// 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/dense_cholesky.h"

#include <algorithm>
#include <memory>
#include <string>
#include <vector>

#ifndef CERES_NO_CUDA
#include "cuda_runtime.h"
#include "cusolverDn.h"
#endif  // CERES_NO_CUDA

#ifndef CERES_NO_LAPACK

// C interface to the LAPACK Cholesky factorization and triangular solve.
extern "C" void dpotrf_(
    const char* uplo, const int* n, double* a, const int* lda, int* info);

extern "C" void dpotrs_(const char* uplo,
                        const int* n,
                        const int* nrhs,
                        const double* a,
                        const int* lda,
                        double* b,
                        const int* ldb,
                        int* info);
#endif

namespace ceres {
namespace internal {

DenseCholesky::~DenseCholesky() = default;

std::unique_ptr<DenseCholesky> DenseCholesky::Create(
    const LinearSolver::Options& options) {
  std::unique_ptr<DenseCholesky> dense_cholesky;

  switch (options.dense_linear_algebra_library_type) {
    case EIGEN:
      dense_cholesky = std::make_unique<EigenDenseCholesky>();
      break;

    case LAPACK:
#ifndef CERES_NO_LAPACK
      dense_cholesky = std::make_unique<LAPACKDenseCholesky>();
      break;
#else
      LOG(FATAL) << "Ceres was compiled without support for LAPACK.";
#endif

    case CUDA:
#ifndef CERES_NO_CUDA
      dense_cholesky = CUDADenseCholesky::Create(options);
      break;
#else
      LOG(FATAL) << "Ceres was compiled without support for CUDA.";
#endif

    default:
      LOG(FATAL) << "Unknown dense linear algebra library type : "
                 << DenseLinearAlgebraLibraryTypeToString(
                        options.dense_linear_algebra_library_type);
  }
  return dense_cholesky;
}

LinearSolverTerminationType DenseCholesky::FactorAndSolve(
    int num_cols,
    double* lhs,
    const double* rhs,
    double* solution,
    std::string* message) {
  LinearSolverTerminationType termination_type =
      Factorize(num_cols, lhs, message);
  if (termination_type == LINEAR_SOLVER_SUCCESS) {
    termination_type = Solve(rhs, solution, message);
  }
  return termination_type;
}

LinearSolverTerminationType EigenDenseCholesky::Factorize(
    int num_cols, double* lhs, std::string* message) {
  Eigen::Map<Eigen::MatrixXd> m(lhs, num_cols, num_cols);
  llt_ = std::make_unique<LLTType>(m);
  if (llt_->info() != Eigen::Success) {
    *message = "Eigen failure. Unable to perform dense Cholesky factorization.";
    return LINEAR_SOLVER_FAILURE;
  }

  *message = "Success.";
  return LINEAR_SOLVER_SUCCESS;
}

LinearSolverTerminationType EigenDenseCholesky::Solve(const double* rhs,
                                                      double* solution,
                                                      std::string* message) {
  if (llt_->info() != Eigen::Success) {
    *message = "Eigen failure. Unable to perform dense Cholesky factorization.";
    return LINEAR_SOLVER_FAILURE;
  }

  VectorRef(solution, llt_->cols()) =
      llt_->solve(ConstVectorRef(rhs, llt_->cols()));
  *message = "Success.";
  return LINEAR_SOLVER_SUCCESS;
}

#ifndef CERES_NO_LAPACK
LinearSolverTerminationType LAPACKDenseCholesky::Factorize(
    int num_cols, double* lhs, std::string* message) {
  lhs_ = lhs;
  num_cols_ = num_cols;

  const char uplo = 'L';
  int info = 0;
  dpotrf_(&uplo, &num_cols_, lhs_, &num_cols_, &info);

  if (info < 0) {
    termination_type_ = LINEAR_SOLVER_FATAL_ERROR;
    LOG(FATAL) << "Congratulations, you found a bug in Ceres. "
               << "Please report it. "
               << "LAPACK::dpotrf fatal error. "
               << "Argument: " << -info << " is invalid.";
  } else if (info > 0) {
    termination_type_ = LINEAR_SOLVER_FAILURE;
    *message = StringPrintf(
        "LAPACK::dpotrf numerical failure. "
        "The leading minor of order %d is not positive definite.",
        info);
  } else {
    termination_type_ = LINEAR_SOLVER_SUCCESS;
    *message = "Success.";
  }
  return termination_type_;
}

