tests/validation/reference/GEMMLowp.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2020, 2024 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 "GEMMLowp.h"

 #include "arm_compute/core/Types.h"
 #include "tests/validation/reference/ArithmeticOperations.h"
 #include "tests/validation/reference/UtilsQuantizedAsymm.h"

 #include "support/ToolchainSupport.h"

 #include <limits>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 namespace
 {
 template <typename T>
 struct DataTypeExtractor
 {
     static DataType data_type()
     {
         DataType data_type = DataType::UNKNOWN;
         if(std::is_same<T, int8_t>::value)
         {
             data_type = DataType::QASYMM8_SIGNED;
         }
         else if(std::is_same<T, uint8_t>::value)
         {
             data_type = DataType::QASYMM8;
         }
         else if(std::is_same<T, int16_t>::value)
         {
             data_type = DataType::QSYMM16;
         }
         return data_type;
     }
 };

 template <typename TIn, typename TOut>
 void quantize_down_scale(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, int32_t result_offset, std::vector<int32_t> result_mult_int,
                          std::vector<int32_t> result_shift, int32_t min, int32_t max)
 {
     const int  cols_in        = in->shape().x();
     const bool is_per_channel = result_mult_int.size() > 1;

 #if defined(_OPENMP)
     #pragma omp parallel for
 #endif /* _OPENMP */
     for(int i = 0; i < in->num_elements(); ++i)
     {
         int32_t result = ((*in)[i] + result_offset);

         if(bias != nullptr)
         {
             result += (*bias)[i % cols_in];
         }

         result *= (is_per_channel) ? result_mult_int[i % cols_in] : result_mult_int[0];

         result >>= (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];

         // Bounded ReLu
         if(min != max)
         {
             result = std::max(min, std::min(max, result));
         }

         (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
                                                     std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
     }
 }

 template <typename TIn, typename TOut>
 void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<int32_t> result_fixedpoint_multiplier,
                                        std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
 {
     const int  cols_in        = in->shape().x();
     const bool is_per_channel = result_fixedpoint_multiplier.size() > 1;

 #if defined(_OPENMP)
     #pragma omp parallel for
 #endif /* _OPENMP */
     for(int i = 0; i < in->num_elements(); ++i)
     {
         TIn result = (*in)[i];

         if(bias != nullptr)
         {
             result += (*bias)[i % cols_in];
         }

         // Fixed point multiplication
         const int32_t multiplier = (is_per_channel) ? result_fixedpoint_multiplier[i % cols_in] : result_fixedpoint_multiplier[0];
         const int32_t shift      = (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];

         if(shift < 0)
         {
             result = asymm_int_mult(result * (1 << (-shift)), multiplier);
         }
         else
         {
             result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift);
         }
         result += result_offset_after_shift;

         // Bounded ReLu
         if(min != max)
         {
             result = std::max(min, std::min(max, result));
         }

         (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
                                                     std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
     }
 }

 template <typename TIn, typename TOut>
 void quantize_down_scale_by_float(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<float_t> result_real_multiplier,
                                   int32_t result_offset, int32_t min, int32_t max)
 {
     const int  cols_in        = in->shape().x();
     const bool is_per_channel = result_real_multiplier.size() > 1;

 #if defined(_OPENMP)
     #pragma omp parallel for
 #endif /* _OPENMP */
     for(int i = 0; i < in->num_elements(); ++i)
     {
         TIn result = (*in)[i];

         if(bias != nullptr)
         {
             result += (*bias)[i % cols_in];
         }

         // Float multiplication
         const float_t multiplier = (is_per_channel) ? result_real_multiplier[i % cols_in] : result_real_multiplier[0];

         float_t result_f = static_cast<float_t>(result) * multiplier + static_cast<float_t>(result_offset);
         result           = static_cast<TIn>(support::cpp11::round(result_f));

         // Bounded ReLu
         if(min != max)
         {
             result = std::max(min, std::min(max, result));
         }

