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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 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: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_sparse_matrix.h"
#include <algorithm>
#include <cstddef>
#include <memory>
#include <numeric>
#include <random>
#include <vector>
#include "ceres/block_structure.h"
#include "ceres/crs_matrix.h"
#include "ceres/internal/eigen.h"
#include "ceres/parallel_for.h"
#include "ceres/parallel_vector_ops.h"
#include "ceres/small_blas.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"
#ifndef CERES_NO_CUDA
#include "cuda_runtime.h"
#endif
namespace ceres::internal {
namespace {
void ComputeCumulativeNumberOfNonZeros(std::vector<CompressedList>& rows) {
if (rows.empty()) {
return;
}
rows[0].cumulative_nnz = rows[0].nnz;
for (int c = 1; c < rows.size(); ++c) {
const int curr_nnz = rows[c].nnz;
rows[c].cumulative_nnz = curr_nnz + rows[c - 1].cumulative_nnz;
}
}
template <bool transpose>
std::unique_ptr<CompressedRowSparseMatrix>
CreateStructureOfCompressedRowSparseMatrix(
const double* values,
int num_rows,
int num_cols,
int num_nonzeros,
const CompressedRowBlockStructure* block_structure) {
auto crs_matrix = std::make_unique<CompressedRowSparseMatrix>(
num_rows, num_cols, num_nonzeros);
auto crs_cols = crs_matrix->mutable_cols();
auto crs_rows = crs_matrix->mutable_rows();
int value_offset = 0;
const int num_row_blocks = block_structure->rows.size();
const auto& cols = block_structure->cols;
*crs_rows++ = 0;
for (int row_block_id = 0; row_block_id < num_row_blocks; ++row_block_id) {
const auto& row_block = block_structure->rows[row_block_id];
// Empty row block: only requires setting row offsets
if (row_block.cells.empty()) {
std::fill(crs_rows, crs_rows + row_block.block.size, value_offset);
crs_rows += row_block.block.size;
continue;
}
int row_nnz = 0;
if constexpr (transpose) {
// Transposed block structure comes with nnz in row-block filled-in
row_nnz = row_block.nnz / row_block.block.size;
} else {
// Nnz field of non-transposed block structure is not filled and it can
// have non-sequential structure (consider the case of jacobian for
// Schur-complement solver: E and F blocks are stored separately).
for (auto& c : row_block.cells) {
row_nnz += cols[c.block_id].size;
}
}
// Row-wise setup of matrix structure
for (int row = 0; row < row_block.block.size; ++row) {
value_offset += row_nnz;
*crs_rows++ = value_offset;
for (auto& c : row_block.cells) {
const int col_block_size = cols[c.block_id].size;
const int col_position = cols[c.block_id].position;
std::iota(crs_cols, crs_cols + col_block_size, col_position);
crs_cols += col_block_size;
}
}
}
return crs_matrix;
}
template <bool transpose>
void UpdateCompressedRowSparseMatrixImpl(
CompressedRowSparseMatrix* crs_matrix,
const double* values,
const CompressedRowBlockStructure* block_structure) {
auto crs_values = crs_matrix->mutable_values();
auto crs_rows = crs_matrix->mutable_rows();
const int num_row_blocks = block_structure->rows.size();
const auto& cols = block_structure->cols;
for (int row_block_id = 0; row_block_id < num_row_blocks; ++row_block_id) {
const auto& row_block = block_structure->rows[row_block_id];
const int row_block_size = row_block.block.size;
const int row_nnz = crs_rows[1] - crs_rows[0];
crs_rows += row_block_size;
if (row_nnz == 0) {
continue;
}
MatrixRef crs_row_block(crs_values, row_block_size, row_nnz);
int col_offset = 0;
for (auto& c : row_block.cells) {
const int col_block_size = cols[c.block_id].size;
auto crs_cell =
crs_row_block.block(0, col_offset, row_block_size, col_block_size);
if constexpr (transpose) {
// Transposed matrix is filled using transposed block-strucutre
ConstMatrixRef cell(
values + c.position, col_block_size, row_block_size);
crs_cell = cell.transpose();
} else {
ConstMatrixRef cell(
values + c.position, row_block_size, col_block_size);
