/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
 *
 * 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 NVIDIA CORPORATION 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 ``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,
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 * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

/*
 *  Test three linear solvers, including Cholesky, LU and QR.
 *  The user has to prepare a sparse matrix of "matrix market format" (with
 * extension .mtx). For example, the user can download matrices in Florida
 * Sparse Matrix Collection.
 *  (http://www.cise.ufl.edu/research/sparse/matrices/)
 *
 *  The user needs to choose a solver by switch -R<solver> and
 *  to provide the path of the matrix by switch -F<file>, then
 *  the program solves
 *          A*x = b  where b = ones(m,1)
 *  and reports relative error
 *          |b-A*x|/(|A|*|x|)
 *
 *  The elapsed time is also reported so the user can compare efficiency of
 * different solvers.
 *
 *  How to use
 *      ./cuSolverDn_LinearSolver                     // Default: cholesky
 *     ./cuSolverDn_LinearSolver -R=chol -filefile>   // cholesky factorization
 *     ./cuSolverDn_LinearSolver -R=lu -file<file>     // LU with partial
 * pivoting
 *     ./cuSolverDn_LinearSolver -R=qr -file<file>     // QR factorization
 *
 *  Remark: the absolute error on solution x is meaningless without knowing
 * condition number of A. The relative error on residual should be close to
 * machine zero, i.e. 1.e-15.
 */

#include <assert.h>
#include <ctype.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include <cuda_runtime.h>

#include "cublas_v2.h"
#include "cusolverDn.h"
#include "helper_cuda.h"

#include "helper_cusolver.h"

template <typename T_ELEM>
int loadMMSparseMatrix(char *filename, char elem_type, bool csrFormat, int *m,
                       int *n, int *nnz, T_ELEM **aVal, int **aRowInd,
                       int **aColInd, int extendSymMatrix);

void UsageDN(void) {
  printf("<options>\n");
  printf("-h          : display this help\n");
  printf("-R=<name>    : choose a linear solver\n");
  printf("              chol (cholesky factorization), this is default\n");
  printf("              qr   (QR factorization)\n");
  printf("              lu   (LU factorization)\n");
  printf("-lda=<int> : leading dimension of A , m by default\n");
  printf("-file=<filename>: filename containing a matrix in MM format\n");
  printf("-device=<device_id> : <device_id> if want to run on specific GPU\n");

  exit(0);
}

/*
 *  solve A*x = b by Cholesky factorization
 *
 */
int linearSolverCHOL(cusolverDnHandle_t handle, int n, const double *Acopy,
                     int lda, const double *b, double *x) {
  int bufferSize = 0;
  int *info = NULL;
  double *buffer = NULL;
  double *A = NULL;
  int h_info = 0;
  double start, stop;
  double time_solve;
  cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER;

  checkCudaErrors(cusolverDnDpotrf_bufferSize(handle, uplo, n, (double *)Acopy,
                                              lda, &bufferSize));

  checkCudaErrors(cudaMalloc(&info, sizeof(int)));
  checkCudaErrors(cudaMalloc(&buffer, sizeof(double) * bufferSize));
  checkCudaErrors(cudaMalloc(&A, sizeof(double) * lda * n));

  // prepare a copy of A because potrf will overwrite A with L
  checkCudaErrors(
      cudaMemcpy(A, Acopy, sizeof(double) * lda * n, cudaMemcpyDeviceToDevice));
  checkCudaErrors(cudaMemset(info, 0, sizeof(int)));

  start = second();
  start = second();

  checkCudaErrors(
      cusolverDnDpotrf(handle, uplo, n, A, lda, buffer, bufferSize, info));

  checkCudaErrors(
      cudaMemcpy(&h_info, info, sizeof(int), cudaMemcpyDeviceToHost));

  if (0 != h_info) {
    fprintf(stderr, "Error: Cholesky factorization failed\n");
  }

  checkCudaErrors(
      cudaMemcpy(x, b, sizeof(double) * n, cudaMemcpyDeviceToDevice));

