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/* ************************************************************************
* Copyright (C) 2020-2024 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell cop-
* ies of the Software, and to permit persons to whom the Software is furnished
* to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IM-
* PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
* FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
* COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
* IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE-
* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
*
* ************************************************************************ */
#pragma once
#include "clientcommon.hpp"
template <testAPI_t API, bool STRIDED, typename T, typename TT, typename W, typename U>
void gesvda_checkBadArgs(const hipsolverHandle_t handle,
hipsolverEigMode_t jobz,
const int rank,
const int m,
const int n,
W dA,
const int lda,
const int stA,
TT dS,
const int stS,
T dU,
const int ldu,
const int stU,
T dV,
const int ldv,
const int stV,
T dWork,
const int lwork,
U dinfo,
double* hRnrmF,
const int bc)
{
// handle
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
nullptr,
jobz,
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
lwork,
dinfo,
hRnrmF,
bc),
HIPSOLVER_STATUS_NOT_INITIALIZED);
// values
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
hipsolverEigMode_t(-1),
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
lwork,
dinfo,
hRnrmF,
bc),
HIPSOLVER_STATUS_INVALID_ENUM);
#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
// pointers
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
(W) nullptr,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
lwork,
dinfo,
hRnrmF,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA,
lda,
stA,
(TT) nullptr,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
lwork,
dinfo,
hRnrmF,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
(T) nullptr,
ldu,
stU,
dV,
ldv,
stV,
dWork,
lwork,
dinfo,
hRnrmF,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
(T) nullptr,
ldv,
stV,
dWork,
lwork,
dinfo,
hRnrmF,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
lwork,
(U) nullptr,
hRnrmF,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
#endif
}
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
void testing_gesvda_bad_arg()
{
using S = decltype(std::real(T{}));
// safe arguments
hipsolver_local_handle handle;
hipsolverEigMode_t jobz = HIPSOLVER_EIG_MODE_VECTOR;
int rank = 1;
int m = 2;
int n = 2;
int lda = 2;
int ldu = 2;
int ldv = 2;
int stA = 2;
int stS = 2;
int stU = 2;
int stV = 2;
int bc = 1;
if(BATCHED)
{
// // memory allocations
// host_strided_batch_vector<double> hRnrmF(1, 1, 1, 1);
// device_batch_vector<T> dA(1, 1, 1);
// device_strided_batch_vector<S> dS(1, 1, 1, 1);
// device_strided_batch_vector<T> dU(1, 1, 1, 1);
// device_strided_batch_vector<T> dV(1, 1, 1, 1);
// device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
// CHECK_HIP_ERROR(dA.memcheck());
// CHECK_HIP_ERROR(dS.memcheck());
// CHECK_HIP_ERROR(dU.memcheck());
// CHECK_HIP_ERROR(dV.memcheck());
// CHECK_HIP_ERROR(dinfo.memcheck());
// int size_W;
// hipsolver_gesvda_bufferSize(API,
// STRIDED,
// handle,
// jobz,
// rank,
// m,
// n,
// dA.data(),
// lda,
// stA,
// dS.data(),
// stS,
// dU.data(),
// ldu,
// stU,
// dV.data(),
// ldv,
// stV,
// &size_W,
// bc);
// device_strided_batch_vector<T> dWork(size_W, 1, size_W, 1);
// if(size_W)
// CHECK_HIP_ERROR(dWork.memcheck());
// // check bad arguments
// gesvda_checkBadArgs<API, STRIDED>(handle,
// jobz,
// rank,
// m,
// n,
// dA.data(),
// lda,
// stA,
// dS.data(),
// stS,
// dU.data(),
// ldu,
// stU,
// dV.data(),
// ldv,
// stV,
// dWork.data(),
// size_W,
// dinfo.data(),
// hRnrmF.data(),
// bc);
}
else
{
// memory allocations
host_strided_batch_vector<double> hRnrmF(1, 1, 1, 1);
device_strided_batch_vector<T> dA(1, 1, 1, 1);
device_strided_batch_vector<S> dS(1, 1, 1, 1);
device_strided_batch_vector<T> dU(1, 1, 1, 1);
device_strided_batch_vector<T> dV(1, 1, 1, 1);
device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
CHECK_HIP_ERROR(dA.memcheck());
CHECK_HIP_ERROR(dS.memcheck());
CHECK_HIP_ERROR(dU.memcheck());
CHECK_HIP_ERROR(dV.memcheck());
CHECK_HIP_ERROR(dinfo.memcheck());