crs_cell = cell;
}
col_offset += col_block_size;
}
crs_values += row_nnz * row_block_size;
}
}
void SetBlockStructureOfCompressedRowSparseMatrix(
CompressedRowSparseMatrix* crs_matrix,
CompressedRowBlockStructure* block_structure) {
const int num_row_blocks = block_structure->rows.size();
auto& row_blocks = *crs_matrix->mutable_row_blocks();
row_blocks.resize(num_row_blocks);
for (int i = 0; i < num_row_blocks; ++i) {
row_blocks[i] = block_structure->rows[i].block;
}
auto& col_blocks = *crs_matrix->mutable_col_blocks();
col_blocks = block_structure->cols;
}
} // namespace
BlockSparseMatrix::BlockSparseMatrix(
CompressedRowBlockStructure* block_structure, bool use_page_locked_memory)
: use_page_locked_memory_(use_page_locked_memory),
num_rows_(0),
num_cols_(0),
num_nonzeros_(0),
block_structure_(block_structure) {
CHECK(block_structure_ != nullptr);
// Count the number of columns in the matrix.
for (auto& col : block_structure_->cols) {
num_cols_ += col.size;
}
// Count the number of non-zero entries and the number of rows in
// the matrix.
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_size = block_structure_->rows[i].block.size;
num_rows_ += row_block_size;
const std::vector<Cell>& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
int col_block_id = cell.block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
num_nonzeros_ += col_block_size * row_block_size;
}
}
CHECK_GE(num_rows_, 0);
CHECK_GE(num_cols_, 0);
CHECK_GE(num_nonzeros_, 0);
VLOG(2) << "Allocating values array with " << num_nonzeros_ * sizeof(double)
<< " bytes."; // NOLINT
values_ = AllocateValues(num_nonzeros_);
max_num_nonzeros_ = num_nonzeros_;
CHECK(values_ != nullptr);
AddTransposeBlockStructure();
}
BlockSparseMatrix::~BlockSparseMatrix() { FreeValues(values_); }
void BlockSparseMatrix::AddTransposeBlockStructure() {
if (transpose_block_structure_ == nullptr) {
transpose_block_structure_ = CreateTranspose(*block_structure_);
}
}
void BlockSparseMatrix::SetZero() {
std::fill(values_, values_ + num_nonzeros_, 0.0);
}
void BlockSparseMatrix::SetZero(ContextImpl* context, int num_threads) {
ParallelSetZero(context, num_threads, values_, num_nonzeros_);
}
void BlockSparseMatrix::RightMultiplyAndAccumulate(const double* x,
double* y) const {
RightMultiplyAndAccumulate(x, y, nullptr, 1);
}
void BlockSparseMatrix::RightMultiplyAndAccumulate(const double* x,
double* y,
ContextImpl* context,
int num_threads) const {
CHECK(x != nullptr);
CHECK(y != nullptr);
const auto values = values_;
const auto block_structure = block_structure_.get();
const auto num_row_blocks = block_structure->rows.size();
ParallelFor(context,
0,
num_row_blocks,
num_threads,
[values, block_structure, x, y](int row_block_id) {
const int row_block_pos =
block_structure->rows[row_block_id].block.position;
const int row_block_size =
block_structure->rows[row_block_id].block.size;
const auto& cells = block_structure->rows[row_block_id].cells;
for (const auto& cell : cells) {
const int col_block_id = cell.block_id;
const int col_block_size =
block_structure->cols[col_block_id].size;
const int col_block_pos =
block_structure->cols[col_block_id].position;
MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values + cell.position,
row_block_size,
col_block_size,
x + col_block_pos,
y + row_block_pos);
}
});
}
// TODO(https://github.com/ceres-solver/ceres-solver/issues/933): This method
// might benefit from caching column-block partition
void BlockSparseMatrix::LeftMultiplyAndAccumulate(const double* x,
double* y,
ContextImpl* context,
int num_threads) const {
// While utilizing transposed structure allows to perform parallel
// left-multiplication by dense vector, it makes access patterns to matrix
// elements scattered. Thus, multiplication using transposed structure
// is only useful for parallel execution
CHECK(x != nullptr);
CHECK(y != nullptr);