         (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
                                                     std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
     }
 }
 } // namespace

 template <typename T_out, typename T_in, typename T_in_1>
 SimpleTensor<T_out> gemmlowp_matrix_multiply_core(const SimpleTensor<T_in> &a, const SimpleTensor<T_in_1> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
 {
     static_assert(std::is_same<typename std::decay<T_out>::type, int32_t>::value, "Only int32_t is allowed for the output");

     DataType            dt = std::is_same<T_out, int32_t>::value ? DataType::S32 : DataType::U32;
     SimpleTensor<T_out> c(shape_c, dt);

     const int K = a.shape().x();
     const int M = a.shape().y();
     const int N = b.shape().x();
     const int D = a.shape().z(); // Number of matrices in a batch

     const int a_stride_z = K * M;
     // Do not slide the matrix B along the 3rd dimension in case matrix B has less than 3 dimensions
     const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0;
     const int c_stride_z = N * M;

     std::vector<T_out> acc;
     acc.resize(N);

     for(int depth = 0; depth < D; ++depth)
     {
         const int base_addr_a = depth * a_stride_z;
         const int base_addr_b = depth * b_stride_z;
         const int base_addr_c = depth * c_stride_z;

         for(int i = 0; i < M; ++i)
         {
             for(int j = 0; j < N; ++j)
             {
                 acc[j] = 0;
             }
             for(int k = 0; k < K; ++k)
             {
                 const T_out tmp_a = a_offset + static_cast<T_out>(a[base_addr_a + k + i * K]);
                 for(int j = 0; j < N; ++j)
                 {
                     const T_out tmp_b       = b_offset + static_cast<T_out>(b[base_addr_b + j + k * N]);
                     const T_out mult_as_int = tmp_a * tmp_b;
                     acc[j] += mult_as_int;
                 }
             }
             for(int j = 0; j < N; ++j)
             {
                 c[base_addr_c + j + i * N] = acc[j];
             }
         }
     }

     return c;
 }

 template <typename T_out, typename T_in, typename T_in_1>
 void gemmlowp_matrix_multiply_core_accumulate(const SimpleTensor<T_in> &a, const SimpleTensor<T_in_1> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset, SimpleTensor<T_out> &dst)
 {
     SimpleTensor<T_out> dst_gemm = gemmlowp_matrix_multiply_core<T_out, T_in, T_in_1>(a, b, shape_c, a_offset, b_offset);
     reference::arithmetic_operation<T_out>(reference::ArithmeticOperation::ADD, dst, dst_gemm, dst, ConvertPolicy::SATURATE);
 }

 // used to validate assembly kernels which don't know anything about offsets
 template <typename T1, typename T2, typename T3>
 SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T3> &b, TensorShape shape_c)
 {
     return gemmlowp_matrix_multiply_core<T1, T2, T3>(a, b, shape_c, 0, 0);
 }

 template <typename TIn, typename TOut>
 SimpleTensor<TOut> gemmlowp_quantize_down_scale(const SimpleTensor<TIn> &in, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift,
                                                 int32_t min, int32_t max)
 {
     SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

     quantize_down_scale<TIn, TOut>(&in, nullptr, &dst, result_offset, result_mult_int, result_shift, min, max);

     return dst;
 }

 template <typename TIn, typename TOut>
 SimpleTensor<TOut> gemmlowp_quantize_down_scale(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, int32_t result_offset, std::vector<int32_t> result_mult_int,
                                                 std::vector<int32_t> result_shift, int32_t min, int32_t max)
 {
     SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

     quantize_down_scale<TIn, TOut>(&in, &bias, &dst, result_offset, result_mult_int, result_shift, min, max);

     return dst;
 }

 template <typename TIn, typename TOut>
 SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
                                                               int32_t result_offset_after_shift, int32_t min, int32_t max)
 {
     SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

     quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);

     return dst;
 }

 template <typename TIn, typename TOut>
 SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
                                                               std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
 {
     SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

     quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);

     return dst;
 }

 template <typename TIn, typename TOut>
 SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias,
                                                          std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max)
 {
     SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

