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TheRock/rocm-libraries/projects/hipsolver/clients/include/testing_syev_heev.hpp
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Flatten subprojects into a single base monorepo
2026-09-02 16:34:47 -07:00

743 lines
30 KiB
C++

/* ************************************************************************
* Copyright (C) 2024-2026 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 S, typename U>
void syev_heev_checkBadArgs(const hipsolverHandle_t handle,
const hipsolverDnParams_t params,
const hipsolverEigMode_t jobz,
const hipsolverFillMode_t uplo,
const int64_t n,
T dA,
const int64_t lda,
S dW,
T dWork,
const size_t lworkOnDevice,
T hWork,
const size_t lworkOnHost,
U dinfo,
const int64_t batchSize)
{
// handle
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
nullptr,
params,
jobz,
uplo,
n,
dA,
lda,
dW,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
dinfo,
batchSize),
HIPSOLVER_STATUS_NOT_INITIALIZED);
// values
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
hipsolverEigMode_t(-1),
uplo,
n,
dA,
lda,
dW,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
dinfo,
batchSize),
HIPSOLVER_STATUS_INVALID_ENUM);
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
jobz,
hipsolverFillMode_t(-1),
n,
dA,
lda,
dW,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
dinfo,
batchSize),
HIPSOLVER_STATUS_INVALID_ENUM);
#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
// pointers
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
(hipsolverDnParams_t) nullptr,
jobz,
uplo,
n,
dA,
lda,
dW,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
dinfo,
batchSize),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
jobz,
uplo,
n,
(T) nullptr,
lda,
dW,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
dinfo,
batchSize),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
jobz,
uplo,
n,
dA,
lda,
(S) nullptr,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
dinfo,
batchSize),
HIPSOLVER_STATUS_INVALID_VALUE);
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
jobz,
uplo,
n,
dA,
lda,
dW,
dWork,
lworkOnDevice,
hWork,
lworkOnHost,
(U) nullptr,
batchSize),
HIPSOLVER_STATUS_INVALID_VALUE);
#endif
}
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T, typename I, typename SIZE>
void testing_syev_heev_bad_arg()
{
using S = decltype(std::real(T{}));
// safe arguments
hipsolver_local_handle handle;
hipsolver_local_params params;
hipsolverEigMode_t jobz = HIPSOLVER_EIG_MODE_NOVECTOR;
hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_LOWER;
int64_t n = 1;
int64_t lda = 1;
int64_t batchSize = 1;
// memory allocations
device_strided_batch_vector<T> dA(1, 1, 1, 1);
device_strided_batch_vector<S> dW(1, 1, 1, 1);
device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
CHECK_HIP_ERROR(dA.memcheck());
CHECK_HIP_ERROR(dW.memcheck());
CHECK_HIP_ERROR(dinfo.memcheck());
SIZE size_dW, size_hW;
hipsolver_syev_heev_bufferSize(API,
STRIDED,
handle,
params,
jobz,
uplo,
n,
dA.data(),
lda,
dW.data(),
&size_dW,
&size_hW,
batchSize);
host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1);
if(size_dW)
CHECK_HIP_ERROR(dWork.memcheck());
// check bad arguments
syev_heev_checkBadArgs<API, STRIDED>(handle,
params,
jobz,
uplo,
n,
dA.data(),
lda,
dW.data(),
dWork.data(),
size_dW,
hWork.data(),
size_hW,
dinfo.data(),
batchSize);
}
template <bool CPU, bool GPU, typename T, typename Td, typename Th>
void syev_heev_initData(const hipsolverHandle_t handle,
const hipsolverEigMode_t evect,
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);
// scale A to avoid singularities
for(int b = 0; b < bc; ++b)
{
for(int i = 0; i < n; i++)
{
for(int j = 0; j < n; j++)
{
if(i == j)
hA[b][i + j * lda] = std::real(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 && evect == HIPSOLVER_EIG_MODE_VECTOR)