if (transpose_block_structure_ == nullptr || num_threads == 1) {
LeftMultiplyAndAccumulate(x, y);
return;
}
auto transpose_bs = transpose_block_structure_.get();
const auto values = values_;
const int num_col_blocks = transpose_bs->rows.size();
if (!num_col_blocks) {
return;
}
// Use non-zero count as iteration cost for guided parallel-for loop
ParallelFor(
context,
0,
num_col_blocks,
num_threads,
[values, transpose_bs, x, y](int row_block_id) {
int row_block_pos = transpose_bs->rows[row_block_id].block.position;
int row_block_size = transpose_bs->rows[row_block_id].block.size;
auto& cells = transpose_bs->rows[row_block_id].cells;
for (auto& cell : cells) {
const int col_block_id = cell.block_id;
const int col_block_size = transpose_bs->cols[col_block_id].size;
const int col_block_pos = transpose_bs->cols[col_block_id].position;
MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values + cell.position,
col_block_size,
row_block_size,
x + col_block_pos,
y + row_block_pos);
}
},
transpose_bs->rows.data(),
[](const CompressedRow& row) { return row.cumulative_nnz; });
}
void BlockSparseMatrix::LeftMultiplyAndAccumulate(const double* x,
double* y) const {
CHECK(x != nullptr);
CHECK(y != nullptr);
// Single-threaded left products are always computed using a non-transpose
// block structure, because it has linear acess pattern to matrix elements
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
const auto& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
int col_block_id = cell.block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
values_ + cell.position,
row_block_size,
col_block_size,
x + row_block_pos,
y + col_block_pos);
}
}
}
void BlockSparseMatrix::SquaredColumnNorm(double* x) const {
CHECK(x != nullptr);
VectorRef(x, num_cols_).setZero();
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_size = block_structure_->rows[i].block.size;
auto& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
int col_block_id = cell.block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
const MatrixRef m(
values_ + cell.position, row_block_size, col_block_size);
VectorRef(x + col_block_pos, col_block_size) += m.colwise().squaredNorm();
}
}
}
// TODO(https://github.com/ceres-solver/ceres-solver/issues/933): This method
// might benefit from caching column-block partition
void BlockSparseMatrix::SquaredColumnNorm(double* x,
ContextImpl* context,
int num_threads) const {
if (transpose_block_structure_ == nullptr || num_threads == 1) {
SquaredColumnNorm(x);
return;
}
CHECK(x != nullptr);
ParallelSetZero(context, num_threads, x, num_cols_);
auto transpose_bs = transpose_block_structure_.get();
const auto values = values_;
const int num_col_blocks = transpose_bs->rows.size();
ParallelFor(
context,
0,
num_col_blocks,
num_threads,
[values, transpose_bs, x](int row_block_id) {
const auto& row = transpose_bs->rows[row_block_id];
for (auto& cell : row.cells) {
const auto& col = transpose_bs->cols[cell.block_id];
const MatrixRef m(values + cell.position, col.size, row.block.size);
VectorRef(x + row.block.position, row.block.size) +=
m.colwise().squaredNorm();
}
},
transpose_bs->rows.data(),
[](const CompressedRow& row) { return row.cumulative_nnz; });
}
void BlockSparseMatrix::ScaleColumns(const double* scale) {
CHECK(scale != nullptr);
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_size = block_structure_->rows[i].block.size;
auto& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
int col_block_id = cell.block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
MatrixRef m(values_ + cell.position, row_block_size, col_block_size);
m *= ConstVectorRef(scale + col_block_pos, col_block_size).asDiagonal();
}
}
}
// TODO(https://github.com/ceres-solver/ceres-solver/issues/933): This method
// might benefit from caching column-block partition
void BlockSparseMatrix::ScaleColumns(const double* scale,
ContextImpl* context,
int num_threads) {