     quantize_down_scale_by_float<TIn, TOut>(&in, &bias, &dst, result_real_multiplier, result_offset, min, max);

     return dst;
 }

 template <typename TIn, typename TOut>
 SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in,
                                                          std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max)
 {
     SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

     quantize_down_scale_by_float<TIn, TOut>(&in, nullptr, &dst, result_real_multiplier, result_offset, min, max);

     return dst;
 }

 template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
                                                                      std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
 template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a,
                                                                      std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
 template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
                                                                     std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
 template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a,
                                                                     std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
 template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
                                                                           std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
 template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
                                                                           std::vector<int32_t> result_fixedpoint_multiplier,
                                                                           std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
 template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
                                                                          std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
 template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
                                                                          std::vector<int32_t> result_fixedpoint_multiplier,
                                                                          std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
 template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
                                                                           std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
 template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
                                                                           std::vector<int32_t> result_fixedpoint_multiplier,
                                                                           std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
 template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
                                                             std::vector<int32_t> result_shift, int32_t min, int32_t max);
 template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
                                                             std::vector<int32_t> result_shift, int32_t min, int32_t max);
 template SimpleTensor<int8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
                                                            std::vector<int32_t> result_shift, int32_t min, int32_t max);
 template SimpleTensor<int8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
                                                            std::vector<int32_t> result_shift, int32_t min, int32_t max);
 template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
 template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
 template void gemmlowp_matrix_multiply_core_accumulate(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset, SimpleTensor<int32_t> &dst);
 template void gemmlowp_matrix_multiply_core_accumulate(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset, SimpleTensor<int32_t> &dst);
 template SimpleTensor<int32_t> gemmlowp<int32_t, int8_t, int8_t>(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
 template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, uint8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c);
 template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, int8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2020, 2024 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 "GEMMLowp.h"

	#include "arm_compute/core/Types.h"
	#include "tests/validation/reference/ArithmeticOperations.h"
	#include "tests/validation/reference/UtilsQuantizedAsymm.h"

	#include "support/ToolchainSupport.h"

	#include <limits>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	namespace
	{
	template <typename T>
	struct DataTypeExtractor
	{
	static DataType data_type()
	{
	DataType data_type = DataType::UNKNOWN;
	if(std::is_same<T, int8_t>::value)
	{
	data_type = DataType::QASYMM8_SIGNED;
	}
	else if(std::is_same<T, uint8_t>::value)
	{
	data_type = DataType::QASYMM8;
	}
	else if(std::is_same<T, int16_t>::value)
	{
	data_type = DataType::QSYMM16;
	}
	return data_type;
	}
	};

	template <typename TIn, typename TOut>
	void quantize_down_scale(const SimpleTensor<TIn> in, const SimpleTensor<TIn> bias, SimpleTensor<TOut> *dst, int32_t result_offset, std::vector<int32_t> result_mult_int,
	std::vector<int32_t> result_shift, int32_t min, int32_t max)
	{
	const int cols_in = in->shape().x();
	const bool is_per_channel = result_mult_int.size() > 1;

	#if defined(_OPENMP)
	#pragma omp parallel for
	#endif /* _OPENMP */
	for(int i = 0; i < in->num_elements(); ++i)
	{
	int32_t result = ((*in)[i] + result_offset);

	if(bias != nullptr)
	{
	result += (*bias)[i % cols_in];
	}

	result *= (is_per_channel) ? result_mult_int[i % cols_in] : result_mult_int[0];

	result >>= (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];

	// Bounded ReLu
	if(min != max)
	{
	result = std::max(min, std::min(max, result));
	}

	(*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
	std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
	}
	}

	template <typename TIn, typename TOut>
	void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> in, const SimpleTensor<TIn> bias, SimpleTensor<TOut> *dst, std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
	{
	const int cols_in = in->shape().x();
	const bool is_per_channel = result_fixedpoint_multiplier.size() > 1;