int size_W;
hipsolver_gesvda_bufferSize(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA.data(),
lda,
stA,
dS.data(),
stS,
dU.data(),
ldu,
stU,
dV.data(),
ldv,
stV,
&size_W,
bc);
device_strided_batch_vector<T> dWork(size_W, 1, size_W, 1);
if(size_W)
CHECK_HIP_ERROR(dWork.memcheck());
// check bad arguments
gesvda_checkBadArgs<API, STRIDED>(handle,
jobz,
rank,
m,
n,
dA.data(),
lda,
stA,
dS.data(),
stS,
dU.data(),
ldu,
stU,
dV.data(),
ldv,
stV,
dWork.data(),
size_W,
dinfo.data(),
hRnrmF,
bc);
}
}
template <bool CPU, bool GPU, typename T, typename Td, typename Th>
void gesvda_initData(const hipsolverHandle_t handle,
hipsolverEigMode_t jobz,
const int m,
const int n,
Td& dA,
const int lda,
const int bc,
Th& hA,
std::vector<T>& A,
bool test = true)
{
if(CPU)
{
rocblas_init<T>(hA, true);
for(int b = 0; b < bc; ++b)
{
// scale A to avoid singularities
for(int i = 0; i < m; i++)
{
for(int j = 0; j < n; j++)
{
if(i == j)
hA[b][i + j * lda] += 400;
else
hA[b][i + j * lda] -= 4;
}
}
// make copy of original data to test vectors if required
if(test && jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
{
for(int i = 0; i < m; i++)
{
for(int j = 0; j < n; j++)
A[b * lda * n + i + j * lda] = hA[b][i + j * lda];
}
}
}
}
if(GPU)
{
// now copy to the GPU
CHECK_HIP_ERROR(dA.transfer_from(hA));
}
}
template <testAPI_t API,
bool STRIDED,
typename T,
typename Wd,
typename Td,
typename Ud,
typename Id,
typename Wh,
typename Th,
typename Uh,
typename Ih>
void gesvda_getError(const hipsolverHandle_t handle,
hipsolverEigMode_t jobz,
const int rank,
const int m,
const int n,
Wd& dA,
const int lda,
const int stA,
Td& dS,
const int stS,
Ud& dU,
const int ldu,
const int stU,
Ud& dV,
const int ldv,
const int stV,
Ud& dWork,
const int lwork,
Id& dinfo,
double* hRnrmF,
const int bc,
Wh& hA,
Th& hS,
Th& hSres,
Uh& hUres,
Uh& hVres,
Ih& hinfo,
Ih& hinfoRes,
double* max_err,
double* max_errv)
{
/** WORKAROUND: Due to errors in gesvdx, we will call gesvd to get all the singular values on the CPU side
and use a subset of them for comparison. This approach has 2 disadvantages:
1. singular values are not computed to the same accuracy by gesvd and gesvda. So, comparison maybe more sensitive.
2. info cannot be tested as it has a different meaning in gesvd
3. we cannot provide timing for CPU execution using gesvd when testing gesvda **/
// (TODO: We may revisit the entire approach in the future: change to another solution,
// or wait for problems with gesvdx_ to be fixed)
using S = decltype(std::real(T{}));
int size_W = 5 * max(m, n);
std::vector<S> hE(size_W);
std::vector<T> hWork(size_W);
std::vector<T> A(lda * n * bc);
// input data initialization
gesvda_initData<true, true, T>(handle, jobz, m, n, dA, lda, bc, hA, A);
// GPU lapack
CHECK_ROCBLAS_ERROR(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA.data(),
lda,
stA,
dS.data(),
stS,
dU.data(),
ldu,
stU,
dV.data(),
ldv,
stV,
dWork.data(),
lwork,
dinfo.data(),
hRnrmF,
bc));
CHECK_HIP_ERROR(hSres.transfer_from(dS));
CHECK_HIP_ERROR(hinfoRes.transfer_from(dinfo));
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
{
CHECK_HIP_ERROR(hUres.transfer_from(dU));
CHECK_HIP_ERROR(hVres.transfer_from(dV));
}
// CPU lapack
// Only singular values needed
for(int b = 0; b < bc; ++b)
cpu_gesvd<T>('N',
'N',
m,
n,
hA[b],
lda,
hS[b],
nullptr,
ldu,
nullptr,
ldv,
hWork.data(),
size_W,
hE.data(),
hinfo[b]);
// // Check info for non-convergence
*max_err = 0;
// for(int b = 0; b < bc; ++b)
// {
// EXPECT_EQ(hinfo[b][0], hinfoRes[b][0]) << "where b = " << b;
// if(hinfo[b][0] != hinfoRes[b][0])
// *max_err += 1;
// }
double err;
*max_errv = 0;
for(int b = 0; b < bc; ++b)
{
// error is ||hS - hSres||
err = norm_error('F', 1, rank, 1, hS[b], hSres[b]);
*max_err = err > *max_err ? err : *max_err;
// Check the singular vectors if required
if(hinfoRes[b][0] == 0 && jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
{
err = 0;
// check singular vectors implicitly (A*v_k = s_k*u_k)
for(int k = 0; k < rank; ++k)
{
T tmp = 0;
double tmp2 = 0;
// (Comparing absolute values to deal with the fact that the pair of singular vectors (u,-v) or (-u,v) are
// both ok and we could get either one with the complementary or main executions when only
// one side set of vectors is required. May be revisited in the future.)