if (transpose_block_structure_ == nullptr || num_threads == 1) {
ScaleColumns(scale);
return;
}
CHECK(scale != nullptr);
auto transpose_bs = transpose_block_structure_.get();
auto values = values_;
const int num_col_blocks = transpose_bs->rows.size();
ParallelFor(
context,
0,
num_col_blocks,
num_threads,
[values, transpose_bs, scale](int row_block_id) {
const auto& row = transpose_bs->rows[row_block_id];
for (auto& cell : row.cells) {
const auto& col = transpose_bs->cols[cell.block_id];
MatrixRef m(values + cell.position, col.size, row.block.size);
m *= ConstVectorRef(scale + row.block.position, row.block.size)
.asDiagonal();
}
},
transpose_bs->rows.data(),
[](const CompressedRow& row) { return row.cumulative_nnz; });
}
std::unique_ptr<CompressedRowSparseMatrix>
BlockSparseMatrix::ToCompressedRowSparseMatrixTranspose() const {
auto bs = transpose_block_structure_.get();
auto crs_matrix = CreateStructureOfCompressedRowSparseMatrix<true>(
values(), num_cols_, num_rows_, num_nonzeros_, bs);
SetBlockStructureOfCompressedRowSparseMatrix(crs_matrix.get(), bs);
UpdateCompressedRowSparseMatrixTranspose(crs_matrix.get());
return crs_matrix;
}
std::unique_ptr<CompressedRowSparseMatrix>
BlockSparseMatrix::ToCompressedRowSparseMatrix() const {
auto crs_matrix = CreateStructureOfCompressedRowSparseMatrix<false>(
values(), num_rows_, num_cols_, num_nonzeros_, block_structure_.get());
SetBlockStructureOfCompressedRowSparseMatrix(crs_matrix.get(),
block_structure_.get());
UpdateCompressedRowSparseMatrix(crs_matrix.get());
return crs_matrix;
}
void BlockSparseMatrix::UpdateCompressedRowSparseMatrixTranspose(
CompressedRowSparseMatrix* crs_matrix) const {
CHECK(crs_matrix != nullptr);
CHECK_EQ(crs_matrix->num_rows(), num_cols_);
CHECK_EQ(crs_matrix->num_cols(), num_rows_);
CHECK_EQ(crs_matrix->num_nonzeros(), num_nonzeros_);
UpdateCompressedRowSparseMatrixImpl<true>(
crs_matrix, values(), transpose_block_structure_.get());
}
void BlockSparseMatrix::UpdateCompressedRowSparseMatrix(
CompressedRowSparseMatrix* crs_matrix) const {
CHECK(crs_matrix != nullptr);
CHECK_EQ(crs_matrix->num_rows(), num_rows_);
CHECK_EQ(crs_matrix->num_cols(), num_cols_);
CHECK_EQ(crs_matrix->num_nonzeros(), num_nonzeros_);
UpdateCompressedRowSparseMatrixImpl<false>(
crs_matrix, values(), block_structure_.get());
}
void BlockSparseMatrix::ToDenseMatrix(Matrix* dense_matrix) const {
CHECK(dense_matrix != nullptr);
dense_matrix->resize(num_rows_, num_cols_);
dense_matrix->setZero();
Matrix& m = *dense_matrix;
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
auto& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
int col_block_id = cell.block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
int jac_pos = cell.position;
m.block(row_block_pos, col_block_pos, row_block_size, col_block_size) +=
MatrixRef(values_ + jac_pos, row_block_size, col_block_size);
}
}
}
void BlockSparseMatrix::ToTripletSparseMatrix(
TripletSparseMatrix* matrix) const {
CHECK(matrix != nullptr);
matrix->Reserve(num_nonzeros_);
matrix->Resize(num_rows_, num_cols_);
matrix->SetZero();
for (int i = 0; i < block_structure_->rows.size(); ++i) {
int row_block_pos = block_structure_->rows[i].block.position;
int row_block_size = block_structure_->rows[i].block.size;
const auto& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
int col_block_id = cell.block_id;
int col_block_size = block_structure_->cols[col_block_id].size;
int col_block_pos = block_structure_->cols[col_block_id].position;
int jac_pos = cell.position;
for (int r = 0; r < row_block_size; ++r) {
for (int c = 0; c < col_block_size; ++c, ++jac_pos) {
matrix->mutable_rows()[jac_pos] = row_block_pos + r;
matrix->mutable_cols()[jac_pos] = col_block_pos + c;
matrix->mutable_values()[jac_pos] = values_[jac_pos];
}
}
}
}
matrix->set_num_nonzeros(num_nonzeros_);
}
// Return a pointer to the block structure. We continue to hold
// ownership of the object though.
