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TheRock/rocm-libraries/projects/hipblas/clients/include/blas1/testing_asum_batched.hpp
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derek.holloway 7502bf64a4
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Flatten subprojects into a single base monorepo
2026-09-02 16:34:47 -07:00

230 lines
9.3 KiB
C++

/* ************************************************************************
* Copyright (C) 2016-2024 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
* copies 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
* IMPLIED, 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 CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
* ************************************************************************ */
#include <stdio.h>
#include <stdlib.h>
#include <vector>
#include "testing_common.hpp"
/* ============================================================================================ */
using hipblasAsumBatchedModel = ArgumentModel<e_a_type, e_N, e_incx, e_batch_count>;
inline void testname_asum_batched(const Arguments& arg, std::string& name)
{
hipblasAsumBatchedModel{}.test_name(arg, name);
}
template <typename T>
void testing_asum_batched_bad_arg(const Arguments& arg)
{
using Tr = real_t<T>;
bool FORTRAN = arg.api == hipblas_client_api::FORTRAN;
auto hipblasAsumBatchedFn
= FORTRAN ? hipblasAsumBatched<T, Tr, true> : hipblasAsumBatched<T, Tr, false>;
auto hipblasAsumBatchedFn_64 = arg.api == FORTRAN_64 ? hipblasAsumBatched_64<T, Tr, true>
: hipblasAsumBatched_64<T, Tr, false>;
for(auto pointer_mode : {HIPBLAS_POINTER_MODE_HOST, HIPBLAS_POINTER_MODE_DEVICE})
{
hipblasLocalHandle handle(arg);
CHECK_HIPBLAS_ERROR(hipblasSetPointerMode(handle, pointer_mode));
int64_t N = 100;
int64_t incx = 1;
int64_t batch_count = 2;
// Host-side result invalid for device mode, but shouldn't matter for bad-arg test cases
Tr res = 10;
device_batch_vector<T> dx(N, incx, batch_count);
DAPI_EXPECT(HIPBLAS_STATUS_NOT_INITIALIZED,
hipblasAsumBatchedFn,
(nullptr, N, dx, incx, batch_count, &res));
DAPI_EXPECT(HIPBLAS_STATUS_INVALID_VALUE,
hipblasAsumBatchedFn,
(handle, N, nullptr, incx, batch_count, &res));
DAPI_EXPECT(HIPBLAS_STATUS_INVALID_VALUE,
hipblasAsumBatchedFn,
(handle, N, dx, incx, batch_count, nullptr));
}
}
template <typename T>
void testing_asum_batched(const Arguments& arg)
{
using Tr = real_t<T>;
bool FORTRAN = arg.api == hipblas_client_api::FORTRAN;
auto hipblasAsumBatchedFn
= FORTRAN ? hipblasAsumBatched<T, Tr, true> : hipblasAsumBatched<T, Tr, false>;
auto hipblasAsumBatchedFn_64 = arg.api == FORTRAN_64 ? hipblasAsumBatched_64<T, Tr, true>
: hipblasAsumBatched_64<T, Tr, false>;
int64_t N = arg.N;
int64_t incx = arg.incx;
int64_t batch_count = arg.batch_count;
hipblasLocalHandle handle(arg);
// check to prevent undefined memory allocation error
if(N <= 0 || incx <= 0 || batch_count <= 0)
{
int64_t batches = std::max(int64_t(1), batch_count);
device_vector<Tr> d_hipblas_result_0(batches);
host_vector<Tr> h_hipblas_result_0(batches);
hipblas_init_nan(h_hipblas_result_0.data(), batches);
CHECK_HIP_ERROR(hipMemcpy(
d_hipblas_result_0, h_hipblas_result_0, sizeof(Tr) * batches, hipMemcpyHostToDevice));
CHECK_HIPBLAS_ERROR(hipblasSetPointerMode(handle, HIPBLAS_POINTER_MODE_DEVICE));
DAPI_CHECK(hipblasAsumBatchedFn,
(handle, N, nullptr, incx, batch_count, d_hipblas_result_0));
if(batch_count > 0)
{
host_vector<Tr> cpu_0(batch_count);