for(int i = 0; i < m; ++i)
{
tmp = 0;
for(rocblas_int j = 0; j < n; ++j)
tmp += A[b * lda * n + i + j * lda] * hVres[b][j + k * ldv];
tmp2 = std::abs(tmp) - std::abs(hSres[b][k] * hUres[b][i + k * ldu]);
err += tmp2 * tmp2;
}
}
err = std::sqrt(err) / double(snorm('F', m, n, A.data() + b * lda * n, lda));
*max_errv = err > *max_errv ? err : *max_errv;
}
}
}
template <testAPI_t API,
bool STRIDED,
typename T,
typename Wd,
typename Td,
typename Ud,
typename Id,
typename Wh,
typename Th,
typename Uh,
typename Ih>
void gesvda_getPerfData(const hipsolverHandle_t handle,
hipsolverEigMode_t jobz,
const int rank,
const int m,
const int n,
Wd& dA,
const int lda,
const int stA,
Td& dS,
const int stS,
Ud& dU,
const int ldu,
const int stU,
Ud& dV,
const int ldv,
const int stV,
Ud& dWork,
const int lwork,
Id& dinfo,
double* hRnrmF,
const int bc,
Wh& hA,
Th& hS,
Uh& hU,
Uh& hV,
Ih& hinfo,
double* gpu_time_used,
double* cpu_time_used,
const int hot_calls,
const bool perf)
{
std::vector<T> A;
if(!perf)
{
// For now we cannot report cpu time due to errors in LAPACK's gesvdx
*cpu_time_used = nan("");
}
gesvda_initData<true, false, T>(handle, jobz, m, n, dA, lda, bc, hA, A, 0);
// cold calls
for(int iter = 0; iter < 2; iter++)
{
gesvda_initData<false, true, T>(handle, jobz, m, n, dA, lda, bc, hA, A, 0);
CHECK_ROCBLAS_ERROR(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA.data(),
lda,
stA,
dS.data(),
stS,
dU.data(),
ldu,
stU,
dV.data(),
ldv,
stV,
dWork.data(),
lwork,
dinfo.data(),
hRnrmF,
bc));
}
// gpu-lapack performance
hipStream_t stream;
CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
double start;
for(int iter = 0; iter < hot_calls; iter++)
{
gesvda_initData<false, true, T>(handle, jobz, m, n, dA, lda, bc, hA, A, 0);
start = get_time_us_sync(stream);
hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
dA.data(),
lda,
stA,
dS.data(),
stS,
dU.data(),
ldu,
stU,
dV.data(),
ldv,
stV,
dWork.data(),
lwork,
dinfo.data(),
hRnrmF,
bc);
*gpu_time_used += get_time_us_sync(stream) - start;
}
*gpu_time_used /= hot_calls;
}
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
void testing_gesvda(Arguments& argus)
{
using S = decltype(std::real(T{}));
// get arguments
hipsolver_local_handle handle;
char jobzC = argus.get<char>("jobz");
int rank = argus.get<int>("rank", 1);
int m = argus.get<int>("m");
int n = argus.get<int>("n", m);
int lda = argus.get<int>("lda", m);
int ldu = argus.get<int>("ldu", m);
int ldv = argus.get<int>("ldv", n);
rocblas_stride stA = argus.get<rocblas_stride>("strideA", lda * n);
rocblas_stride stS = argus.get<rocblas_stride>("strideS", rank);
rocblas_stride stU = argus.get<rocblas_stride>("strideU", ldu * rank);
rocblas_stride stV = argus.get<rocblas_stride>("strideV", ldv * rank);
hipsolverEigMode_t jobz = char2hipsolver_evect(jobzC);
int bc = argus.batch_count;
int hot_calls = argus.iters;
rocblas_stride stUres = 0;
rocblas_stride stVres = 0;
// determine sizes
size_t size_A = size_t(lda) * n;
size_t size_S = size_t(rank);
size_t size_S_cpu = size_t(min(m, n));
size_t size_V = 0;
size_t size_U = 0;
