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/*
* Copyright (c) 2017-2018 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.
*/
#ifndef ARM_COMPUTE_TEST_GEMM_FIXTURE
#define ARM_COMPUTE_TEST_GEMM_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/GEMM.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class GEMMValidationFixedPointFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, TensorShape output_shape, float alpha, float beta, DataType data_type, int fractional_bits)
{
_fractional_bits = fractional_bits;
_data_type = data_type;
_target = compute_target(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type, fractional_bits);
_reference = compute_reference(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type, fractional_bits);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
switch(tensor.data_type())
{
case DataType::F16:
case DataType::F32:
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
break;
}
default:
library->fill_tensor_uniform(tensor, i);
}
}
TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta,
DataType data_type, int fixed_point_position)
{
// Create tensors
TensorType a = create_tensor<TensorType>(shape_a, data_type, 1, fixed_point_position);
TensorType b = create_tensor<TensorType>(shape_b, data_type, 1, fixed_point_position);
TensorType c = create_tensor<TensorType>(shape_c, data_type, 1, fixed_point_position);
TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
// Create and configure function
FunctionType gemm;
// The GEMMinfo includes the values of the depth in case of reinterpreted 3d output.
// If the output shape has the same number of dimensions of the input the method called is a 2D matrix multiplication (depth_output_reinterpreted_as_3D = 1),
// in the other case we have to use the reinterpreted version of GEMM (depth_output_reinterpreted_as_3D = depth of the 3D output).
bool is_output_reinterpreted_as_3D = output_shape.num_dimensions() > shape_a.num_dimensions();
gemm.configure(&a, &b, &c, &dst, alpha, beta,
GEMMInfo(false, false, false, is_output_reinterpreted_as_3D ? output_shape[2] : 1));
ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
a.allocator()->allocate();
b.allocator()->allocate();
c.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(a), 0);
fill(AccessorType(b), 1);
fill(AccessorType(c), 2);
// Compute GEMM function
gemm.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta,
DataType data_type, int fixed_point_position)
{
// Create reference
SimpleTensor<T> a{ shape_a, data_type, 1, fixed_point_position };
SimpleTensor<T> b{ shape_b, data_type, 1, fixed_point_position };
SimpleTensor<T> c{ shape_c, data_type, 1, fixed_point_position };
// Fill reference
fill(a, 0);
fill(b, 1);
fill(c, 2);
return reference::gemm<T>(a, b, c, alpha, beta);
}
TensorType _target{};
SimpleTensor<T> _reference{};
int _fractional_bits{};
DataType _data_type{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class GEMMValidationFixture : public GEMMValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, TensorShape output_shape, float alpha, float beta, DataType data_type)
{
GEMMValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type, 0);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_GEMM_FIXTURE */
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