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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, typename T, typename U, typename V>
void potrf_checkBadArgs(const hipsolverHandle_t handle,
const hipsolverFillMode_t uplo,
const int n,
T dA,
const int lda,
const int stA,
U dWork,
const int lwork,
V dinfo,
const int bc)
{
// handle
EXPECT_ROCBLAS_STATUS(
hipsolver_potrf(API, nullptr, uplo, n, dA, lda, stA, dWork, lwork, dinfo, bc),
HIPSOLVER_STATUS_NOT_INITIALIZED);
// values
EXPECT_ROCBLAS_STATUS(
hipsolver_potrf(
API, handle, hipsolverFillMode_t(-1), n, dA, lda, stA, dWork, lwork, dinfo, bc),
HIPSOLVER_STATUS_INVALID_ENUM);
#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
// pointers
EXPECT_ROCBLAS_STATUS(
hipsolver_potrf(API, handle, uplo, n, (T) nullptr, lda, stA, dWork, lwork, dinfo, bc),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(
hipsolver_potrf(API, handle, uplo, n, dA, lda, stA, dWork, lwork, (V) nullptr, bc),
HIPSOLVER_STATUS_INVALID_VALUE);
#endif
}
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
void testing_potrf_bad_arg()
{
// safe arguments
hipsolver_local_handle handle;
hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_UPPER;
int n = 1;
int lda = 1;
int stA = 1;
int bc = 1;
if(BATCHED)
{
// memory allocations
device_batch_vector<T> dA(1, 1, 1);
device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
CHECK_HIP_ERROR(dA.memcheck());
CHECK_HIP_ERROR(dinfo.memcheck());
int size_W;
hipsolver_potrf_bufferSize(API, handle, uplo, n, dA.data(), lda, &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
potrf_checkBadArgs<API>(
handle, uplo, n, dA.data(), lda, stA, dWork.data(), size_W, dinfo.data(), bc);
}
else
{
// memory allocations
device_strided_batch_vector<T> dA(1, 1, 1, 1);
device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
CHECK_HIP_ERROR(dA.memcheck());
CHECK_HIP_ERROR(dinfo.memcheck());
int size_W;
hipsolver_potrf_bufferSize(API, handle, uplo, n, dA.data(), lda, &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
potrf_checkBadArgs<API>(
handle, uplo, n, dA.data(), lda, stA, dWork.data(), size_W, dinfo.data(), bc);
}
}
template <bool CPU, bool GPU, typename T, typename Td, typename Ud, typename Th, typename Uh>
void potrf_initData(const hipsolverHandle_t handle,
const hipsolverFillMode_t uplo,
const int n,
Td& dA,
const int lda,
const int stA,
Ud& dInfo,
const int bc,
Th& hA,
Uh& hInfo)
{
if(CPU)
{
rocblas_init<T>(hA, true);
for(rocblas_int b = 0; b < bc; ++b)
{
// scale to ensure positive definiteness
for(rocblas_int i = 0; i < n; i++)
hA[b][i + i * lda] = hA[b][i + i * lda] * conj(hA[b][i + i * lda]) * 400;
}
}
if(GPU)
{
// now copy data to the GPU
CHECK_HIP_ERROR(dA.transfer_from(hA));
}
}
template <testAPI_t API,
typename T,
typename Td,
typename Ud,
typename Vd,
typename Th,
typename Uh>
void potrf_getError(const hipsolverHandle_t handle,
const hipsolverFillMode_t uplo,
const int n,
Td& dA,
const int lda,
const int stA,
Vd& dWork,
const int lwork,
Ud& dInfo,
const int bc,
Th& hA,
Th& hARes,
Uh& hInfo,
Uh& hInfoRes,
double* max_err)
{
// input data initialization
potrf_initData<true, true, T>(handle, uplo, n, dA, lda, stA, dInfo, bc, hA, hInfo);
// execute computations
// GPU lapack
CHECK_ROCBLAS_ERROR(hipsolver_potrf(
API, handle, uplo, n, dA.data(), lda, stA, dWork.data(), lwork, dInfo.data(), bc));
CHECK_HIP_ERROR(hARes.transfer_from(dA));
CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
// CPU lapack
for(int b = 0; b < bc; ++b)
cpu_potrf(uplo, n, hA[b], lda, hInfo[b]);
// error is ||hA - hARes|| / ||hA|| (ideally ||LL' - Lres Lres'|| / ||LL'||)
// (THIS DOES NOT ACCOUNT FOR NUMERICAL REPRODUCIBILITY ISSUES.