  checkCudaErrors(cusolverDnDpotrs(handle, uplo, n, 1, A, lda, x, n, info));

  checkCudaErrors(cudaDeviceSynchronize());
  stop = second();

  time_solve = stop - start;
  fprintf(stdout, "timing: cholesky = %10.6f sec\n", time_solve);

  if (info) {
    checkCudaErrors(cudaFree(info));
  }
  if (buffer) {
    checkCudaErrors(cudaFree(buffer));
  }
  if (A) {
    checkCudaErrors(cudaFree(A));
  }

  return 0;
}

/*
 *  solve A*x = b by LU with partial pivoting
 *
 */
int linearSolverLU(cusolverDnHandle_t handle, int n, const double *Acopy,
                   int lda, const double *b, double *x) {
  int bufferSize = 0;
  int *info = NULL;
  double *buffer = NULL;
  double *A = NULL;
  int *ipiv = NULL;  // pivoting sequence
  int h_info = 0;
  double start, stop;
  double time_solve;

  checkCudaErrors(cusolverDnDgetrf_bufferSize(handle, n, n, (double *)Acopy,
                                              lda, &bufferSize));

  checkCudaErrors(cudaMalloc(&info, sizeof(int)));
  checkCudaErrors(cudaMalloc(&buffer, sizeof(double) * bufferSize));
  checkCudaErrors(cudaMalloc(&A, sizeof(double) * lda * n));
  checkCudaErrors(cudaMalloc(&ipiv, sizeof(int) * n));

  // prepare a copy of A because getrf will overwrite A with L
  checkCudaErrors(
      cudaMemcpy(A, Acopy, sizeof(double) * lda * n, cudaMemcpyDeviceToDevice));
  checkCudaErrors(cudaMemset(info, 0, sizeof(int)));

  start = second();
  start = second();

  checkCudaErrors(cusolverDnDgetrf(handle, n, n, A, lda, buffer, ipiv, info));
  checkCudaErrors(
      cudaMemcpy(&h_info, info, sizeof(int), cudaMemcpyDeviceToHost));

  if (0 != h_info) {
    fprintf(stderr, "Error: LU factorization failed\n");
  }

  checkCudaErrors(
      cudaMemcpy(x, b, sizeof(double) * n, cudaMemcpyDeviceToDevice));
  checkCudaErrors(
      cusolverDnDgetrs(handle, CUBLAS_OP_N, n, 1, A, lda, ipiv, x, n, info));
  checkCudaErrors(cudaDeviceSynchronize());
  stop = second();

  time_solve = stop - start;
  fprintf(stdout, "timing: LU = %10.6f sec\n", time_solve);

  if (info) {
    checkCudaErrors(cudaFree(info));
  }
  if (buffer) {
    checkCudaErrors(cudaFree(buffer));
  }
  if (A) {
    checkCudaErrors(cudaFree(A));
  }
  if (ipiv) {
    checkCudaErrors(cudaFree(ipiv));
  }

  return 0;
}

/*
 *  solve A*x = b by QR
 *
 */
int linearSolverQR(cusolverDnHandle_t handle, int n, const double *Acopy,
                   int lda, const double *b, double *x) {
  cublasHandle_t cublasHandle = NULL;  // used in residual evaluation
  int bufferSize = 0;
  int bufferSize_geqrf = 0;
  int bufferSize_ormqr = 0;
  int *info = NULL;
  double *buffer = NULL;
  double *A = NULL;
  double *tau = NULL;
  int h_info = 0;
  double start, stop;
  double time_solve;
  const double one = 1.0;

  checkCudaErrors(cublasCreate(&cublasHandle));

  checkCudaErrors(cusolverDnDgeqrf_bufferSize(handle, n, n, (double *)Acopy,
                                              lda, &bufferSize_geqrf));
  checkCudaErrors(cusolverDnDormqr_bufferSize(handle, CUBLAS_SIDE_LEFT,
                                              CUBLAS_OP_T, n, 1, n, A, lda,
                                              NULL, x, n, &bufferSize_ormqr));