host_vector<Tr> gpu_0(batch_count);
CHECK_HIP_ERROR(hipMemcpy(
gpu_0, d_hipblas_result_0, sizeof(Tr) * batch_count, hipMemcpyDeviceToHost));
unit_check_general<Tr>(1, batch_count, 1, cpu_0, gpu_0);
}
return;
}
double gpu_time_used{0}, hipblas_error_host{0}, hipblas_error_device{0};
// Naming: dX is in GPU (device) memory. hK is in CPU (host) memory, plz follow this practice
host_batch_vector<T> hx(N, incx, batch_count);
host_vector<Tr> h_hipblas_result_host(batch_count);
host_vector<Tr> h_hipblas_result_device(batch_count);
host_vector<Tr> h_cpu_result(batch_count);
device_batch_vector<T> dx(N, incx, batch_count);
device_vector<Tr> d_hipblas_result(batch_count);
CHECK_DEVICE_ALLOCATION(dx.memcheck());
CHECK_DEVICE_ALLOCATION(d_hipblas_result.memcheck());
// Initial Data on CPU
hipblas_init_vector(hx, arg, hipblas_client_alpha_sets_nan, true);
// copy data from CPU to device
CHECK_HIP_ERROR(dx.transfer_from(hx));
/* =====================================================================
HIPBLAS
=================================================================== */
if(arg.unit_check || arg.norm_check)
{
// hipblasAsum accept both dev/host pointer for the scalar
CHECK_HIPBLAS_ERROR(hipblasSetPointerMode(handle, HIPBLAS_POINTER_MODE_DEVICE));
DAPI_CHECK(hipblasAsumBatchedFn,
(handle, N, dx.ptr_on_device(), incx, batch_count, d_hipblas_result));
CHECK_HIPBLAS_ERROR(hipblasSetPointerMode(handle, HIPBLAS_POINTER_MODE_HOST));
DAPI_CHECK(hipblasAsumBatchedFn,
(handle, N, dx.ptr_on_device(), incx, batch_count, h_hipblas_result_host));
CHECK_HIP_ERROR(hipMemcpy(h_hipblas_result_device,
d_hipblas_result,
sizeof(Tr) * batch_count,
hipMemcpyDeviceToHost));
/* =====================================================================
CPU BLAS
=================================================================== */
for(int64_t b = 0; b < batch_count; b++)
{
ref_asum<T>(N, hx[b], incx, &(h_cpu_result[b]));
}
bool near_check = arg.initialization == hipblas_initialization::hpl;
Tr abs_error = hipblas_type_epsilon<Tr> * h_cpu_result[0];
Tr tolerance = 20.0;
abs_error *= tolerance;
if(arg.unit_check)
{
if(near_check)
{
near_check_general<Tr>(batch_count,
1,
1,
h_cpu_result.data(),
h_hipblas_result_host.data(),
abs_error);
near_check_general<Tr>(batch_count,
1,
1,
h_cpu_result.data(),
h_hipblas_result_device.data(),
abs_error);
}
else
{
unit_check_general<Tr>(1, batch_count, 1, h_cpu_result, h_hipblas_result_host);
unit_check_general<Tr>(1, batch_count, 1, h_cpu_result, h_hipblas_result_device);
}
}
if(arg.norm_check)
{
hipblas_error_host = norm_check_general<Tr>(
'F', 1, batch_count, 1, h_cpu_result, h_hipblas_result_host);
hipblas_error_device = norm_check_general<Tr>(
'F', 1, batch_count, 1, h_cpu_result, h_hipblas_result_device);
}
} // end of if unit/norm check
if(arg.timing)
{
hipStream_t stream;
CHECK_HIPBLAS_ERROR(hipblasGetStream(handle, &stream));
CHECK_HIPBLAS_ERROR(hipblasSetPointerMode(handle, HIPBLAS_POINTER_MODE_DEVICE));
int runs = arg.cold_iters + arg.iters;
for(int iter = 0; iter < runs; iter++)
{
if(iter == arg.cold_iters)
gpu_time_used = get_time_us_sync(stream);
DAPI_CHECK(hipblasAsumBatchedFn,
(handle, N, dx.ptr_on_device(), incx, batch_count, d_hipblas_result));
}
gpu_time_used = get_time_us_sync(stream) - gpu_time_used;
hipblasAsumBatchedModel{}.log_args<T>(std::cout,
arg,
gpu_time_used,
asum_gflop_count<T>(N),
asum_gbyte_count<T>(N),
hipblas_error_host,
hipblas_error_device);
}
}