internal/ceres/cuda_block_sparse_crs_view_test.cc

// Ceres Solver - A fast non-linear least squares minimizer
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// http://ceres-solver.org/
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// Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin)

#include "ceres/cuda_block_sparse_crs_view.h"

#include <glog/logging.h>
#include <gtest/gtest.h>

#ifndef CERES_NO_CUDA

namespace ceres::internal {
class CudaBlockSparseCRSViewTest : public ::testing::Test {
 protected:
  void SetUp() final {
    std::string message;
    CHECK(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;
    A_ = BlockSparseMatrix::CreateRandomMatrix(options, rng);
    std::iota(
        A_->mutable_values(), A_->mutable_values() + A_->num_nonzeros(), 1);
  }

  std::unique_ptr<BlockSparseMatrix> A_;
  ContextImpl context_;
};

TEST_F(CudaBlockSparseCRSViewTest, CreateUpdateValues) {
  auto view = CudaBlockSparseCRSView(*A_, &context_);

  // TODO: we definitely would like to use crs_matrix() here, but
  // CudaSparseMatrix::RightMultiplyAndAccumulate is defined non-const because
  // it might allocate additional storage by request of cuSPARSE
  auto matrix = view.mutable_crs_matrix();
  ASSERT_EQ(matrix->num_cols(), A_->num_cols());
  ASSERT_EQ(matrix->num_rows(), A_->num_rows());
  ASSERT_EQ(matrix->num_nonzeros(), A_->num_nonzeros());

  const int num_rows = A_->num_rows();
  const int num_cols = A_->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);
    A_->RightMultiplyAndAccumulate(
        x.data(), y.data(), &context_, std::thread::hardware_concurrency());
    matrix->RightMultiplyAndAccumulate(x_cuda, &y_cuda);
    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.);
  }
}
}  // namespace ceres::internal

#endif  // CERES_NO_CUDA