LinearSolverTerminationType LAPACKDenseCholesky::Solve(const double* rhs,
                                                       double* solution,
                                                       std::string* message) {
  const char uplo = 'L';
  const int nrhs = 1;
  int info = 0;

  std::copy_n(rhs, num_cols_, solution);
  dpotrs_(
      &uplo, &num_cols_, &nrhs, lhs_, &num_cols_, solution, &num_cols_, &info);

  if (info < 0) {
    termination_type_ = LINEAR_SOLVER_FATAL_ERROR;
    LOG(FATAL) << "Congratulations, you found a bug in Ceres. "
               << "Please report it. "
               << "LAPACK::dpotrs fatal error. "
               << "Argument: " << -info << " is invalid.";
  }

  *message = "Success";
  termination_type_ = LINEAR_SOLVER_SUCCESS;

  return termination_type_;
}

#endif  // CERES_NO_LAPACK

#ifndef CERES_NO_CUDA

bool CUDADenseCholesky32Bit::Init(std::string* message) {
  if (cusolverDnCreate(&cusolver_handle_) != CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnCreate failed.";
    return false;
  }
  if (cudaStreamCreate(&stream_) != cudaSuccess) {
    *message = "cuSolverDN::cudaStreamCreate failed.";
    cusolverDnDestroy(cusolver_handle_);
    return false;
  }
  if (cusolverDnSetStream(cusolver_handle_, stream_) !=
      CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnSetStream failed.";
    cudaStreamDestroy(stream_);
    cusolverDnDestroy(cusolver_handle_);
    return false;
  }
  error_.Reserve(1);
  *message = "CUDADenseCholesky64Bit::Init Success.";
  return true;
}

CUDADenseCholesky32Bit::~CUDADenseCholesky32Bit() {
  if (cusolver_handle_ != nullptr) {
    CHECK_EQ(cusolverDnDestroy(cusolver_handle_), CUSOLVER_STATUS_SUCCESS);
    CHECK_EQ(cudaStreamDestroy(stream_), cudaSuccess);
  }
}

LinearSolverTerminationType CUDADenseCholesky32Bit::Factorize(
    int num_cols, double* lhs, std::string* message) {
  factorize_result_ = LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  // Allocate GPU memory if necessary.
  lhs_.Reserve(num_cols * num_cols);
  num_cols_ = num_cols;
  // Copy A to GPU.
  lhs_.CopyToGpu(lhs, num_cols * num_cols);
  // Allocate scratch space on GPU.
  int device_workspace_size = 0;
  if (cusolverDnDpotrf_bufferSize(cusolver_handle_,
                                  CUBLAS_FILL_MODE_LOWER,
                                  num_cols,
                                  lhs_.data(),
                                  num_cols,
                                  &device_workspace_size) !=
      CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnDpotrf_bufferSize failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  // Allocate GPU scratch memory.
  device_workspace_.Reserve(device_workspace_size);
  // Run the actual factorization (potrf)
  if (cusolverDnDpotrf(cusolver_handle_,
                       CUBLAS_FILL_MODE_LOWER,
                       num_cols,
                       lhs_.data(),
                       num_cols,
                       reinterpret_cast<double*>(device_workspace_.data()),
                       device_workspace_.size(),
                       error_.data()) != CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnDpotrf failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  if (cudaDeviceSynchronize() != cudaSuccess || cudaStreamSynchronize(stream_),
      cudaSuccess) {
    *message = "Cuda device synchronization failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  int error = 0;
  // Check for errors.
  error_.CopyToHost(&error, 1);
  if (error < 0) {
    LOG(FATAL) << "Congratulations, you found a bug in Ceres - "
               << "please report it. "
               << "cuSolverDN::cusolverDnXpotrf fatal error. "
               << "Argument: " << -error << " is invalid.";
    // The following line is unreachable, but return failure just to be
    // pedantic, since the compiler does not know that.
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  } else if (error > 0) {
    *message = StringPrintf(
        "cuSolverDN::cusolverDnDpotrf numerical failure. "
        "The leading minor of order %d is not positive definite.",
        error);
    factorize_result_ = LinearSolverTerminationType::LINEAR_SOLVER_FAILURE;
    return LinearSolverTerminationType::LINEAR_SOLVER_FAILURE;
  }
  *message = "Success";
  factorize_result_ = LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS;
  return LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS;
}