{
for(int i = 0; i < n; 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 I,
typename SIZE,
typename Sd,
typename Td,
typename Id,
typename Sh,
typename Th,
typename Ih>
void syev_heev_getError(const hipsolverHandle_t handle,
const hipsolverDnParams_t params,
const hipsolverEigMode_t evect,
const hipsolverFillMode_t uplo,
const I n,
Td& dA,
const I lda,
Sd& dW,
Td& dWork,
const SIZE lworkOnDevice,
Th& hWork,
const SIZE lworkOnHost,
Id& dinfo,
const int bc,
Th& hA,
Th& hAres,
Sh& hW,
Sh& hWres,
Ih& hinfo,
Ih& hinfoRes,
double* max_err)
{
constexpr bool COMPLEX = is_complex<T>;
using S = decltype(std::real(T{}));
I lrwork, ltwork;
if(!COMPLEX)
{
ltwork = std::max(I(1), 3 * n - 1);
lrwork = 0;
}
else
{
lrwork = std::max(I(1), 3 * n - 2);
ltwork = std::max(I(1), 2 * n - 1);
}
std::vector<T> work(ltwork);
std::vector<S> hE(lrwork);
std::vector<T> A(lda * n * bc);
// input data initialization
syev_heev_initData<true, true, T>(handle, evect, n, dA, lda, bc, hA, A);
// execute computations
// GPU lapack
CHECK_ROCBLAS_ERROR(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
evect,
uplo,
n,
dA.data(),
lda,
dW.data(),
dWork.data(),
lworkOnDevice,
hWork.data(),
lworkOnHost,
dinfo.data(),
bc));
CHECK_HIP_ERROR(hWres.transfer_from(dW));
CHECK_HIP_ERROR(hinfoRes.transfer_from(dinfo));
if(evect == HIPSOLVER_EIG_MODE_VECTOR)
CHECK_HIP_ERROR(hAres.transfer_from(dA));
// CPU lapack
for(int b = 0; b < bc; ++b)
cpu_syev_heev(evect, uplo, n, hA[b], lda, hW[b], work.data(), ltwork, 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 = 0;
for(int b = 0; b < bc; ++b)
{
if(evect != HIPSOLVER_EIG_MODE_VECTOR)
{
// only eigenvalues needed; can compare with LAPACK
// error is ||hW - hWRes|| / ||hW||
// using frobenius norm
if(hinfo[b][0] == 0)
{
err = norm_error('F', 1, n, 1, hW[b], hWres[b]);
}
*max_err = err > *max_err ? err : *max_err;
}
else
{
// both eigenvalues and eigenvectors needed; need to implicitly test
// eigenvectors due to non-uniqueness of eigenvectors under scaling
if(hinfo[b][0] == 0)
{
// multiply A with each of the n eigenvectors and divide by corresponding
// eigenvalues
T alpha;
T beta = 0;
for(int j = 0; j < n; j++)
{
alpha = T(1) / hWres[b][j];
cpu_symv_hemv(uplo,
n,
alpha,
A.data() + b * lda * n,
lda,
hAres[b] + j * lda,
1,
beta,
hA[b] + j * lda,
1);
}
// error is ||hA - hARes|| / ||hA||
// using frobenius norm
err = norm_error('F', n, n, lda, hA[b], hAres[b]);
*max_err = err > *max_err ? err : *max_err;
}
}
}
}
template <testAPI_t API,
bool STRIDED,
typename T,
typename I,
typename SIZE,
typename Sd,
typename Td,
typename Id,
typename Sh,
typename Th,
typename Ih>
void syev_heev_getPerfData(const hipsolverHandle_t handle,
const hipsolverDnParams_t params,
const hipsolverEigMode_t evect,
const hipsolverFillMode_t uplo,
const I n,
Td& dA,
const I lda,
Sd& dW,
Td& dWork,
const SIZE lworkOnDevice,
Th& hWork,
const SIZE lworkOnHost,
Id& dinfo,
const int bc,
Th& hA,
Sh& hW,
Ih& hinfo,
double* gpu_time_used,
double* cpu_time_used,
const int hot_calls,
const bool perf)
{
constexpr bool COMPLEX = is_complex<T>;
using S = decltype(std::real(T{}));
I lrwork, ltwork;
if(!COMPLEX)
{
ltwork = std::max(I(1), 3 * n - 1);
lrwork = 0;
}
else
{
lrwork = std::max(I(1), 3 * n - 2);
ltwork = std::max(I(1), 2 * n - 1);
}
std::vector<T> work(ltwork);
std::vector<S> hE(lrwork);
std::vector<T> A;
if(!perf)
{
syev_heev_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
// 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_syev_heev(
evect, uplo, n, hA[b], lda, hW[b], work.data(), ltwork, hE.data(), hinfo[b]);
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
}
syev_heev_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
// cold calls
for(int iter = 0; iter < 2; iter++)
{
syev_heev_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
