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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 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
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// 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
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin)
#include "ceres/cuda_block_sparse_crs_view.h"
#include <numeric>
#include "gtest/gtest.h"
#ifndef CERES_NO_CUDA
namespace ceres::internal {
class CudaBlockSparseCRSViewTest : public ::testing::Test {
protected:
void SetUp() final {
std::string message;
ASSERT_TRUE(context_.InitCuda(&message))
<< "InitCuda() failed because: " << message;
BlockSparseMatrix::RandomMatrixOptions options;
options.num_row_blocks = 1234;
options.min_row_block_size = 1;
options.max_row_block_size = 10;
options.num_col_blocks = 567;
options.min_col_block_size = 1;
options.max_col_block_size = 10;
options.block_density = 0.2;
std::mt19937 rng;
// Block-sparse matrix with order of values different from CRS
block_sparse_non_crs_compatible_ =
BlockSparseMatrix::CreateRandomMatrix(options, rng, true);
std::iota(block_sparse_non_crs_compatible_->mutable_values(),
block_sparse_non_crs_compatible_->mutable_values() +
block_sparse_non_crs_compatible_->num_nonzeros(),
1);
options.max_row_block_size = 1;
// Block-sparse matrix with CRS order of values (row-blocks are rows)
block_sparse_crs_compatible_rows_ =
BlockSparseMatrix::CreateRandomMatrix(options, rng, true);
std::iota(block_sparse_crs_compatible_rows_->mutable_values(),
block_sparse_crs_compatible_rows_->mutable_values() +
block_sparse_crs_compatible_rows_->num_nonzeros(),
1);
// Block-sparse matrix with CRS order of values (single cell per row-block)
auto bs = std::make_unique<CompressedRowBlockStructure>(
*block_sparse_non_crs_compatible_->block_structure());
int num_nonzeros = 0;
for (auto& r : bs->rows) {
const int num_cells = r.cells.size();
if (num_cells > 1) {
std::uniform_int_distribution<int> uniform_cell(0, num_cells - 1);
const int selected_cell = uniform_cell(rng);
std::swap(r.cells[0], r.cells[selected_cell]);
r.cells.resize(1);
}
const int row_block_size = r.block.size;
for (auto& c : r.cells) {
c.position = num_nonzeros;
const int col_block_size = bs->cols[c.block_id].size;
num_nonzeros += col_block_size * row_block_size;
}
}
block_sparse_crs_compatible_single_cell_ =
std::make_unique<BlockSparseMatrix>(bs.release());
std::iota(block_sparse_crs_compatible_single_cell_->mutable_values(),
block_sparse_crs_compatible_single_cell_->mutable_values() +
block_sparse_crs_compatible_single_cell_->num_nonzeros(),
1);
}
void Compare(const BlockSparseMatrix& bsm, const CudaSparseMatrix& csm) {
ASSERT_EQ(csm.num_cols(), bsm.num_cols());
ASSERT_EQ(csm.num_rows(), bsm.num_rows());
ASSERT_EQ(csm.num_nonzeros(), bsm.num_nonzeros());
const int num_rows = bsm.num_rows();
const int num_cols = bsm.num_cols();
Vector x(num_cols);
Vector y(num_rows);
CudaVector x_cuda(&context_, num_cols);
CudaVector y_cuda(&context_, num_rows);
Vector y_cuda_host(num_rows);
for (int i = 0; i < num_cols; ++i) {
x.setZero();
y.setZero();
y_cuda.SetZero();
x[i] = 1.;
x_cuda.CopyFromCpu(x);
csm.RightMultiplyAndAccumulate(x_cuda, &y_cuda);
bsm.RightMultiplyAndAccumulate(
x.data(), y.data(), &context_, std::thread::hardware_concurrency());
y_cuda.CopyTo(&y_cuda_host);
// There will be up to 1 non-zero product per row, thus we expect an exact
// match on 32-bit integer indices
EXPECT_EQ((y - y_cuda_host).squaredNorm(), 0.);
}
}
std::unique_ptr<BlockSparseMatrix> block_sparse_non_crs_compatible_;
std::unique_ptr<BlockSparseMatrix> block_sparse_crs_compatible_rows_;
std::unique_ptr<BlockSparseMatrix> block_sparse_crs_compatible_single_cell_;
ContextImpl context_;
};
TEST_F(CudaBlockSparseCRSViewTest, CreateUpdateValuesNonCompatible) {
auto view =
CudaBlockSparseCRSView(*block_sparse_non_crs_compatible_, &context_);
ASSERT_EQ(view.IsCrsCompatible(), false);
auto matrix = view.crs_matrix();
Compare(*block_sparse_non_crs_compatible_, *matrix);
}
TEST_F(CudaBlockSparseCRSViewTest, CreateUpdateValuesCompatibleRows) {
auto view =
CudaBlockSparseCRSView(*block_sparse_crs_compatible_rows_, &context_);
ASSERT_EQ(view.IsCrsCompatible(), true);
auto matrix = view.crs_matrix();
Compare(*block_sparse_crs_compatible_rows_, *matrix);
}
TEST_F(CudaBlockSparseCRSViewTest, CreateUpdateValuesCompatibleSingleCell) {
auto view = CudaBlockSparseCRSView(*block_sparse_crs_compatible_single_cell_,
&context_);
ASSERT_EQ(view.IsCrsCompatible(), true);
auto matrix = view.crs_matrix();
Compare(*block_sparse_crs_compatible_single_cell_, *matrix);
}
} // namespace ceres::internal
#endif // CERES_NO_CUDA