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/*
* Copyright (c) 2018-2019 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* 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 "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "utils/Utils.h"
#include <cstdlib>
using namespace arm_compute;
using namespace utils;
class NESGEMMExample : public Example
{
public:
bool do_setup(int argc, char **argv) override
{
NPYLoader npy0;
NPYLoader npy1;
NPYLoader npy2;
alpha = 1.0f;
beta = 0.0f;
std::ifstream stream;
if (argc > 1)
{
stream.open(argv[1], std::fstream::in);
}
if (argc < 3 || (argc < 4 && stream.bad()))
{
// Print help
std::cout << "Usage: 1) ./build/neon_sgemm input_matrix_1.npy input_matrix_2.npy [input_matrix_3.npy] "
"[alpha = 1] [beta = 0]\n";
std::cout << " 2) ./build/neon_sgemm M N K [alpha = 1.0f] [beta = 0.0f]\n\n";
std::cout << "Too few or no input_matrices provided. Using M=7, N=3, K=5, alpha=1.0f and beta=0.0f\n\n";
src0.allocator()->init(TensorInfo(TensorShape(5U, 7U), 1, DataType::F32));
src1.allocator()->init(TensorInfo(TensorShape(3U, 5U), 1, DataType::F32));
src2.allocator()->init(TensorInfo(TensorShape(3U, 7U), 1, DataType::F32));
}
else
{
if (stream.good()) /* case file1.npy file2.npy [file3.npy] [alpha = 1.0f] [beta = 0.0f] */
{
npy0.open(argv[1]);
npy0.init_tensor(src0, DataType::F32);
npy1.open(argv[2]);
npy1.init_tensor(src1, DataType::F32);
if (argc > 3)
{
stream.close();
stream.clear();
stream.open(argv[3], std::fstream::in);
if (stream.good()) /* case with third file */
{
npy2.open(argv[3]);
npy2.init_tensor(src2, DataType::F32);
if (argc > 4)
{
// Convert string to float
alpha = strtof(argv[4], nullptr);
if (argc > 5)
{
// Convert string to float
beta = strtof(argv[5], nullptr);
}
}
}
else /* case without third file */
{
alpha = strtof(argv[3], nullptr);
if (argc > 4)
{
beta = strtof(argv[4], nullptr);
}
}
}
}
else /* case M N K [alpha = 1.0f] [beta = 0.0f] */
{
size_t M = strtol(argv[1], nullptr, 10);
size_t N = strtol(argv[2], nullptr, 10);
size_t K = strtol(argv[3], nullptr, 10);
src0.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
src1.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
src2.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
if (argc > 4)
{
alpha = strtof(argv[4], nullptr);
if (argc > 5)
{
beta = strtof(argv[5], nullptr);
}
}
}
}
init_sgemm_output(dst, src0, src1, DataType::F32);
// Configure function
sgemm.configure(&src0, &src1, nullptr, &dst, alpha, beta);
// Allocate all the images
src0.allocator()->allocate();
src1.allocator()->allocate();
dst.allocator()->allocate();
// Fill the input images with either the data provided or random data
if (npy0.is_open())
{
npy0.fill_tensor(src0);
npy1.fill_tensor(src1);
output_filename = "sgemm_out.npy";
is_fortran = npy0.is_fortran();
if (npy2.is_open())
{
src2.allocator()->allocate();
npy2.fill_tensor(src2);
}
}
else
{
src2.allocator()->allocate();
fill_random_tensor(src0, -1.f, 1.f);
fill_random_tensor(src1, -1.f, 1.f);
fill_random_tensor(src2, -1.f, 1.f);
}
// Dummy run for CLTuner
sgemm.run();
return true;
}
void do_run() override
{
// Execute the function
sgemm.run();
}
void do_teardown() override
{
if (!output_filename.empty()) /* Save to .npy file */
{
save_to_npy(dst, output_filename, is_fortran);
}
}
private:
Tensor src0{}, src1{}, src2{}, dst{};
NEGEMM sgemm{};
float alpha{}, beta{};
bool is_fortran{};
std::string output_filename{};
};
/** Main program for sgemm test
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Matrix A, [optional] Matrix B, [optional] Matrix C, [optional] alpha, [optional] beta )
*/
int main(int argc, char **argv)
{
return utils::run_example<NESGEMMExample>(argc, argv);
}
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