  printf("buffer_geqrf = %d, buffer_ormqr = %d \n", bufferSize_geqrf,
         bufferSize_ormqr);

  bufferSize = (bufferSize_geqrf > bufferSize_ormqr) ? bufferSize_geqrf
                                                     : bufferSize_ormqr;

  checkCudaErrors(cudaMalloc(&info, sizeof(int)));
  checkCudaErrors(cudaMalloc(&buffer, sizeof(double) * bufferSize));
  checkCudaErrors(cudaMalloc(&A, sizeof(double) * lda * n));
  checkCudaErrors(cudaMalloc((void **)&tau, sizeof(double) * n));

  // prepare a copy of A because getrf will overwrite A with L
  checkCudaErrors(
      cudaMemcpy(A, Acopy, sizeof(double) * lda * n, cudaMemcpyDeviceToDevice));

  checkCudaErrors(cudaMemset(info, 0, sizeof(int)));

  start = second();
  start = second();

  // compute QR factorization
  checkCudaErrors(
      cusolverDnDgeqrf(handle, n, n, A, lda, tau, buffer, bufferSize, info));

  checkCudaErrors(
      cudaMemcpy(&h_info, info, sizeof(int), cudaMemcpyDeviceToHost));

  if (0 != h_info) {
    fprintf(stderr, "Error: LU factorization failed\n");
  }

  checkCudaErrors(
      cudaMemcpy(x, b, sizeof(double) * n, cudaMemcpyDeviceToDevice));

  // compute Q^T*b
  checkCudaErrors(cusolverDnDormqr(handle, CUBLAS_SIDE_LEFT, CUBLAS_OP_T, n, 1,
                                   n, A, lda, tau, x, n, buffer, bufferSize,
                                   info));

  // x = R \ Q^T*b
  checkCudaErrors(cublasDtrsm(cublasHandle, CUBLAS_SIDE_LEFT,
                              CUBLAS_FILL_MODE_UPPER, CUBLAS_OP_N,
                              CUBLAS_DIAG_NON_UNIT, n, 1, &one, A, lda, x, n));
  checkCudaErrors(cudaDeviceSynchronize());
  stop = second();

  time_solve = stop - start;
  fprintf(stdout, "timing: QR = %10.6f sec\n", time_solve);

  if (cublasHandle) {
    checkCudaErrors(cublasDestroy(cublasHandle));
  }
  if (info) {
    checkCudaErrors(cudaFree(info));
  }
  if (buffer) {
    checkCudaErrors(cudaFree(buffer));
  }
  if (A) {
    checkCudaErrors(cudaFree(A));
  }
  if (tau) {
    checkCudaErrors(cudaFree(tau));
  }

  return 0;
}

void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts) {
  memset(&opts, 0, sizeof(opts));

  if (checkCmdLineFlag(argc, (const char **)argv, "-h")) {
    UsageDN();
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "R")) {
    char *solverType = NULL;
    getCmdLineArgumentString(argc, (const char **)argv, "R", &solverType);

    if (solverType) {
      if ((STRCASECMP(solverType, "chol") != 0) &&
          (STRCASECMP(solverType, "lu") != 0) &&
          (STRCASECMP(solverType, "qr") != 0)) {
        printf("\nIncorrect argument passed to -R option\n");
        UsageDN();
      } else {
        opts.testFunc = solverType;
      }
    }
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "file")) {
    char *fileName = 0;
    getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName);

    if (fileName) {
      opts.sparse_mat_filename = fileName;
    } else {
      printf("\nIncorrect filename passed to -file \n ");
      UsageDN();
    }
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "lda")) {
    opts.lda = getCmdLineArgumentInt(argc, (const char **)argv, "lda");
  }
}

int main(int argc, char *argv[]) {
  struct testOpts opts;
  cusolverDnHandle_t handle = NULL;
  cublasHandle_t cublasHandle = NULL;  // used in residual evaluation
  cudaStream_t stream = NULL;

  int rowsA = 0;  // number of rows of A
  int colsA = 0;  // number of columns of A
  int nnzA = 0;   // number of nonzeros of A
  int baseA = 0;  // base index in CSR format
  int lda = 0;    // leading dimension in dense matrix