// IT MIGHT BE REVISITED IN THE FUTURE)
// using frobenius norm
double err;
int nn;
*max_err = 0;
for(int b = 0; b < bc; ++b)
{
nn = hInfoRes[b][0] == 0 ? n : hInfoRes[b][0];
// (TODO: For now, the algorithm is modifying the whole input matrix even when
// it is not positive definite. So we only check the principal nn-by-nn submatrix.
// Once this is corrected, nn could be always equal to n.)
if(uplo == HIPSOLVER_FILL_MODE_UPPER)
err = norm_error_upperTr('F', nn, nn, lda, hA[b], hARes[b]);
else
err = norm_error_lowerTr('F', nn, nn, lda, hA[b], hARes[b]);
*max_err = err > *max_err ? err : *max_err;
}
// also check info for non positive definite cases
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])
err++;
}
*max_err += err;
}
template <testAPI_t API,
typename T,
typename Td,
typename Ud,
typename Vd,
typename Th,
typename Uh>
void potrf_getPerfData(const hipsolverHandle_t handle,
const hipsolverFillMode_t uplo,
const int n,
Td& dA,
const int lda,
const int stA,
Vd& dWork,
const int lwork,
Ud& dInfo,
const int bc,
Th& hA,
Uh& hInfo,
double* gpu_time_used,
double* cpu_time_used,
const int hot_calls,
const bool perf)
{
if(!perf)
{
potrf_initData<true, false, T>(handle, uplo, n, dA, lda, stA, dInfo, bc, hA, hInfo);
// cpu-lapack performance (only if not in perf mode)
*cpu_time_used = get_time_us_no_sync();
for(int b = 0; b < bc; ++b)
cpu_potrf(uplo, n, hA[b], lda, hInfo[b]);
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
}
potrf_initData<true, false, T>(handle, uplo, n, dA, lda, stA, dInfo, bc, hA, hInfo);
// cold calls
for(int iter = 0; iter < 2; iter++)
{
potrf_initData<false, true, T>(handle, uplo, n, dA, lda, stA, dInfo, bc, hA, hInfo);
CHECK_ROCBLAS_ERROR(hipsolver_potrf(
API, handle, uplo, n, dA.data(), lda, stA, dWork.data(), lwork, dInfo.data(), bc));
}
// gpu-lapack performance
hipStream_t stream;
CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
double start;
for(int iter = 0; iter < hot_calls; iter++)
{
potrf_initData<false, true, T>(handle, uplo, n, dA, lda, stA, dInfo, bc, hA, hInfo);
start = get_time_us_sync(stream);
hipsolver_potrf(
API, handle, uplo, n, dA.data(), lda, stA, dWork.data(), lwork, dInfo.data(), 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_potrf(Arguments& argus)
{
// get arguments
hipsolver_local_handle handle;
char uploC = argus.get<char>("uplo");
int n = argus.get<int>("n");
int lda = argus.get<int>("lda", n);
int stA = argus.get<int>("strideA", lda * n);
int bc = argus.batch_count;
hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
int hot_calls = argus.iters;
rocblas_stride stARes = (argus.unit_check || argus.norm_check) ? stA : 0;
// check non-supported values
if(uplo != HIPSOLVER_FILL_MODE_UPPER && uplo != HIPSOLVER_FILL_MODE_LOWER)
{
if(BATCHED)
{
EXPECT_ROCBLAS_STATUS(hipsolver_potrf(API,
handle,
uplo,
n,
(T**)nullptr,
lda,
stA,
(T*)nullptr,
0,
(int*)nullptr,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
}
else
{
EXPECT_ROCBLAS_STATUS(
hipsolver_potrf(
API, handle, uplo, n, (T*)nullptr, lda, stA, (T*)nullptr, 0, (int*)nullptr, bc),
HIPSOLVER_STATUS_INVALID_VALUE);
}
if(argus.timing)
rocsolver_bench_inform(inform_invalid_args);
return;
}
// determine sizes
size_t size_A = size_t(lda) * n;
