tests/validation/fixtures/GEMMLowpFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2019 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.
  */
 #ifndef ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE
 #define ARM_COMPUTE_TEST_GEMMLOWP_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/GEMMLowp.h"

 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
 class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
     {
         _target    = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset);
         _reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
         std::uniform_int_distribution<> distribution(1, 254);
         library->fill(tensor, distribution, i);
     }

     TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
     {
         // Create tensors
         TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
         TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
         TensorType c = create_tensor<TensorType>(shape_c, DataType::S32, 1);

         a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
         b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));

         // Create and configure function
         // The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
         FunctionType gemmlowp;
         // TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
         gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 0), 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);

         // Allocate tensors
         a.allocator()->allocate();
         b.allocator()->allocate();
         c.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);

         // Fill tensors
         fill(AccessorType(a), 0);
         fill(AccessorType(b), 1);

         // Compute GEMM function
         gemmlowp.run();
         return c;
     }

     SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
     {
         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<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
         SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };

         // Fill reference
         fill(a, 0);
         fill(b, 1);

         return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_c, a_offset, b_offset);
     }

     TensorType            _target{};
     SimpleTensor<int32_t> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType>
 class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
     {
         _target    = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
         _reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         std::uniform_int_distribution<> distribution(-6000, 6000);
         library->fill(tensor, distribution, i);
     }

     TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
     {
         TensorShape shape_bias(shape[0]);

         // Create tensors
         TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
         TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
         TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);

         // Create and configure function
         FunctionType output_stage;
         output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max);

         ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         a.allocator()->allocate();
         c.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensor
         fill(AccessorType(a), 0);

         if(add_bias)
         {
             ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);

             // Allocate bias tensor
             b.allocator()->allocate();

             ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);

             // Fill tensor
             fill(AccessorType(b), 1);
         }

         // Compute GEMM function
         output_stage.run();
         return c;
     }

     SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
     {
         // Create reference
         TensorShape shape_bias(shape[0]);

         SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
         SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };

         // Fill reference
         fill(a, 0);

         if(add_bias)
         {
             // Fill bias
             fill(b, 1);

             return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, b, result_offset, result_mult_int, result_shift, min, max);
         }
         else
         {
             return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift, min, max);
         }
     }

     TensorType            _target{};
     SimpleTensor<uint8_t> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType>
 class GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
     {
         _target    = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
         _reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         std::uniform_int_distribution<> distribution(-6000, 6000);
         library->fill(tensor, distribution, i);
     }

     TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
     {
         TensorShape shape_bias(shape[0]);

         // Create tensors
         TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
         TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
         TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);

         // Create and configure function
         FunctionType output_stage;
         output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);

         ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         a.allocator()->allocate();
         c.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensor
         fill(AccessorType(a), 0);

         if(add_bias)
         {
             ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);

             // Allocate bias tensor
             b.allocator()->allocate();

             ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);

             // Fill tensor
             fill(AccessorType(b), 1);
         }

         // Compute GEMM function
         output_stage.run();
         return c;
     }

     SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max,
                                             bool add_bias)
     {
         // Create reference
         TensorShape shape_bias(shape[0]);

         SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
         SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };

         // Fill reference
         fill(a, 0);

         if(add_bias)
         {
             // Fill bias
             fill(b, 1);

             return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, b, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max);
         }
         else
         {
             return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max);
         }
     }

     TensorType            _target{};
     SimpleTensor<uint8_t> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
 class GEMMLowpMatrixMultiplyReshapedValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
                bool interleave_rhs)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0         = m0;
         lhs_info.k0         = k0;
         lhs_info.v0         = v0;
         lhs_info.interleave = interleave_lhs;
         lhs_info.transpose  = false;

         GEMMRHSMatrixInfo rhs_info;
         rhs_info.n0         = n0;
         rhs_info.k0         = k0;
         rhs_info.h0         = h0;
         rhs_info.interleave = interleave_rhs;
         rhs_info.transpose  = true;

         // Set the tensor shapes for LHS and RHS matrices
         const TensorShape lhs_shape(k, m, batch_size);
         const TensorShape rhs_shape(n, k, batch_size);

         _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info);
         _reference = compute_reference(lhs_shape, rhs_shape);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
         std::uniform_int_distribution<> distribution(1, 254);
         library->fill(tensor, distribution, i);
     }

     TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info)
     {
         // Create tensors
         TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
         TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
         TensorType lhs_reshaped;
         TensorType rhs_reshaped;
         TensorType dst;

         const unsigned int M = lhs_shape[1];
         const unsigned int N = rhs_shape[0];
         const unsigned int K = lhs_shape[0];

         // The output tensor will be auto-initialized within the function

         // Create and configure function
         ReshapeLHSFunctionType reshape_lhs;
         ReshapeRHSFunctionType reshape_rhs;
         GEMMFunctionType       gemm;
         reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
         reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
         gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));

         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
         lhs_reshaped.allocator()->allocate();
         rhs_reshaped.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);

         // Compute GEMM
         reshape_lhs.run();
         reshape_rhs.run();
         gemm.run();

         return dst;
     }

     SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape)
     {
         TensorShape dst_shape = lhs_shape;
         dst_shape[0]          = rhs_shape[0];
         dst_shape[1]          = lhs_shape[1];

         // Create reference
         SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
         SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };

         // Fill reference
         fill(lhs, 0);
         fill(rhs, 1);

         return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
     }

     TensorType            _target{};
     SimpleTensor<int32_t> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
 class GEMMLowpMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
                bool interleave_lhs, bool interleave_rhs)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0         = m0;
         lhs_info.k0         = k0;
         lhs_info.v0         = v0;
         lhs_info.interleave = interleave_lhs;
         lhs_info.transpose  = false;

         GEMMRHSMatrixInfo rhs_info;
         rhs_info.n0         = n0;
         rhs_info.k0         = k0;
         rhs_info.h0         = h0;
         rhs_info.interleave = interleave_rhs;
         rhs_info.transpose  = true;

         // In case of GEMM3D, m is the product between m_w and m_h
         const unsigned int m = m_w * m_h;

         // Set the tensor shapes for LHS and RHS matrices
         const TensorShape lhs_shape(k, m, batch_size);
         const TensorShape rhs_shape(n, k, batch_size);

         _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h);
         _reference = compute_reference(lhs_shape, rhs_shape, m_h);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
         std::uniform_int_distribution<> distribution(1, 254);
         library->fill(tensor, distribution, i);
     }

     TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h)
     {
         // Create tensors
         TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
         TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
         TensorType lhs_reshaped;
         TensorType rhs_reshaped;
         TensorType dst;

         const unsigned int M = lhs_shape[1];
         const unsigned int N = rhs_shape[0];
         const unsigned int K = lhs_shape[0];

         // The output tensor will be auto-initialized within the function

         // Create and configure function
         ReshapeLHSFunctionType reshape_lhs;
         ReshapeRHSFunctionType reshape_rhs;
         GEMMFunctionType       gemm;
         reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
         reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
         gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));

         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
         lhs_reshaped.allocator()->allocate();
         rhs_reshaped.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);

         // Compute GEMM
         reshape_lhs.run();
         reshape_rhs.run();
         gemm.run();

         return dst;
     }

     SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h)
     {
         TensorShape dst_shape = lhs_shape;
         dst_shape.set(0, rhs_shape[0]);
         dst_shape.set(1, lhs_shape[1] / m_h);
         dst_shape.set(2, m_h);
         dst_shape.set(3, lhs_shape[2]);

         // Create reference
         SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
         SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };

         // Fill reference
         fill(lhs, 0);
         fill(rhs, 1);

         return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
     }

     TensorType            _target{};
     SimpleTensor<int32_t> _reference{};
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
	/*
	* Copyright (c) 2017-2019 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.
	*/
	#ifndef ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE
	#define ARM_COMPUTE_TEST_GEMMLOWP_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/GEMMLowp.h"

	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false>
	class GEMMLowpMatrixMultiplyCoreValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
	{
	_target = compute_target(shape_a, shape_b, shape_c, a_offset, b_offset);
	_reference = compute_reference(shape_a, shape_b, shape_c, a_offset, b_offset);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
	std::uniform_int_distribution<> distribution(1, 254);
	library->fill(tensor, distribution, i);
	}

	TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
	{
	// Create tensors
	TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
	TensorType b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1);
	TensorType c = create_tensor<TensorType>(shape_c, DataType::S32, 1);

	a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
	b.info()->set_quantization_info(QuantizationInfo(1.0f / 255, b_offset));

	// Create and configure function
	// The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
	FunctionType gemmlowp;
	// TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
	gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 0), 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);

	// Allocate tensors
	a.allocator()->allocate();
	b.allocator()->allocate();
	c.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);

	// Fill tensors
	fill(AccessorType(a), 0);
	fill(AccessorType(b), 1);

	// Compute GEMM function
	gemmlowp.run();
	return c;
	}

	SimpleTensor<int32_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, int32_t a_offset, int32_t b_offset)
	{
	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<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
	SimpleTensor<uint8_t> b{ shape_b, DataType::QASYMM8, 1 };

	// Fill reference
	fill(a, 0);
	fill(b, 1);

	return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(a, b, shape_c, a_offset, b_offset);
	}

	TensorType _target{};
	SimpleTensor<int32_t> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType>
	class GEMMLowpQuantizeDownInt32ToUint8ScaleValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
	{
	_target = compute_target(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
	_reference = compute_reference(shape, result_offset, result_mult_int, result_shift, min, max, add_bias);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	std::uniform_int_distribution<> distribution(-6000, 6000);
	library->fill(tensor, distribution, i);
	}

	TensorType compute_target(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
	{
	TensorShape shape_bias(shape[0]);

	// Create tensors
	TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
	TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
	TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);

	// Create and configure function
	FunctionType output_stage;
	output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_offset, result_mult_int, result_shift, min, max);

	ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	a.allocator()->allocate();
	c.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensor
	fill(AccessorType(a), 0);

	if(add_bias)
	{
	ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate bias tensor
	b.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensor
	fill(AccessorType(b), 1);
	}

	// Compute GEMM function
	output_stage.run();
	return c;
	}

	SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_offset, int32_t result_mult_int, int32_t result_shift, int32_t min, int32_t max, bool add_bias)
	{
	// Create reference
	TensorShape shape_bias(shape[0]);

	SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
	SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };

	// Fill reference
	fill(a, 0);

	if(add_bias)
	{
	// Fill bias
	fill(b, 1);

	return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, b, result_offset, result_mult_int, result_shift, min, max);
	}
	else
	{
	return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int, result_shift, min, max);
	}
	}

	TensorType _target{};
	SimpleTensor<uint8_t> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType>
	class GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
	{
	_target = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
	_reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	std::uniform_int_distribution<> distribution(-6000, 6000);
	library->fill(tensor, distribution, i);
	}

	TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
	{
	TensorShape shape_bias(shape[0]);

	// Create tensors
	TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
	TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
	TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8, 1);

	// Create and configure function
	FunctionType output_stage;
	output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);

	ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	a.allocator()->allocate();
	c.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensor
	fill(AccessorType(a), 0);

	if(add_bias)
	{
	ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate bias tensor
	b.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensor
	fill(AccessorType(b), 1);
	}

	// Compute GEMM function
	output_stage.run();
	return c;
	}

	SimpleTensor<uint8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max,
	bool add_bias)
	{
	// Create reference
	TensorShape shape_bias(shape[0]);

	SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
	SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };

	// Fill reference
	fill(a, 0);

	if(add_bias)
	{
	// Fill bias
	fill(b, 1);

	return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, b, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max);
	}
	else
	{
	return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, result_fixed_point_multiplier, result_shift, result_offset_after_shift, min, max);
	}
	}

	TensorType _target{};
	SimpleTensor<uint8_t> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
	class GEMMLowpMatrixMultiplyReshapedValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
	bool interleave_rhs)
	{
	GEMMLHSMatrixInfo lhs_info;
	lhs_info.m0 = m0;
	lhs_info.k0 = k0;
	lhs_info.v0 = v0;
	lhs_info.interleave = interleave_lhs;
	lhs_info.transpose = false;

	GEMMRHSMatrixInfo rhs_info;
	rhs_info.n0 = n0;
	rhs_info.k0 = k0;
	rhs_info.h0 = h0;
	rhs_info.interleave = interleave_rhs;
	rhs_info.transpose = true;