  // CSR(A) from I/O
  int *h_csrRowPtrA = NULL;
  int *h_csrColIndA = NULL;
  double *h_csrValA = NULL;

  double *h_A = NULL;  // dense matrix from CSR(A)
  double *h_x = NULL;  // a copy of d_x
  double *h_b = NULL;  // b = ones(m,1)
  double *h_r = NULL;  // r = b - A*x, a copy of d_r

  double *d_A = NULL;  // a copy of h_A
  double *d_x = NULL;  // x = A \ b
  double *d_b = NULL;  // a copy of h_b
  double *d_r = NULL;  // r = b - A*x

  // the constants are used in residual evaluation, r = b - A*x
  const double minus_one = -1.0;
  const double one = 1.0;

  double x_inf = 0.0;
  double r_inf = 0.0;
  double A_inf = 0.0;
  int errors = 0;

  parseCommandLineArguments(argc, argv, opts);

  if (NULL == opts.testFunc) {
    opts.testFunc = "chol";  // By default running Cholesky as NO solver
                             // selected with -R option.
  }

  findCudaDevice(argc, (const char **)argv);

  printf("step 1: read matrix market format\n");

  if (opts.sparse_mat_filename == NULL) {
    opts.sparse_mat_filename = sdkFindFilePath("gr_900_900_crg.mtx", argv[0]);
    if (opts.sparse_mat_filename != NULL)
      printf("Using default input file [%s]\n", opts.sparse_mat_filename);
    else
      printf("Could not find gr_900_900_crg.mtx\n");
  } else {
    printf("Using input file [%s]\n", opts.sparse_mat_filename);
  }

  if (opts.sparse_mat_filename == NULL) {
    fprintf(stderr, "Error: input matrix is not provided\n");
    return EXIT_FAILURE;
  }

  if (loadMMSparseMatrix<double>(opts.sparse_mat_filename, 'd', true, &rowsA,
                                 &colsA, &nnzA, &h_csrValA, &h_csrRowPtrA,
                                 &h_csrColIndA, true)) {
    exit(EXIT_FAILURE);
  }
  baseA = h_csrRowPtrA[0];  // baseA = {0,1}

  printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA,
         nnzA, baseA);

  if (rowsA != colsA) {
    fprintf(stderr, "Error: only support square matrix\n");
    exit(EXIT_FAILURE);
  }

  printf("step 2: convert CSR(A) to dense matrix\n");

  lda = opts.lda ? opts.lda : rowsA;
  if (lda < rowsA) {
    fprintf(stderr, "Error: lda must be greater or equal to dimension of A\n");
    exit(EXIT_FAILURE);
  }

  h_A = (double *)malloc(sizeof(double) * lda * colsA);
  h_x = (double *)malloc(sizeof(double) * colsA);
  h_b = (double *)malloc(sizeof(double) * rowsA);
  h_r = (double *)malloc(sizeof(double) * rowsA);
  assert(NULL != h_A);
  assert(NULL != h_x);
  assert(NULL != h_b);
  assert(NULL != h_r);

  memset(h_A, 0, sizeof(double) * lda * colsA);

  for (int row = 0; row < rowsA; row++) {
    const int start = h_csrRowPtrA[row] - baseA;
    const int end = h_csrRowPtrA[row + 1] - baseA;
    for (int colidx = start; colidx < end; colidx++) {
      const int col = h_csrColIndA[colidx] - baseA;
      const double Areg = h_csrValA[colidx];
      h_A[row + col * lda] = Areg;
    }
  }

  printf("step 3: set right hand side vector (b) to 1\n");
  for (int row = 0; row < rowsA; row++) {
    h_b[row] = 1.0;
  }