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0;
// check invalid sizes
bool invalid_size = (n < 0 || lda < n || bc < 0);
if(invalid_size)
{
#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
if(BATCHED)
{
EXPECT_ROCBLAS_STATUS(hipsolver_potrf(API,
handle,
uplo,
n,
(T**)nullptr,
lda,
stA,
(T*)nullptr,
0,
(int*)nullptr,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
}
else
{
EXPECT_ROCBLAS_STATUS(
hipsolver_potrf(
API, handle, uplo, n, (T*)nullptr, lda, stA, (T*)nullptr, 0, (int*)nullptr, bc),
HIPSOLVER_STATUS_INVALID_VALUE);
}
#endif
if(argus.timing)
rocsolver_bench_inform(inform_invalid_size);
return;
}
// memory size query is necessary
int size_W;
if(BATCHED)
hipsolver_potrf_bufferSize(API, handle, uplo, n, (T**)nullptr, lda, &size_W, bc);
else
hipsolver_potrf_bufferSize(API, handle, uplo, n, (T*)nullptr, lda, &size_W, bc);
if(argus.mem_query)
{
rocsolver_bench_inform(inform_mem_query, size_W);
return;
}
if(BATCHED)
{
// memory allocations
host_batch_vector<T> hA(size_A, 1, bc);
host_batch_vector<T> hARes(size_ARes, 1, bc);
host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
device_batch_vector<T> dA(size_A, 1, 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_A)
CHECK_HIP_ERROR(dA.memcheck());
CHECK_HIP_ERROR(dInfo.memcheck());
if(size_W)
CHECK_HIP_ERROR(dWork.memcheck());
// check computations
if(argus.unit_check || argus.norm_check)
potrf_getError<API, T>(handle,
uplo,
n,
dA,
lda,
stA,
dWork,
size_W,
dInfo,
bc,
hA,
hARes,
hInfo,
hInfoRes,
&max_error);
// collect performance data
if(argus.timing)
potrf_getPerfData<API, T>(handle,
uplo,
n,
dA,
lda,
stA,
dWork,
size_W,
dInfo,
bc,
hA,
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);
host_strided_batch_vector<T> hARes(size_ARes, 1, stARes, bc);
host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
device_strided_batch_vector<T> dA(size_A, 1, stA, 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_A)
CHECK_HIP_ERROR(dA.memcheck());
CHECK_HIP_ERROR(dInfo.memcheck());
if(size_W)
CHECK_HIP_ERROR(dWork.memcheck());
// check computations
if(argus.unit_check || argus.norm_check)
potrf_getError<API, T>(handle,
uplo,
n,
dA,
lda,
stA,
dWork,
size_W,
dInfo,
bc,
hA,
hARes,
hInfo,
hInfoRes,
&max_error);
// collect performance data
if(argus.timing)
potrf_getPerfData<API, T>(handle,
uplo,
n,
dA,
lda,
stA,
dWork,
size_W,
dInfo,
bc,
hA,
hInfo,
&gpu_time_used,
&cpu_time_used,
hot_calls,
argus.perf);
}
// validate results for rocsolver-test
// using n * machine_precision as tolerance
if(argus.unit_check)
ROCSOLVER_TEST_CHECK(T, max_error, n);
// output results for rocsolver-bench
if(argus.timing)
{
if(!argus.perf)
{
std::cerr << "\n============================================\n";
std::cerr << "Arguments:\n";
std::cerr << "============================================\n";
if(BATCHED)
{
rocsolver_bench_output("uplo", "n", "lda", "batch_c");
rocsolver_bench_output(uploC, n, lda, bc);
}
else if(STRIDED)
{
rocsolver_bench_output("uplo", "n", "lda", "strideA", "batch_c");
rocsolver_bench_output(uploC, n, lda, stA, bc);
}
else
{
rocsolver_bench_output("uplo", "n", "lda");
rocsolver_bench_output(uploC, n, lda);
}
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);
}
}
}
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