const CompressedRowBlockStructure* BlockSparseMatrix::block_structure() const {
return block_structure_.get();
}
// Return a pointer to the block structure of matrix transpose. We continue to
// hold ownership of the object though.
const CompressedRowBlockStructure*
BlockSparseMatrix::transpose_block_structure() const {
return transpose_block_structure_.get();
}
void BlockSparseMatrix::ToTextFile(FILE* file) const {
CHECK(file != nullptr);
for (int i = 0; i < block_structure_->rows.size(); ++i) {
const int row_block_pos = block_structure_->rows[i].block.position;
const int row_block_size = block_structure_->rows[i].block.size;
const auto& cells = block_structure_->rows[i].cells;
for (const auto& cell : cells) {
const int col_block_id = cell.block_id;
const int col_block_size = block_structure_->cols[col_block_id].size;
const int col_block_pos = block_structure_->cols[col_block_id].position;
int jac_pos = cell.position;
for (int r = 0; r < row_block_size; ++r) {
for (int c = 0; c < col_block_size; ++c) {
fprintf(file,
"% 10d % 10d %17f\n",
row_block_pos + r,
col_block_pos + c,
values_[jac_pos++]);
}
}
}
}
}
std::unique_ptr<BlockSparseMatrix> BlockSparseMatrix::CreateDiagonalMatrix(
const double* diagonal, const std::vector<Block>& column_blocks) {
// Create the block structure for the diagonal matrix.
auto* bs = new CompressedRowBlockStructure();
bs->cols = column_blocks;
int position = 0;
bs->rows.resize(column_blocks.size(), CompressedRow(1));
for (int i = 0; i < column_blocks.size(); ++i) {
CompressedRow& row = bs->rows[i];
row.block = column_blocks[i];
Cell& cell = row.cells[0];
cell.block_id = i;
cell.position = position;
position += row.block.size * row.block.size;
}
// Create the BlockSparseMatrix with the given block structure.
auto matrix = std::make_unique<BlockSparseMatrix>(bs);
matrix->SetZero();
// Fill the values array of the block sparse matrix.
double* values = matrix->mutable_values();
for (const auto& column_block : column_blocks) {
const int size = column_block.size;
for (int j = 0; j < size; ++j) {
// (j + 1) * size is compact way of accessing the (j,j) entry.
values[j * (size + 1)] = diagonal[j];
}
diagonal += size;
values += size * size;
}
return matrix;
}
void BlockSparseMatrix::AppendRows(const BlockSparseMatrix& m) {
CHECK_EQ(m.num_cols(), num_cols());
const CompressedRowBlockStructure* m_bs = m.block_structure();
CHECK_EQ(m_bs->cols.size(), block_structure_->cols.size());
const int old_num_nonzeros = num_nonzeros_;
const int old_num_row_blocks = block_structure_->rows.size();
block_structure_->rows.resize(old_num_row_blocks + m_bs->rows.size());
for (int i = 0; i < m_bs->rows.size(); ++i) {
const CompressedRow& m_row = m_bs->rows[i];
const int row_block_id = old_num_row_blocks + i;
CompressedRow& row = block_structure_->rows[row_block_id];
row.block.size = m_row.block.size;
row.block.position = num_rows_;
num_rows_ += m_row.block.size;
row.cells.resize(m_row.cells.size());
if (transpose_block_structure_) {
transpose_block_structure_->cols.emplace_back(row.block);
}
for (int c = 0; c < m_row.cells.size(); ++c) {
const int block_id = m_row.cells[c].block_id;
row.cells[c].block_id = block_id;
row.cells[c].position = num_nonzeros_;
const int cell_nnz = m_row.block.size * m_bs->cols[block_id].size;
if (transpose_block_structure_) {
transpose_block_structure_->rows[block_id].cells.emplace_back(
row_block_id, num_nonzeros_);
transpose_block_structure_->rows[block_id].nnz += cell_nnz;
}
num_nonzeros_ += cell_nnz;
}
}
if (num_nonzeros_ > max_num_nonzeros_) {
double* old_values = values_;
values_ = AllocateValues(num_nonzeros_);
std::copy_n(old_values, old_num_nonzeros, values_);
max_num_nonzeros_ = num_nonzeros_;
FreeValues(old_values);
}
std::copy(
m.values(), m.values() + m.num_nonzeros(), values_ + old_num_nonzeros);
if (transpose_block_structure_ == nullptr) {
return;
}
ComputeCumulativeNumberOfNonZeros(transpose_block_structure_->rows);
}
void BlockSparseMatrix::DeleteRowBlocks(const int delta_row_blocks) {
const int num_row_blocks = block_structure_->rows.size();
const int new_num_row_blocks = num_row_blocks - delta_row_blocks;
int delta_num_nonzeros = 0;
int delta_num_rows = 0;
const std::vector<Block>& column_blocks = block_structure_->cols;
for (int i = 0; i < delta_row_blocks; ++i) {
const CompressedRow& row = block_structure_->rows[num_row_blocks - i - 1];
delta_num_rows += row.block.size;
for (int c = 0; c < row.cells.size(); ++c) {
const Cell& cell = row.cells[c];
delta_num_nonzeros += row.block.size * column_blocks[cell.block_id].size;
if (transpose_block_structure_) {
auto& col_cells = transpose_block_structure_->rows[cell.block_id].cells;
while (!col_cells.empty() &&
col_cells.back().block_id >= new_num_row_blocks) {
const int del_block_id = col_cells.back().block_id;
const int del_block_rows =
block_structure_->rows[del_block_id].block.size;
const int del_block_cols = column_blocks[cell.block_id].size;
const int del_cell_nnz = del_block_rows * del_block_cols;
transpose_block_structure_->rows[cell.block_id].nnz -= del_cell_nnz;
col_cells.pop_back();
}
}
}
}
num_nonzeros_ -= delta_num_nonzeros;
num_rows_ -= delta_num_rows;
block_structure_->rows.resize(new_num_row_blocks);
if (transpose_block_structure_ == nullptr) {
return;
}
for (int i = 0; i < delta_row_blocks; ++i) {
transpose_block_structure_->cols.pop_back();
}
ComputeCumulativeNumberOfNonZeros(transpose_block_structure_->rows);
}
std::unique_ptr<BlockSparseMatrix> BlockSparseMatrix::CreateRandomMatrix(
const BlockSparseMatrix::RandomMatrixOptions& options,
std::mt19937& prng,
bool use_page_locked_memory) {
CHECK_GT(options.num_row_blocks, 0);
CHECK_GT(options.min_row_block_size, 0);
CHECK_GT(options.max_row_block_size, 0);
CHECK_LE(options.min_row_block_size, options.max_row_block_size);
CHECK_GT(options.block_density, 0.0);
CHECK_LE(options.block_density, 1.0);
std::uniform_int_distribution<int> col_distribution(
options.min_col_block_size, options.max_col_block_size);
std::uniform_int_distribution<int> row_distribution(
options.min_row_block_size, options.max_row_block_size);
auto bs = std::make_unique<CompressedRowBlockStructure>();
if (options.col_blocks.empty()) {
CHECK_GT(options.num_col_blocks, 0);
CHECK_GT(options.min_col_block_size, 0);
CHECK_GT(options.max_col_block_size, 0);
CHECK_LE(options.min_col_block_size, options.max_col_block_size);
// Generate the col block structure.