LinearSolverTerminationType CUDADenseCholesky32Bit::Solve(
    const double* rhs, double* solution, std::string* message) {
  if (factorize_result_ != LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS) {
    *message = "Factorize did not complete succesfully previously.";
    return factorize_result_;
  }
  // Copy RHS to GPU.
  rhs_.CopyToGpu(rhs, num_cols_);
  // Solve the system.
  if (cusolverDnDpotrs(cusolver_handle_,
                       CUBLAS_FILL_MODE_LOWER,
                       num_cols_,
                       1,
                       lhs_.data(),
                       num_cols_,
                       rhs_.data(),
                       num_cols_,
                       error_.data()) != CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnDpotrs failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  if (cudaDeviceSynchronize() != cudaSuccess || cudaStreamSynchronize(stream_),
      cudaSuccess) {
    *message = "Cuda device synchronization failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  // Check for errors.
  int error = 0;
  // Copy X from GPU to host.
  rhs_.CopyToHost(solution, num_cols_);
  if (error != 0) {
    LOG(FATAL) << "Congratulations, you found a bug in Ceres. "
               << "Please report it."
               << "cuSolverDN::cusolverDnDpotrs fatal error. "
               << "Argument: " << -error << " is invalid.";
  }
  // Copy error variable from GPU to host.
  error_.CopyToHost(&error, 1);
  *message = "Success";
  return LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS;
}

std::unique_ptr<CUDADenseCholesky32Bit> CUDADenseCholesky32Bit::Create(
    const LinearSolver::Options& options) {
  if (options.dense_linear_algebra_library_type != CUDA) {
    // The user called the wrong factory method.
    return nullptr;
  }
  CUDADenseCholesky32Bit* cuda_dense_cholesky = new CUDADenseCholesky32Bit();
  std::string cuda_error;
  if (cuda_dense_cholesky->Init(&cuda_error)) {
    return std::unique_ptr<CUDADenseCholesky32Bit>(cuda_dense_cholesky);
  }
  // Initialization failed, destroy the object and return a nullptr.
  delete cuda_dense_cholesky;
  LOG(ERROR) << "CUDADenseCholesky32Bit::Init failed: " << cuda_error;
  return std::unique_ptr<CUDADenseCholesky32Bit>(nullptr);
}

std::unique_ptr<CUDADenseCholesky64Bit> CUDADenseCholesky64Bit::Create(
    const LinearSolver::Options& options) {
  if (options.dense_linear_algebra_library_type != CUDA) {
    // The user called the wrong factory method.
    return nullptr;
  }
  CUDADenseCholesky64Bit* cuda_dense_cholesky = new CUDADenseCholesky64Bit();
  std::string cuda_error;
  if (cuda_dense_cholesky->Init(&cuda_error)) {
    return std::unique_ptr<CUDADenseCholesky64Bit>(cuda_dense_cholesky);
  }
  // Initialization failed, destroy the object and return a nullptr.
  delete cuda_dense_cholesky;
  LOG(ERROR) << "CUDADenseCholesky64Bit::Init failed: " << cuda_error;
  return std::unique_ptr<CUDADenseCholesky64Bit>(nullptr);
}

#ifdef CERES_CUDA_NO_64BIT_SOLVER_API
// CUDA < 11.1 did not have the 64-bit APIs, so the implementation of the new
// interface will just generate a fatal error.

CUDADenseCholesky64Bit::~CUDADenseCholesky64Bit() {}

bool CUDADenseCholesky64Bit::Init(std::string* message) {
  *message = "Cannot use CUDADenseCholesky64Bit with CUDA < 11.1.";
  return false;
}

LinearSolverTerminationType CUDADenseCholesky64Bit::Factorize(int,
                                                              double*,
                                                              std::string*) {
  return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
}

LinearSolverTerminationType CUDADenseCholesky64Bit::Solve(const double*,
                                                          double*,
                                                          std::string*) {
  return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
}

#else  // CERES_CUDA_NO_64BIT_SOLVER_API

bool CUDADenseCholesky64Bit::Init(std::string* message) {
  if (cusolverDnCreate(&cusolver_handle_) != CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnCreate failed.";
    return false;
  }
  if (cudaStreamCreate(&stream_) != cudaSuccess) {
    *message = "cuSolverDN::cudaStreamCreate failed.";
    cusolverDnDestroy(cusolver_handle_);
    return false;
  }
  if (cusolverDnSetStream(cusolver_handle_, stream_) !=
      CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnSetStream failed.";
    cudaStreamDestroy(stream_);
    cusolverDnDestroy(cusolver_handle_);
    return false;
  }
  error_.Reserve(1);
  *message = "CUDADenseCholesky64Bit::Init Success.";
  return true;
}

CUDADenseCholesky64Bit::~CUDADenseCholesky64Bit() {
  if (cusolver_handle_ != nullptr) {
    CHECK_EQ(cusolverDnDestroy(cusolver_handle_), CUSOLVER_STATUS_SUCCESS);
    CHECK_EQ(cudaStreamDestroy(stream_), cudaSuccess);
  }
}

