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2025-05-01 10:12:08 +00:00

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/*
* Copyright (c) 2025 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 "utils/Utils.h"
using namespace arm_compute;
using namespace utils;
class NEMatMulExample : public Example
{
public:
bool do_setup(int argc, char **argv) override
{
size_t m = 4096;
size_t n = 4096;
size_t k = 128;
if (argc == 4)
{
try
{
m = std::stoul(argv[1]);
n = std::stoul(argv[2]);
k = std::stoul(argv[3]);
}
catch (const std::exception &e)
{
ARM_COMPUTE_ERROR(e.what());
return false;
}
}
else if (argc != 1)
{
ARM_COMPUTE_ERROR("Invalid number of arguments. Usage:\n"
"<M> <N> <K>\n");
return false;
}
const TensorInfo a_info{TensorShape{k, m}, 1, DataType::F32, DataLayout::NHWC};
const TensorInfo b_info{TensorShape{n, k}, 1, DataType::F32, DataLayout::NHWC};
const TensorInfo output_info{TensorShape{n, m}, 1, DataType::F32, DataLayout::NHWC};
a.allocator()->init(a_info);
b.allocator()->init(b_info);
output.allocator()->init(output_info);
a.info()->set_are_values_constant(false);
b.info()->set_are_values_constant(false);
output.info()->set_are_values_constant(false);
const MatMulInfo info;
const CpuMatMulSettings settings;
auto status = NEMatMul::validate(a.info(), b.info(), output.info(), info, settings);
if (status.error_code() != ErrorCode::OK)
{
ARM_COMPUTE_ERROR(status.error_description().c_str());
return false;
}
matmul.configure(&a, &b, &output, info, settings);
a.allocator()->allocate();
b.allocator()->allocate();
output.allocator()->allocate();
// Fill with fixed values
const std::vector<float> values_a(m * k, 2.2f);
const std::vector<float> values_b(n * k, 3.5f);
fill_tensor_vector(a, values_a);
fill_tensor_vector(b, values_b);
return true;
}
void do_run() override
{
matmul.run();
}
private:
NEMatMul matmul{};
Tensor a{}, b{}, output{};
};
/** Main program for MatMul test
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments (M, N, K)
*/
int main(int argc, char **argv)
{
return utils::run_example<NEMatMulExample>(argc, argv);
}