int col_block_position = 0;
for (int i = 0; i < options.num_col_blocks; ++i) {
const int col_block_size = col_distribution(prng);
bs->cols.emplace_back(col_block_size, col_block_position);
col_block_position += col_block_size;
}
} else {
bs->cols = options.col_blocks;
}
bool matrix_has_blocks = false;
std::uniform_real_distribution<double> uniform01(0.0, 1.0);
while (!matrix_has_blocks) {
VLOG(1) << "Clearing";
bs->rows.clear();
int row_block_position = 0;
int value_position = 0;
for (int r = 0; r < options.num_row_blocks; ++r) {
const int row_block_size = row_distribution(prng);
bs->rows.emplace_back();
CompressedRow& row = bs->rows.back();
row.block.size = row_block_size;
row.block.position = row_block_position;
row_block_position += row_block_size;
for (int c = 0; c < bs->cols.size(); ++c) {
if (uniform01(prng) > options.block_density) continue;
row.cells.emplace_back();
Cell& cell = row.cells.back();
cell.block_id = c;
cell.position = value_position;
value_position += row_block_size * bs->cols[c].size;
matrix_has_blocks = true;
}
}
}
auto matrix =
std::make_unique<BlockSparseMatrix>(bs.release(), use_page_locked_memory);
double* values = matrix->mutable_values();
std::normal_distribution<double> standard_normal_distribution;
std::generate_n(
values, matrix->num_nonzeros(), [&standard_normal_distribution, &prng] {
return standard_normal_distribution(prng);
});
return matrix;
}
std::unique_ptr<CompressedRowBlockStructure> CreateTranspose(
const CompressedRowBlockStructure& bs) {
auto transpose = std::make_unique<CompressedRowBlockStructure>();
transpose->rows.resize(bs.cols.size());
for (int i = 0; i < bs.cols.size(); ++i) {
transpose->rows[i].block = bs.cols[i];
transpose->rows[i].nnz = 0;
}
transpose->cols.resize(bs.rows.size());
for (int i = 0; i < bs.rows.size(); ++i) {
auto& row = bs.rows[i];
transpose->cols[i] = row.block;
const int nrows = row.block.size;
for (auto& cell : row.cells) {
transpose->rows[cell.block_id].cells.emplace_back(i, cell.position);
const int ncols = transpose->rows[cell.block_id].block.size;
transpose->rows[cell.block_id].nnz += nrows * ncols;
}
}
ComputeCumulativeNumberOfNonZeros(transpose->rows);
return transpose;
}
double* BlockSparseMatrix::AllocateValues(int size) {
if (!use_page_locked_memory_) {
return new double[size];
}
#ifndef CERES_NO_CUDA
double* values = nullptr;
CHECK_EQ(cudaSuccess,
cudaHostAlloc(&values, sizeof(double) * size, cudaHostAllocDefault));
return values;
#else
LOG(FATAL) << "Page locked memory requested when CUDA is not available. "
<< "This is a Ceres bug; please contact the developers!";
return nullptr;
#endif
};
void BlockSparseMatrix::FreeValues(double*& values) {
if (!use_page_locked_memory_) {
delete[] values;
values = nullptr;
return;
}
#ifndef CERES_NO_CUDA
CHECK_EQ(cudaSuccess, cudaFreeHost(values));
#else
LOG(FATAL) << "Page locked memory requested when CUDA is not available. "
<< "This is a Ceres bug; please contact the developers!";
#endif
values = nullptr;
};
} // namespace ceres::internal