosts1)
      global_to_local.insert({global_i, local_i++});
 
    // Compute location in which data for each index should be stored
    // when received
    const std::vector<std::int64_t>& ghost_index_array
        = ghost_index_data_in.array();
    for (std::size_t i = 0; i < ghost_index_array.size(); i += 2)
    {
      // Row must be on this process
      const std::int32_t local_row = ghost_index_array[i] - local_range[0][0];
      assert(local_row >= 0 and local_row < local_size[0]);
 
      // Column may be owned or unowned
      std::int32_t local_col = ghost_index_array[i + 1] - local_range[1][0];
      if (local_col < 0 or local_col >= local_size[1])
      {
        const auto it = global_to_local.find(ghost_index_array[i + 1]);
        assert(it != global_to_local.end());
        local_col = it->second;
      }
      auto cit0 = std::next(_cols.begin(), _row_ptr[local_row]);
      auto cit1 = std::next(_cols.begin(), _row_ptr[local_row + 1]);
 
      // Find position of column index and insert data
      auto cit = std::lower_bound(cit0, cit1, local_col);
      assert(cit != cit1);
      assert(*cit == local_col);
      std::size_t d = std::distance(_cols.begin(), cit);
      _unpack_pos.push_back(d);
    }
  }
 
  MatrixCSR(MatrixCSR&& A) = default;
 
  void set(T x) { std::fill(_data.begin(), _data.end(), x); }
 
  void set(const xtl::span<const T>& x,
           const xtl::span<const std::int32_t>& rows,
           const xtl::span<const std::int32_t>& cols)
  {
    impl::set_csr(_data, _cols, _row_ptr, x, rows, cols,
                  _index_maps[0]->size_local());
  }
 
  void add(const xtl::span<const T>& x,
           const xtl::span<const std::int32_t>& rows,
           const xtl::span<const std::int32_t>& cols)
  {
    impl::add_csr(_data, _cols, _row_ptr, x, rows, cols);
  }
 
  std::int32_t num_owned_rows() const { return _index_maps[0]->size_local(); }
 
  std::int32_t num_all_rows() const { return _row_ptr.size() - 1; }
 
  xt::xtensor<T, 2> to_dense() const
  {
    const std::int32_t nrows = num_all_rows();
    const std::int32_t ncols
        = _index_maps[1]->size_local() + _index_maps[1]->num_ghosts();
    xt::xtensor<T, 2> A = xt::zeros<T>({nrows, ncols});
    for (std::size_t r = 0; r < nrows; ++r)
      for (int j = _row_ptr[r]; j < _row_ptr[r + 1]; ++j)
        A(r, _cols[j]) = _data[j];
    return A;
  }
 
  void finalize()
  {
    finalize_begin();
    finalize_end();
  }
 
  void finalize_begin()
  {
    const std::int32_t local_size0 = _index_maps[0]->size_local();
    const std::int32_t num_ghosts0 = _index_maps[0]->num_ghosts();
 
    // For each ghost row, pack and send values to send to neighborhood
    std::vector<int> insert_pos(_val_send_disp);
    std::vector<T> ghost_value_data(_val_send_disp.back());
    for (int i = 0; i < num_ghosts0; ++i)
    {
      const int neighbor_rank = _ghost_row_to_neighbor_rank[i];
 
      // Get position in send buffer to place data to send to this neighbour
      const std::int32_t val_pos = insert_pos[neighbor_rank];
      std::copy(std::next(_data.data(), _row_ptr[local_size0 + i]),
                std::next(_data.data(), _row_ptr[local_size0 + i + 1]),
                std::next(ghost_value_data.begin(), val_pos));
      insert_pos[neighbor_rank]
          += _row_ptr[local_size0 + i + 1] - _row_ptr[local_size0 + i];
    }
 
    _ghost_value_data_in.resize(_val_recv_disp.back());
 
    // Compute data sizes for send and receive from displacements
    std::vector<int> val_send_count(_val_send_disp.size() - 1);
    std::adjacent_difference(std::next(_val_send_disp.begin()),
                             _val_send_disp.end(), val_send_count.begin());
 
    std::vector<int> val_recv_count(_val_recv_disp.size() - 1);
    std::adjacent_difference(std::next(_val_recv_disp.begin()),
                             _val_recv_disp.end(), val_recv_count.begin());
 
    int status = MPI_Ineighbor_alltoallv(
        ghost_value_data.data(), val_send_count.data(), _val_send_disp.data(),
        dolfinx::MPI::mpi_type<T>(), _ghost_value_data_in.data(),
        val_recv_count.data(), _val_recv_disp.data(),
        dolfinx::MPI::mpi_type<T>(), _comm.comm(), &_request);
    assert(status == MPI_SUCCESS);
  }
 
  void finalize_end()
  {
    int status = MPI_Wait(&_request, MPI_STATUS_IGNORE);
    assert(status == MPI_SUCCESS);
 
    // Add to local rows
    assert(_ghost_value_data_in.size() == _unpack_pos.size());
    for (std::size_t i = 0; i < _ghost_value_data_in.size(); ++i)
      _data[_unpack_pos[i]] += _ghost_value_data_in[i];
 
    // Set ghost row data to zero
    const std::int32_t local_size0 = _index_maps[0]->size_local();
    std::fill(std::next(_data.begin(), _row_ptr[local_size0]), _data.end(), 0);
  }
 
  double norm_squared() const
  {
    const std::size_t num_owned_rows = _index_maps[0]->size_local();
    assert(num_owned_rows < _row_ptr.size());
 
    const double norm_sq_local = std::accumulate(
        _data.cbegin(), std::next(_data.cbegin(), _row_ptr[num_owned_rows]),
        double(0), [](double norm, T y) { return norm + std::norm(y); });
    double norm_sq;
    MPI_Allreduce(&norm_sq_local, &norm_sq, 1, MPI_DOUBLE, MPI_SUM,
                  _comm.comm());
    return norm_sq;
  }
 
  const std::array<std::shared_ptr<const common::IndexMap>, 2>&
  index_maps() const
  {
    return _index_maps;
  }
 
  std::vector<T>& values() { return _data; }
 
  const std::vector<T>& values() const { return _data; }
 
  const std::vector<std::int32_t>& row_ptr() const { return _row_ptr; }
 
  const std::vector<std::int32_t>& cols() const { return _cols; }
 
  const std::vector<std::int32_t>& off_diag_offset() const
  {
    return _off_diagonal_offset;
  }
 
private:
  // Maps describing the data layout for rows and columns
  std::array<std::shared_ptr<const common::IndexMap>, 2> _index_maps;
 
  // Block sizes
  std::array<int, 2> _bs;
 
  // Matrix data
  std::vector<T, Allocator> _data;
  std::vector<std::int32_t> _cols, _row_ptr;
 
  // Start of off-diagonal (unowned columns) on each row
  std::vector<std::int32_t> _off_diagonal_offset;
 
  // Neighborhood communicator (ghost->owner communicator for rows)
  dolfinx::MPI::Comm _comm;
 
  // -- Precomputed data for finalize/update
 
  // Request in non-blocking communication
  MPI_Request _request;
 
  // Position in _data to add received data
  std::vector<int> _unpack_pos;
 
  // Displacements for alltoall for each neighbor when sending and
  // receiving
  std::vector<int> _val_send_disp, _val_recv_disp;
 
  // Ownership of each row, by neighbor
  std::vector<int> _ghost_row_to_neighbor_rank;
 
  // Temporary store for finalize data during non-blocking communication
  std::vector<T> _ghost_value_data_in;
};
 
} // namespace dolfinx::la