size_t size_Sres = 0;
size_t size_hUres = 0;
size_t size_hVres = 0;
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
{
size_U = size_t(ldu) * rank;
size_V = size_t(ldv) * rank;
}
if(argus.unit_check || argus.norm_check)
{
size_Sres = size_S;
size_hUres = size_U;
size_hVres = size_V;
stUres = stU;
stVres = stV;
}
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0, max_errorv = 0;
// check invalid sizes
bool invalid_size = (rank <= 0 || rank > min(m, n) || n < 0 || m < 0 || lda < m || ldu < 1
|| ldv < 1 || bc < 0)
|| (jobz != HIPSOLVER_EIG_MODE_NOVECTOR && (ldu < m || ldv < n));
if(invalid_size)
{
if(BATCHED)
{
// EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
// STRIDED,
// handle,
// jobz,
// rank,
// m,
// n,
// (T* const*)nullptr,
// lda,
// stA,
// (S*)nullptr,
// stS,
// (T*)nullptr,
// ldu,
// stU,
// (T*)nullptr,
// ldv,
// stV,
// (T*)nullptr,
// 0,
// (int*)nullptr,
// (double*)nullptr,
// bc),
// HIPSOLVER_STATUS_INVALID_VALUE);
}
else
{
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
(T*)nullptr,
lda,
stA,
(S*)nullptr,
stS,
(T*)nullptr,
ldu,
stU,
(T*)nullptr,
ldv,
stV,
(T*)nullptr,
0,
(int*)nullptr,
(double*)nullptr,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
}
if(argus.timing)
rocsolver_bench_inform(inform_invalid_size);
return;
}
// memory size query is necessary
int size_W;
hipsolver_gesvda_bufferSize(API,
STRIDED,
handle,
jobz,
rank,
m,
n,
(T*)nullptr,
lda,
stA,
(S*)nullptr,
stS,
(T*)nullptr,
ldu,
stU,
(T*)nullptr,
ldv,
stV,
&size_W,
bc);
if(argus.mem_query)
{
rocsolver_bench_inform(inform_mem_query, size_W);
return;
}
// memory allocations (all cases)
// host
host_strided_batch_vector<S> hS(
size_S_cpu, 1, size_S_cpu, bc); // extra space for cpu_gesvd call
host_strided_batch_vector<T> hV(size_V, 1, stV, bc);
host_strided_batch_vector<T> hU(size_U, 1, stU, bc);
host_strided_batch_vector<double> hRnrmF(1, 1, 1, bc);
host_strided_batch_vector<int> hinfo(1, 1, 1, bc);
host_strided_batch_vector<int> hinfoRes(1, 1, 1, bc);
host_strided_batch_vector<S> hSres(size_Sres, 1, stS, bc);
host_strided_batch_vector<T> hVres(size_hVres, 1, stVres, bc);
host_strided_batch_vector<T> hUres(size_hUres, 1, stUres, bc);
// device
device_strided_batch_vector<S> dS(size_S, 1, stS, bc);
device_strided_batch_vector<T> dV(size_V, 1, stV, bc);
device_strided_batch_vector<T> dU(size_U, 1, stU, bc);
device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
device_strided_batch_vector<T> dWork(size_W, 1, size_W, 1); // size_W accounts for bc
if(size_S)
CHECK_HIP_ERROR(dS.memcheck());
if(size_V)
CHECK_HIP_ERROR(dV.memcheck());
if(size_U)
CHECK_HIP_ERROR(dU.memcheck());
CHECK_HIP_ERROR(dinfo.memcheck());
if(size_W)
CHECK_HIP_ERROR(dWork.memcheck());
if(BATCHED)
{
// // memory allocations
// host_batch_vector<T> hA(size_A, 1, bc);
// device_batch_vector<T> dA(size_A, 1, bc);
// if(size_A)
// CHECK_HIP_ERROR(dA.memcheck());
// // check computations
// if(argus.unit_check || argus.norm_check)
// {
// gesvda_getError<API, STRIDED, T>(handle,
// jobz,
// rank,
// m,
// n,
// dA,
// lda,
// stA,
// dS,
// stS,