LinearSolverTerminationType CUDADenseCholesky64Bit::Factorize(
    int num_cols, double* lhs, std::string* message) {
  factorize_result_ = LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  // Allocate GPU memory if necessary.
  lhs_.Reserve(num_cols * num_cols);
  num_cols_ = num_cols;
  // Copy A to GPU.
  lhs_.CopyToGpu(lhs, num_cols * num_cols);
  // Allocate scratch space on GPU.
  size_t host_workspace_size = 0;
  size_t device_workspace_size = 0;
  if (cusolverDnXpotrf_bufferSize(cusolver_handle_,
                                  nullptr,
                                  CUBLAS_FILL_MODE_LOWER,
                                  num_cols,
                                  CUDA_R_64F,
                                  lhs_.data(),
                                  num_cols,
                                  CUDA_R_64F,
                                  &device_workspace_size,
                                  &host_workspace_size) !=
      CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnXpotrf_bufferSize failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  // Allocate host scratch memory.
  host_workspace_.resize(host_workspace_size);
  // Allocate GPU scratch memory.
  device_workspace_.Reserve(device_workspace_size);
  // Run the actual factorization (potrf)
  if (cusolverDnXpotrf(cusolver_handle_,
                       nullptr,
                       CUBLAS_FILL_MODE_LOWER,
                       num_cols,
                       CUDA_R_64F,
                       lhs_.data(),
                       num_cols,
                       CUDA_R_64F,
                       device_workspace_.data(),
                       device_workspace_.size(),
                       host_workspace_.data(),
                       host_workspace_.size(),
                       error_.data()) != CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnXpotrf failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  if (cudaDeviceSynchronize() != cudaSuccess ||
      cudaStreamSynchronize(stream_) != cudaSuccess) {
    *message = "Cuda device synchronization failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  int error = 0;
  // Check for errors.
  error_.CopyToHost(&error, 1);
  if (error < 0) {
    LOG(FATAL) << "Congratulations, you found a bug in Ceres - "
               << "please report it. "
               << "cuSolverDN::cusolverDnXpotrf fatal error. "
               << "Argument: " << -error << " is invalid.";
    // The following line is unreachable, but return failure just to be
    // pedantic, since the compiler does not know that.
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  } else if (error > 0) {
    *message = StringPrintf(
        "cuSolverDN::cusolverDnXpotrf numerical failure. "
        "The leading minor of order %d is not positive definite.",
        error);
    factorize_result_ = LinearSolverTerminationType::LINEAR_SOLVER_FAILURE;
    return LinearSolverTerminationType::LINEAR_SOLVER_FAILURE;
  }

  *message = "Success";
  factorize_result_ = LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS;
  return LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS;
}

LinearSolverTerminationType CUDADenseCholesky64Bit::Solve(
    const double* rhs, double* solution, std::string* message) {
  if (factorize_result_ != LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS) {
    *message = "Factorize did not complete succesfully previously.";
    return factorize_result_;
  }
  // Copy RHS to GPU.
  rhs_.CopyToGpu(rhs, num_cols_);
  // Solve the system.
  if (cusolverDnXpotrs(cusolver_handle_,
                       nullptr,
                       CUBLAS_FILL_MODE_LOWER,
                       num_cols_,
                       1,
                       CUDA_R_64F,
                       lhs_.data(),
                       num_cols_,
                       CUDA_R_64F,
                       rhs_.data(),
                       num_cols_,
                       error_.data()) != CUSOLVER_STATUS_SUCCESS) {
    *message = "cuSolverDN::cusolverDnXpotrs failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  if (cudaDeviceSynchronize() != cudaSuccess ||
      cudaStreamSynchronize(stream_) != cudaSuccess) {
    *message = "Cuda device synchronization failed.";
    return LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR;
  }
  // Check for errors.
  int error = 0;
  // Copy error variable from GPU to host.
  error_.CopyToHost(&error, 1);
  if (error != 0) {
    LOG(FATAL) << "Congratulations, you found a bug in Ceres. "
               << "Please report it. "
               << "cuSolverDN::cusolverDnXpotrs fatal error. "
               << "Argument: " << -error << " is invalid.";
  }
  // Copy X from GPU to host.
  rhs_.CopyToHost(solution, num_cols_);
  *message = "Success";
  return LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS;
}

#endif  // CERES_CUDA_NO_64BIT_SOLVER_API

#endif  // CERES_NO_CUDA

}  // namespace internal
}  // namespace ceres