	#if defined(_OPENMP)
	#pragma omp parallel for
	#endif /* _OPENMP */
	for(int i = 0; i < in->num_elements(); ++i)
	{
	TIn result = (*in)[i];

	if(bias != nullptr)
	{
	result += (*bias)[i % cols_in];
	}

	// Fixed point multiplication
	const int32_t multiplier = (is_per_channel) ? result_fixedpoint_multiplier[i % cols_in] : result_fixedpoint_multiplier[0];
	const int32_t shift = (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];

	if(shift < 0)
	{
	result = asymm_int_mult(result * (1 << (-shift)), multiplier);
	}
	else
	{
	result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift);
	}
	result += result_offset_after_shift;

	// Bounded ReLu
	if(min != max)
	{
	result = std::max(min, std::min(max, result));
	}

	(*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
	std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
	}
	}

	template <typename TIn, typename TOut>
	void quantize_down_scale_by_float(const SimpleTensor<TIn> in, const SimpleTensor<TIn> bias, SimpleTensor<TOut> *dst, std::vector<float_t> result_real_multiplier,
	int32_t result_offset, int32_t min, int32_t max)
	{
	const int cols_in = in->shape().x();
	const bool is_per_channel = result_real_multiplier.size() > 1;

	#if defined(_OPENMP)
	#pragma omp parallel for
	#endif /* _OPENMP */
	for(int i = 0; i < in->num_elements(); ++i)
	{
	TIn result = (*in)[i];

	if(bias != nullptr)
	{
	result += (*bias)[i % cols_in];
	}

	// Float multiplication
	const float_t multiplier = (is_per_channel) ? result_real_multiplier[i % cols_in] : result_real_multiplier[0];

	float_t result_f = static_cast<float_t>(result) * multiplier + static_cast<float_t>(result_offset);
	result = static_cast<TIn>(support::cpp11::round(result_f));

	// Bounded ReLu
	if(min != max)
	{
	result = std::max(min, std::min(max, result));
	}

	(*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
	std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
	}
	}
	} // namespace

	template <typename T_out, typename T_in, typename T_in_1>
	SimpleTensor<T_out> gemmlowp_matrix_multiply_core(const SimpleTensor<T_in> &a, const SimpleTensor<T_in_1> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset)
	{
	static_assert(std::is_same<typename std::decay<T_out>::type, int32_t>::value, "Only int32_t is allowed for the output");

	DataType dt = std::is_same<T_out, int32_t>::value ? DataType::S32 : DataType::U32;
	SimpleTensor<T_out> c(shape_c, dt);

	const int K = a.shape().x();
	const int M = a.shape().y();
	const int N = b.shape().x();
	const int D = a.shape().z(); // Number of matrices in a batch

	const int a_stride_z = K * M;
	// Do not slide the matrix B along the 3rd dimension in case matrix B has less than 3 dimensions
	const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0;
	const int c_stride_z = N * M;

	std::vector<T_out> acc;
	acc.resize(N);

	for(int depth = 0; depth < D; ++depth)
	{
	const int base_addr_a = depth * a_stride_z;
	const int base_addr_b = depth * b_stride_z;
	const int base_addr_c = depth * c_stride_z;

	for(int i = 0; i < M; ++i)
	{
	for(int j = 0; j < N; ++j)
	{
	acc[j] = 0;
	}
	for(int k = 0; k < K; ++k)
	{
	const T_out tmp_a = a_offset + static_cast<T_out>(a[base_addr_a + k + i * K]);
	for(int j = 0; j < N; ++j)
	{
	const T_out tmp_b = b_offset + static_cast<T_out>(b[base_addr_b + j + k * N]);
	const T_out mult_as_int = tmp_a * tmp_b;
	acc[j] += mult_as_int;
	}
	}
	for(int j = 0; j < N; ++j)
	{
	c[base_addr_c + j + i * N] = acc[j];
	}
	}
	}

	return c;
	}

	template <typename T_out, typename T_in, typename T_in_1>
	void gemmlowp_matrix_multiply_core_accumulate(const SimpleTensor<T_in> &a, const SimpleTensor<T_in_1> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset, SimpleTensor<T_out> &dst)
	{
	SimpleTensor<T_out> dst_gemm = gemmlowp_matrix_multiply_core<T_out, T_in, T_in_1>(a, b, shape_c, a_offset, b_offset);
	reference::arithmetic_operation<T_out>(reference::ArithmeticOperation::ADD, dst, dst_gemm, dst, ConvertPolicy::SATURATE);
	}