// dU,
// ldu,
// stU,
// dV,
// ldv,
// stV,
// dWork,
// size_W,
// dinfo,
// hRnrmF,
// bc,
// hA,
// hS,
// hSres,
// hUres,
// hVres,
// hinfo,
// hinfoRes,
// &max_error,
// &max_errorv);
// }
// // collect performance data
// if(argus.timing)
// {
// gesvda_getPerfData<API, STRIDED, T>(handle,
// jobz,
// rank,
// m,
// n,
// dA,
// lda,
// stA,
// dS,
// stS,
// dU,
// ldu,
// stU,
// dV,
// ldv,
// stV,
// dWork,
// size_W,
// dinfo,
// hRnrmF,
// bc,
// hA,
// hS,
// hU,
// hV,
// hinfo,
// &gpu_time_used,
// &cpu_time_used,
// hot_calls,
// argus.perf);
// }
}
else
{
// memory allocations
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
if(size_A)
CHECK_HIP_ERROR(dA.memcheck());
// check computations
if(argus.unit_check || argus.norm_check)
{
gesvda_getError<API, STRIDED, T>(handle,
jobz,
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
size_W,
dinfo,
hRnrmF,
bc,
hA,
hS,
hSres,
hUres,
hVres,
hinfo,
hinfoRes,
&max_error,
&max_errorv);
}
// collect performance data
if(argus.timing)
{
gesvda_getPerfData<API, STRIDED, T>(handle,
jobz,
rank,
m,
n,
dA,
lda,
stA,
dS,
stS,
dU,
ldu,
stU,
dV,
ldv,
stV,
dWork,
size_W,
dinfo,
hRnrmF,
bc,
hA,
hS,
hU,
hV,
hinfo,
&gpu_time_used,
&cpu_time_used,
hot_calls,
argus.perf);
}
}
// validate results for rocsolver-test
// using 3 * min(m, n) * machine_precision as tolerance
if(argus.unit_check)
{
ROCSOLVER_TEST_CHECK(T, max_error, 3 * min(m, n));
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
ROCSOLVER_TEST_CHECK(T, max_errorv, 3 * min(m, n));
}
// output results for rocsolver-bench
if(argus.timing)
{
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
max_error = (max_error >= max_errorv) ? max_error : max_errorv;
if(!argus.perf)
{
std::cerr << "\n============================================\n";
std::cerr << "Arguments:\n";
std::cerr << "============================================\n";
if(BATCHED)
{
rocsolver_bench_output("jobz", "rank", "m", "n", "lda", "ldu", "ldv", "batch_c");
rocsolver_bench_output(jobz, rank, m, n, lda, ldu, ldv, bc);
}
else if(STRIDED)
{
rocsolver_bench_output("jobz",
"rank",
"m",
"n",
"lda",
"strideA",
"strideS",
"ldu",
"strideU",
"ldv",
"strideV",
"batch_c");
rocsolver_bench_output(jobz, rank, m, n, lda, stA, stS, ldu, stU, ldv, stV, bc);
}
else
{
rocsolver_bench_output("jobz", "rank", "m", "n", "lda", "ldu", "ldv");
rocsolver_bench_output(jobz, rank, m, n, lda, ldu, ldv);
}
std::cerr << "\n============================================\n";
std::cerr << "Results:\n";
std::cerr << "============================================\n";
if(argus.norm_check)
{
rocsolver_bench_output("cpu_time", "gpu_time", "error");
rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
}
else
{
rocsolver_bench_output("cpu_time", "gpu_time");
rocsolver_bench_output(cpu_time_used, gpu_time_used);
}
std::cerr << std::endl;
}
else
{
if(argus.norm_check)
rocsolver_bench_output(gpu_time_used, max_error);
else
rocsolver_bench_output(gpu_time_used);
}
}
// ensure all arguments were consumed
argus.validate_consumed();
}
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