// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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//
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//   this list of conditions and the following disclaimer in the documentation
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//
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// Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin)

#ifndef CERES_INTERNAL_CUDA_BLOCK_STRUCTURE_H_
#define CERES_INTERNAL_CUDA_BLOCK_STRUCTURE_H_

#include "ceres/internal/config.h"

#ifndef CERES_NO_CUDA

#include "ceres/block_structure.h"
#include "ceres/cuda_buffer.h"

namespace ceres::internal {
class CudaBlockStructureTest;

// This class stores a read-only block-sparse structure in gpu memory.
// Invariants are the same as those of CompressedRowBlockStructure.
// In order to simplify allocation and copying data to gpu, cells from all
// row-blocks are stored in a single array sequentially. Array
// first_cell_in_row_block of size num_row_blocks + 1 allows to identify range
// of cells corresponding to a row-block. Cells corresponding to i-th row-block
// are stored in sub-array cells[first_cell_in_row_block[i]; ...
// first_cell_in_row_block[i + 1] - 1], and their order is preserved.
class CERES_NO_EXPORT CudaBlockSparseStructure {
 public:
  // CompressedRowBlockStructure is contains a vector of CompressedLists, with
  // each CompressedList containing a vector of Cells. We precompute a flat
  // array of cells on cpu and transfer it to the gpu.
  CudaBlockSparseStructure(const CompressedRowBlockStructure& block_structure,
                           ContextImpl* context);
  // In the case of partitioned matrices, number of non-zeros in E and layout of
  // F are computed
  CudaBlockSparseStructure(const CompressedRowBlockStructure& block_structure,
                           const int num_col_blocks_e,
                           ContextImpl* context);

  int num_rows() const { return num_rows_; }
  int num_cols() const { return num_cols_; }
  int num_cells() const { return num_cells_; }
  int num_nonzeros() const { return num_nonzeros_; }
  // When partitioned matrix constructor was used, returns number of non-zeros
  // in E sub-matrix
  int num_nonzeros_e() const { return num_nonzeros_e_; }
  int num_row_blocks() const { return num_row_blocks_; }
  int num_row_blocks_e() const { return num_row_blocks_e_; }
  int num_col_blocks() const { return num_col_blocks_; }

  // Returns true if values from block-sparse matrix (F sub-matrix in
  // partitioned case) can be copied to CRS matrix as-is. This is possible if
  // each row-block is stored in CRS order:
  //  - Row-block consists of a single row
  //  - Row-block contains a single cell
  bool IsCrsCompatible() const { return is_crs_compatible_; }

  // Device pointer to array of num_row_blocks + 1 indices of the first cell of
  // row block
  const int* first_cell_in_row_block() const {
    return first_cell_in_row_block_.data();
  }
  // Device pointer to array of num_row_blocks + 1 indices of the first value in
  // this or subsequent row-blocks of submatrix F
  const int* value_offset_row_block_f() const {
    return value_offset_row_block_f_.data();
  }
  // Device pointer to array of num_cells cells, sorted by row-block
  const Cell* cells() const { return cells_.data(); }
  // Device pointer to array of row blocks
  const Block* row_blocks() const { return row_blocks_.data(); }
  // Device pointer to array of column blocks
  const Block* col_blocks() const { return col_blocks_.data(); }

 private:
  int num_rows_;
  int num_cols_;
  int num_cells_;
  int num_nonzeros_;
  int num_nonzeros_e_;
  int num_row_blocks_;
  int num_row_blocks_e_;
  int num_col_blocks_;
  bool is_crs_compatible_;
  CudaBuffer<int> first_cell_in_row_block_;
  CudaBuffer<int> value_offset_row_block_f_;
  CudaBuffer<Cell> cells_;
  CudaBuffer<Block> row_blocks_;
  CudaBuffer<Block> col_blocks_;
  friend class CudaBlockStructureTest;
};
}  // namespace ceres::internal

#endif  // CERES_NO_CUDA
#endif  // CERES_INTERNAL_CUDA_BLOCK_SPARSE_STRUCTURE_H_