CHECK_ROCBLAS_ERROR(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
evect,
uplo,
n,
dA.data(),
lda,
dW.data(),
dWork.data(),
lworkOnDevice,
hWork.data(),
lworkOnHost,
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++)
{
syev_heev_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
start = get_time_us_sync(stream);
hipsolver_syev_heev(API,
STRIDED,
handle,
params,
evect,
uplo,
n,
dA.data(),
lda,
dW.data(),
dWork.data(),
lworkOnDevice,
hWork.data(),
lworkOnHost,
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,
typename I = int64_t,
typename SIZE = size_t>
void testing_syev_heev(Arguments& argus)
{
using S = decltype(std::real(T{}));
// get arguments
hipsolver_local_handle handle;
hipsolver_local_params params;
char evectC = argus.get<char>("jobz");
char uploC = argus.get<char>("uplo");
I n = argus.get<int>("n");
I lda = argus.get<int>("lda", n);
I stA = lda * n;
I stW = n;
hipsolverEigMode_t evect = char2hipsolver_evect(evectC);
hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
int bc = argus.batch_count;
int hot_calls = argus.iters;
// determine sizes
size_t size_A = size_t(lda) * n;
size_t size_W = n;
size_t size_Ares = (argus.unit_check || argus.norm_check) ? size_A : 0;
size_t size_Wres = (argus.unit_check || argus.norm_check) ? size_W : 0;
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
// check invalid sizes
bool invalid_size = (n < 0 || lda < n || bc < 0);
if(invalid_size)
{
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
STRIDED,
handle,
params,
evect,
uplo,
n,
(T*)nullptr,
lda,
(S*)nullptr,
(T*)nullptr,
(SIZE)0,
(T*)nullptr,
(SIZE)0,
(int*)nullptr,
bc),
HIPSOLVER_STATUS_INVALID_VALUE);
if(argus.timing)
rocsolver_bench_inform(inform_invalid_size);
return;
}
// memory size query is necessary
SIZE size_dW, size_hW;
hipsolver_syev_heev_bufferSize(API,
STRIDED,
handle,
params,
evect,
uplo,
n,
(T*)nullptr,
lda,
(S*)nullptr,
&size_dW,
&size_hW,
bc);
if(argus.mem_query)
{
rocsolver_bench_inform(inform_mem_query, size_dW);
return;
}
// memory allocations (all cases)
// host
host_strided_batch_vector<S> hW(size_W, 1, stW, 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> hWres(size_Wres, 1, stW, bc);
// device
device_strided_batch_vector<S> dW(size_W, 1, stW, bc);
device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
if(size_W)
CHECK_HIP_ERROR(dW.memcheck());
CHECK_HIP_ERROR(dinfo.memcheck());
// memory allocations
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
host_strided_batch_vector<T> hAres(size_Ares, 1, stA, bc);
host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1); // size_hW accounts for bc
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1); // size_dW accounts for bc
if(size_A)
CHECK_HIP_ERROR(dA.memcheck());
if(size_dW)
CHECK_HIP_ERROR(dWork.memcheck());
// check computations
if(argus.unit_check || argus.norm_check)
{
syev_heev_getError<API, STRIDED, T>(handle,
params,
evect,
uplo,
n,
dA,
lda,
dW,
dWork,
size_dW,
hWork,
size_hW,
dinfo,
bc,
hA,
hAres,
hW,
hWres,
hinfo,
hinfoRes,
&max_error);
}
// collect performance data
if(argus.timing)
{
syev_heev_getPerfData<API, STRIDED, T>(handle,
params,
evect,
uplo,
n,
dA,
lda,
dW,
dWork,
size_dW,
hWork,
size_hW,
dinfo,
bc,
hA,
hW,
hinfo,
&gpu_time_used,
&cpu_time_used,
hot_calls,
argus.perf);
}
// validate results for rocsolver-test
// using 4 * n * machine_precision as tolerance
if(argus.unit_check)
ROCSOLVER_TEST_CHECK(T, max_error, 4 * n);
// output results for rocsolver-bench
if(argus.timing)
{
if(!argus.perf)
{
std::cerr << "\n============================================\n";
std::cerr << "Arguments:\n";
std::cerr << "============================================\n";
rocsolver_bench_output("jobz", "uplo", "n", "lda", "batch_c");
rocsolver_bench_output(evectC, uploC, n, lda, bc);
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();
}