	// used to validate assembly kernels which don't know anything about offsets
	template <typename T1, typename T2, typename T3>
	SimpleTensor<T1> gemmlowp(const SimpleTensor<T2> &a, const SimpleTensor<T3> &b, TensorShape shape_c)
	{
	return gemmlowp_matrix_multiply_core<T1, T2, T3>(a, b, shape_c, 0, 0);
	}

	template <typename TIn, typename TOut>
	SimpleTensor<TOut> gemmlowp_quantize_down_scale(const SimpleTensor<TIn> &in, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift,
	int32_t min, int32_t max)
	{
	SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

	quantize_down_scale<TIn, TOut>(&in, nullptr, &dst, result_offset, result_mult_int, result_shift, min, max);

	return dst;
	}

	template <typename TIn, typename TOut>
	SimpleTensor<TOut> gemmlowp_quantize_down_scale(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, int32_t result_offset, std::vector<int32_t> result_mult_int,
	std::vector<int32_t> result_shift, int32_t min, int32_t max)
	{
	SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

	quantize_down_scale<TIn, TOut>(&in, &bias, &dst, result_offset, result_mult_int, result_shift, min, max);

	return dst;
	}

	template <typename TIn, typename TOut>
	SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
	int32_t result_offset_after_shift, int32_t min, int32_t max)
	{
	SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

	quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);

	return dst;
	}

	template <typename TIn, typename TOut>
	SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
	{
	SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

	quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);

	return dst;
	}

	template <typename TIn, typename TOut>
	SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias,
	std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max)
	{
	SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

	quantize_down_scale_by_float<TIn, TOut>(&in, &bias, &dst, result_real_multiplier, result_offset, min, max);

	return dst;
	}

	template <typename TIn, typename TOut>
	SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<TIn> &in,
	std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max)
	{
	SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());

	quantize_down_scale_by_float<TIn, TOut>(&in, nullptr, &dst, result_real_multiplier, result_offset, min, max);

	return dst;
	}

	template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
	std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
	template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a,
	std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
	template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
	std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
	template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_float(const SimpleTensor<int32_t> &a,
	std::vector<float_t> result_real_multiplier, int32_t result_offset, int32_t min, int32_t max);
	template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
	template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
	std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
	template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
	template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
	std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
	template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
	template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
	std::vector<int32_t> result_fixedpoint_multiplier,
	std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
	template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
	std::vector<int32_t> result_shift, int32_t min, int32_t max);
	template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
	std::vector<int32_t> result_shift, int32_t min, int32_t max);
	template SimpleTensor<int8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
	std::vector<int32_t> result_shift, int32_t min, int32_t max);
	template SimpleTensor<int8_t> gemmlowp_quantize_down_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
	std::vector<int32_t> result_shift, int32_t min, int32_t max);
	template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
	template SimpleTensor<int32_t> gemmlowp_matrix_multiply_core(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset);
	template void gemmlowp_matrix_multiply_core_accumulate(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset, SimpleTensor<int32_t> &dst);
	template void gemmlowp_matrix_multiply_core_accumulate(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c, int32_t a_offset, int32_t b_offset, SimpleTensor<int32_t> &dst);
	template SimpleTensor<int32_t> gemmlowp<int32_t, int8_t, int8_t>(const SimpleTensor<int8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
	template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, uint8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<uint8_t> &b, TensorShape shape_c);
	template SimpleTensor<int32_t> gemmlowp<int32_t, uint8_t, int8_t>(const SimpleTensor<uint8_t> &a, const SimpleTensor<int8_t> &b, TensorShape shape_c);
	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute