tests/validation/fixtures/GEMMFixture.h

/*
 * 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
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 * 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, bool reinterpret_input_as_3d = false, bool reinterpret_ouput_as_3d = false>
class GEMMValidationFixture : 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)
    {
        _data_type = data_type;

        _target    = compute_target(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type);
        _reference = compute_reference(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type);
    }

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)
    {
        // Create tensors
        TensorType a   = create_tensor<TensorType>(shape_a, data_type, 1);
        TensorType b   = create_tensor<TensorType>(shape_b, data_type, 1);
        TensorType c   = create_tensor<TensorType>(shape_c, data_type, 1);
        TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);

        // 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).
        gemm.configure(&a, &b, &c, &dst, alpha, beta, GEMMInfo(false, false, false, (reinterpret_ouput_as_3d ? output_shape[2] : 1), reinterpret_input_as_3d));
        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)
    {
        TensorShape shape_a_to_use = shape_a;
        if(reinterpret_input_as_3d)
        {
            // Collapse the second and third dimension if the input is 3D
            shape_a_to_use.collapse(2U, 1U);
        }

        // Create reference
        SimpleTensor<T> a{ shape_a_to_use, data_type, 1 };
        SimpleTensor<T> b{ shape_b, data_type, 1 };
        SimpleTensor<T> c{ shape_c, data_type, 1 };

        // 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{};
    DataType        _data_type{};
};

} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_GEMM_FIXTURE */