  // verify if A is symmetric or not.
  if (0 == strcmp(opts.testFunc, "chol")) {
    int issym = 1;
    for (int j = 0; j < colsA; j++) {
      for (int i = j; i < rowsA; i++) {
        double Aij = h_A[i + j * lda];
        double Aji = h_A[j + i * lda];
        if (Aij != Aji) {
          issym = 0;
          break;
        }
      }
    }
    if (!issym) {
      printf("Error: A has no symmetric pattern, please use LU or QR \n");
      exit(EXIT_FAILURE);
    }
  }

  checkCudaErrors(cusolverDnCreate(&handle));
  checkCudaErrors(cublasCreate(&cublasHandle));
  checkCudaErrors(cudaStreamCreate(&stream));

  checkCudaErrors(cusolverDnSetStream(handle, stream));
  checkCudaErrors(cublasSetStream(cublasHandle, stream));

  checkCudaErrors(cudaMalloc((void **)&d_A, sizeof(double) * lda * colsA));
  checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA));
  checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA));
  checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA));

  printf("step 4: prepare data on device\n");
  checkCudaErrors(cudaMemcpy(d_A, h_A, sizeof(double) * lda * colsA,
                             cudaMemcpyHostToDevice));
  checkCudaErrors(
      cudaMemcpy(d_b, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice));

  printf("step 5: solve A*x = b \n");
  // d_A and d_b are read-only
  if (0 == strcmp(opts.testFunc, "chol")) {
    linearSolverCHOL(handle, rowsA, d_A, lda, d_b, d_x);
  } else if (0 == strcmp(opts.testFunc, "lu")) {
    linearSolverLU(handle, rowsA, d_A, lda, d_b, d_x);
  } else if (0 == strcmp(opts.testFunc, "qr")) {
    linearSolverQR(handle, rowsA, d_A, lda, d_b, d_x);
  } else {
    fprintf(stderr, "Error: %s is unknown function\n", opts.testFunc);
    exit(EXIT_FAILURE);
  }
  printf("step 6: evaluate residual\n");
  checkCudaErrors(
      cudaMemcpy(d_r, d_b, sizeof(double) * rowsA, cudaMemcpyDeviceToDevice));

  // r = b - A*x
  checkCudaErrors(cublasDgemm_v2(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N, rowsA,
                                 1, colsA, &minus_one, d_A, lda, d_x, rowsA,
                                 &one, d_r, rowsA));

  checkCudaErrors(
      cudaMemcpy(h_x, d_x, sizeof(double) * colsA, cudaMemcpyDeviceToHost));
  checkCudaErrors(
      cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost));

  x_inf = vec_norminf(colsA, h_x);
  r_inf = vec_norminf(rowsA, h_r);
  A_inf = mat_norminf(rowsA, colsA, h_A, lda);

  printf("|b - A*x| = %E \n", r_inf);
  printf("|A| = %E \n", A_inf);
  printf("|x| = %E \n", x_inf);
  printf("|b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf));

  if (handle) {
    checkCudaErrors(cusolverDnDestroy(handle));
  }
  if (cublasHandle) {
    checkCudaErrors(cublasDestroy(cublasHandle));
  }
  if (stream) {
    checkCudaErrors(cudaStreamDestroy(stream));
  }

  if (h_csrValA) {
    free(h_csrValA);
  }
  if (h_csrRowPtrA) {
    free(h_csrRowPtrA);
  }
  if (h_csrColIndA) {
    free(h_csrColIndA);
  }

  if (h_A) {
    free(h_A);
  }
  if (h_x) {
    free(h_x);
  }
  if (h_b) {
    free(h_b);
  }
  if (h_r) {
    free(h_r);
  }

  if (d_A) {
    checkCudaErrors(cudaFree(d_A));
  }
  if (d_x) {
    checkCudaErrors(cudaFree(d_x));
  }
  if (d_b) {
    checkCudaErrors(cudaFree(d_b));
  }
  if (d_r) {
    checkCudaErrors(cudaFree(d_r));
  }

  return 0;
}