	// Set the tensor shapes for LHS and RHS matrices
	const TensorShape lhs_shape(k, m, batch_size);
	const TensorShape rhs_shape(n, k, batch_size);

	_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info);
	_reference = compute_reference(lhs_shape, rhs_shape);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
	std::uniform_int_distribution<> distribution(1, 254);
	library->fill(tensor, distribution, i);
	}

	TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info)
	{
	// Create tensors
	TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
	TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
	TensorType lhs_reshaped;
	TensorType rhs_reshaped;
	TensorType dst;

	const unsigned int M = lhs_shape[1];
	const unsigned int N = rhs_shape[0];
	const unsigned int K = lhs_shape[0];

	// The output tensor will be auto-initialized within the function

	// Create and configure function
	ReshapeLHSFunctionType reshape_lhs;
	ReshapeRHSFunctionType reshape_rhs;
	GEMMFunctionType gemm;
	reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
	reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
	gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));

	ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	lhs.allocator()->allocate();
	rhs.allocator()->allocate();
	lhs_reshaped.allocator()->allocate();
	rhs_reshaped.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensors
	fill(AccessorType(lhs), 0);
	fill(AccessorType(rhs), 1);

	// Compute GEMM
	reshape_lhs.run();
	reshape_rhs.run();
	gemm.run();

	return dst;
	}

	SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape)
	{
	TensorShape dst_shape = lhs_shape;
	dst_shape[0] = rhs_shape[0];
	dst_shape[1] = lhs_shape[1];

	// Create reference
	SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
	SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };

	// Fill reference
	fill(lhs, 0);
	fill(rhs, 1);

	return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
	}

	TensorType _target{};
	SimpleTensor<int32_t> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
	class GEMMLowpMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
	bool interleave_lhs, bool interleave_rhs)
	{
	GEMMLHSMatrixInfo lhs_info;
	lhs_info.m0 = m0;
	lhs_info.k0 = k0;
	lhs_info.v0 = v0;
	lhs_info.interleave = interleave_lhs;
	lhs_info.transpose = false;

	GEMMRHSMatrixInfo rhs_info;
	rhs_info.n0 = n0;
	rhs_info.k0 = k0;
	rhs_info.h0 = h0;
	rhs_info.interleave = interleave_rhs;
	rhs_info.transpose = true;

	// In case of GEMM3D, m is the product between m_w and m_h
	const unsigned int m = m_w * m_h;

	// Set the tensor shapes for LHS and RHS matrices
	const TensorShape lhs_shape(k, m, batch_size);
	const TensorShape rhs_shape(n, k, batch_size);

	_target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h);
	_reference = compute_reference(lhs_shape, rhs_shape, m_h);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	// Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path
	std::uniform_int_distribution<> distribution(1, 254);
	library->fill(tensor, distribution, i);
	}

	TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h)
	{
	// Create tensors
	TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1);
	TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1);
	TensorType lhs_reshaped;
	TensorType rhs_reshaped;
	TensorType dst;

	const unsigned int M = lhs_shape[1];
	const unsigned int N = rhs_shape[0];
	const unsigned int K = lhs_shape[0];

	// The output tensor will be auto-initialized within the function

	// Create and configure function
	ReshapeLHSFunctionType reshape_lhs;
	ReshapeRHSFunctionType reshape_rhs;
	GEMMFunctionType gemm;
	reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info);
	reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info);
	gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));

	ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	lhs.allocator()->allocate();
	rhs.allocator()->allocate();
	lhs_reshaped.allocator()->allocate();
	rhs_reshaped.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensors
	fill(AccessorType(lhs), 0);
	fill(AccessorType(rhs), 1);

	// Compute GEMM
	reshape_lhs.run();
	reshape_rhs.run();
	gemm.run();

	return dst;
	}

	SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h)
	{
	TensorShape dst_shape = lhs_shape;
	dst_shape.set(0, rhs_shape[0]);
	dst_shape.set(1, lhs_shape[1] / m_h);
	dst_shape.set(2, m_h);
	dst_shape.set(3, lhs_shape[2]);

	// Create reference
	SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 };
	SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 };

	// Fill reference
	fill(lhs, 0);
	fill(rhs, 1);

	return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0);
	}

	TensorType _target{};
	SimpleTensor<int32_t> _reference{};
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */