src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2020 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 "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 #include <arm_neon.h>

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
                           int32_t a_offset, int32_t b_offset)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);

     // If a_offset == 0, vector_sum_col can be a nullptr
     if(a_offset != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
         ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
     }

     // If b_offset == 0, vector_sum_row can be a nullptr
     if(b_offset != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);

         // Check if input is a 3D reinterpretation
         const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();

         // Validate input
         ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
         ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));

         TensorShape output_shape = mm_result->tensor_shape();
         if(output_shape.num_dimensions() > 1)
         {
             const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;

             TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
             vector_sum_row_shape.collapse_from(1);
             output_shape.collapse_from(output_batch_idx);

             ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
                                             "mm_result tensor must have the same number of batches of output tensor");

             if(a_offset != 0)
             {
                 TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
                 vector_sum_col_shape.collapse_from(1);

                 ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
                                                 "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
             }
         }
     }

     return Status{};
 }

 void run_offset_contribution(const Window &window,
                              ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row,
                              int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col, bool is_gemm3d)
 {
     Window collapsed_window = window.collapse_if_possible(window, Window::DimZ);
     collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1));

     const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0;
     const int depth_input  = is_gemm3d ? mm_result->info()->dimension(2) : 1;

     const int window_start_x = window.x().start();
     const int window_end_x   = window.x().end();
     const int window_step_x  = 16;

     Iterator mm_result_it(mm_result, collapsed_window);

     if((a_offset != 0) && (b_offset != 0) && (vector_sum_col != nullptr) && (vector_sum_row != nullptr)) // true, true
     {
         // Set window for vector_sum_col
         Window win_vector_sum_col(collapsed_window);
         win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
         win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));

         // Set window for vector_sum_row
         Window win_vector_sum_row(collapsed_window);
         win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));

         Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);
         Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);

         const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();

         // Offset in case vector_sum_col is batched
         const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;

         execute_window_loop(collapsed_window, [&](const Coordinates & id)
         {
             const int batch_id           = id.z() / depth_input;
             auto      vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
             auto      mm_result_ptr      = reinterpret_cast<int32_t *>(mm_result_it.ptr());

             // Compute the leftover term due to b_offset.
             int32_t b_offset_term_s32 = *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input);
             b_offset_term_s32 *= b_offset;

             const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32);

             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 // Compute the leftover term due to a_offset.
                 int32x4x4_t a_offset_term_s32 =
                 {
                     {
                         vld1q_s32(vector_sum_col_ptr + x + 0),
                         vld1q_s32(vector_sum_col_ptr + x + 4),
                         vld1q_s32(vector_sum_col_ptr + x + 8),
                         vld1q_s32(vector_sum_col_ptr + x + 12)
                     }
                 };

                 a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
                 a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
                 a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
                 a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);

                 // Add a_offset_term_s32 and b_offset_term_s32
                 int32x4x4_t offset_term_s32 =
                 {
                     {
                         vdupq_n_s32(k_offset),
                         vdupq_n_s32(k_offset),
                         vdupq_n_s32(k_offset),
                         vdupq_n_s32(k_offset)
                     }
                 };

                 offset_term_s32.val[0] = vaddq_s32(offset_term_s32.val[0], vaddq_s32(a_offset_term_s32.val[0], b_offset_term_s32_vec));
                 offset_term_s32.val[1] = vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32_vec));
                 offset_term_s32.val[2] = vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32_vec));
                 offset_term_s32.val[3] = vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32_vec));

                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(mm_result_ptr + x + 0),
                         vld1q_s32(mm_result_ptr + x + 4),
                         vld1q_s32(mm_result_ptr + x + 8),
                         vld1q_s32(mm_result_ptr + x + 12)
                     }
                 };

                 // Add the offset terms to GEMM's result
                 in_s32.val[0] = vaddq_s32(in_s32.val[0], offset_term_s32.val[0]);
                 in_s32.val[1] = vaddq_s32(in_s32.val[1], offset_term_s32.val[1]);
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], offset_term_s32.val[2]);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32.val[3]);

                 // Store the result with the offset contribution
                 vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
                 vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
                 vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
                 vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
             }

             // Left-overs loop
             for(; x < window_end_x; ++x)
             {
                 // Compute the leftover term due to a_offset.
                 int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x);

                 a_offset_term_s32 *= a_offset;

                 // Add the offset terms to GEMM's result
                 // Store the result with the offset contribution
                 mm_result_ptr[x] += k_offset + a_offset_term_s32 + b_offset_term_s32;
             }
         },
         vector_sum_col_it, vector_sum_row_it, mm_result_it);
     }
     else if((a_offset == 0) && (b_offset != 0) && (vector_sum_row != nullptr)) // false, true
     {
         ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);

         // Set window for vector_sum_row
         Window win_vector_sum_row(collapsed_window);
         win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));

         Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);

         const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();

         execute_window_loop(collapsed_window, [&](const Coordinates & id)
         {
             const int batch_id      = id.z() / depth_input;
             auto      mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());

             // Compute the leftover term due to b_offset.
             int32_t b_offset_term_s32 = *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input);
             b_offset_term_s32 *= b_offset;

             const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32);

             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(mm_result_ptr + x + 0),
                         vld1q_s32(mm_result_ptr + x + 4),
                         vld1q_s32(mm_result_ptr + x + 8),
                         vld1q_s32(mm_result_ptr + x + 12)
                     }
                 };

                 // Add the offset terms to GEMM's result
                 in_s32.val[0] = vaddq_s32(in_s32.val[0], b_offset_term_s32_vec);
                 in_s32.val[1] = vaddq_s32(in_s32.val[1], b_offset_term_s32_vec);
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], b_offset_term_s32_vec);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32_vec);

                 // Store the result with the offset contribution
                 vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
                 vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
                 vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
                 vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
             }

             // Left-overs loop
             for(; x < window_end_x; ++x)
             {
                 // Add the offset terms to GEMM's result
                 // Store the result with the offset contribution
                 mm_result_ptr[x] += b_offset_term_s32;
             }
         },
         vector_sum_row_it, mm_result_it);
     }
     else if((a_offset != 0) && (b_offset == 0) && (vector_sum_col != nullptr)) // true, false
     {
         // Set window for vector_sum_col
         Window win_vector_sum_col(collapsed_window);
         win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
         win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));

         Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);

         // Offset in case vector_sum_col is batched
         const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;

         execute_window_loop(collapsed_window, [&](const Coordinates & id)
         {
             const int batch_id           = id.z() / depth_input;
             auto      vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
             auto      mm_result_ptr      = reinterpret_cast<int32_t *>(mm_result_it.ptr());

             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 // Compute the leftover term due to a_offset.
                 int32x4x4_t a_offset_term_s32 =
                 {
                     {
                         vld1q_s32(vector_sum_col_ptr + x + 0),
                         vld1q_s32(vector_sum_col_ptr + x + 4),
                         vld1q_s32(vector_sum_col_ptr + x + 8),
                         vld1q_s32(vector_sum_col_ptr + x + 12)
                     }
                 };

                 a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
                 a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
                 a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
                 a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);

                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(mm_result_ptr + x + 0),
                         vld1q_s32(mm_result_ptr + x + 4),
                         vld1q_s32(mm_result_ptr + x + 8),
                         vld1q_s32(mm_result_ptr + x + 12)
                     }
                 };

                 // Add the offset terms to GEMM's result
                 in_s32.val[0] = vaddq_s32(in_s32.val[0], a_offset_term_s32.val[0]);
                 in_s32.val[1] = vaddq_s32(in_s32.val[1], a_offset_term_s32.val[1]);
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], a_offset_term_s32.val[2]);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], a_offset_term_s32.val[3]);

                 // Store the result with the offset contribution
                 vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
                 vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
                 vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
                 vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
             }

             // Left-overs loop
             for(; x < window_end_x; ++x)
             {
                 // Compute the leftover term due to a_offset.
                 const int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x);

                 // Add the offset terms to GEMM's result
                 // Store the result with the offset contribution
                 mm_result_ptr[x] += a_offset_term_s32 * a_offset;
             }
         },
         vector_sum_col_it, mm_result_it);
     }
     else // false, false
     {
         // No offset contribution from matrix A and matrix B
         return;
     }
 }
 } // namespace

 NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel()
     : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true)
 {
 }

 void NEGEMMLowpOffsetContributionKernel::configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
                                                   vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
                                                   vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
                                                   a_offset, b_offset));                                         // NOLINT

     _vector_sum_col = vector_sum_col;
     _vector_sum_row = vector_sum_row;
     _mm_result      = mm_result;
     _a_offset       = a_offset;
     _b_offset       = b_offset;
     _k_offset       = a_offset * b_offset * k;

     // If a_offset == 0, vector_sum_col can be a nullptr
     if(a_offset != 0)
     {
         // Check if vector_sum_col_shape should be slidden or not
         // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
         // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
         _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
     }

     // Configure kernel window
     Window      win = calculate_max_window(*mm_result->info(), Steps());
     Coordinates coord;
     coord.set_num_dimensions(mm_result->info()->num_dimensions());
     mm_result->info()->set_valid_region(ValidRegion(coord, mm_result->info()->tensor_shape()));
     INEKernel::configure(win);
 }

 Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
                                                     int32_t a_offset, int32_t b_offset)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));

     return Status{};
 }

 void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

     // Check if input is a 3D reinterpretation
     const bool reinterpret_as_3d = _vector_sum_row != nullptr
                                    && _mm_result->info()->num_dimensions() > 1
                                    && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x();

     run_offset_contribution(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, reinterpret_as_3d);
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2020 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 "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	#include <arm_neon.h>

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo mm_result, const ITensorInfo vector_sum_col, const ITensorInfo *vector_sum_row,
	int32_t a_offset, int32_t b_offset)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);

	// If a_offset == 0, vector_sum_col can be a nullptr
	if(a_offset != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
	}

	// If b_offset == 0, vector_sum_row can be a nullptr
	if(b_offset != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);

	// Check if input is a 3D reinterpretation
	const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();

	// Validate input
	ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
	ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));

	TensorShape output_shape = mm_result->tensor_shape();
	if(output_shape.num_dimensions() > 1)
	{
	const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;

	TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
	vector_sum_row_shape.collapse_from(1);
	output_shape.collapse_from(output_batch_idx);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
	"mm_result tensor must have the same number of batches of output tensor");

	if(a_offset != 0)
	{
	TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
	vector_sum_col_shape.collapse_from(1);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
	"vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
	}
	}
	}

	return Status{};
	}

	void run_offset_contribution(const Window &window,
	ITensor mm_result, const ITensor vector_sum_col, const ITensor *vector_sum_row,
	int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col, bool is_gemm3d)
	{
	Window collapsed_window = window.collapse_if_possible(window, Window::DimZ);
	collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1));

	const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0;
	const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1;

	const int window_start_x = window.x().start();
	const int window_end_x = window.x().end();
	const int window_step_x = 16;

	Iterator mm_result_it(mm_result, collapsed_window);

	if((a_offset != 0) && (b_offset != 0) && (vector_sum_col != nullptr) && (vector_sum_row != nullptr)) // true, true
	{
	// Set window for vector_sum_col
	Window win_vector_sum_col(collapsed_window);
	win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
	win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));

	// Set window for vector_sum_row
	Window win_vector_sum_row(collapsed_window);
	win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
	win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
	win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));

	Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);
	Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);

	const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();

	// Offset in case vector_sum_col is batched
	const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;

	execute_window_loop(collapsed_window, [&](const Coordinates & id)
	{
	const int batch_id = id.z() / depth_input;
	auto vector_sum_col_ptr = reinterpret_cast<const int32_t >(vector_sum_col_it.ptr() + batch_id vector_sum_col_batch_offset);
	auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());

	// Compute the leftover term due to b_offset.
	int32_t b_offset_term_s32 = (reinterpret_cast<const int32_t >(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input);
	b_offset_term_s32 *= b_offset;

	const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32);

	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	// Compute the leftover term due to a_offset.
	int32x4x4_t a_offset_term_s32 =
	{
	{
	vld1q_s32(vector_sum_col_ptr + x + 0),
	vld1q_s32(vector_sum_col_ptr + x + 4),
	vld1q_s32(vector_sum_col_ptr + x + 8),
	vld1q_s32(vector_sum_col_ptr + x + 12)
	}
	};

	a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
	a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
	a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
	a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);

	// Add a_offset_term_s32 and b_offset_term_s32
	int32x4x4_t offset_term_s32 =
	{
	{
	vdupq_n_s32(k_offset),
	vdupq_n_s32(k_offset),
	vdupq_n_s32(k_offset),
	vdupq_n_s32(k_offset)
	}
	};

	offset_term_s32.val[0] = vaddq_s32(offset_term_s32.val[0], vaddq_s32(a_offset_term_s32.val[0], b_offset_term_s32_vec));
	offset_term_s32.val[1] = vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32_vec));
	offset_term_s32.val[2] = vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32_vec));
	offset_term_s32.val[3] = vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32_vec));

	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(mm_result_ptr + x + 0),
	vld1q_s32(mm_result_ptr + x + 4),
	vld1q_s32(mm_result_ptr + x + 8),
	vld1q_s32(mm_result_ptr + x + 12)
	}
	};

	// Add the offset terms to GEMM's result
	in_s32.val[0] = vaddq_s32(in_s32.val[0], offset_term_s32.val[0]);
	in_s32.val[1] = vaddq_s32(in_s32.val[1], offset_term_s32.val[1]);
	in_s32.val[2] = vaddq_s32(in_s32.val[2], offset_term_s32.val[2]);
	in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32.val[3]);

	// Store the result with the offset contribution
	vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
	vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
	vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
	vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
	}

	// Left-overs loop
	for(; x < window_end_x; ++x)
	{
	// Compute the leftover term due to a_offset.
	int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x);

	a_offset_term_s32 *= a_offset;

	// Add the offset terms to GEMM's result
	// Store the result with the offset contribution
	mm_result_ptr[x] += k_offset + a_offset_term_s32 + b_offset_term_s32;
	}
	},
	vector_sum_col_it, vector_sum_row_it, mm_result_it);
	}
	else if((a_offset == 0) && (b_offset != 0) && (vector_sum_row != nullptr)) // false, true
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);

	// Set window for vector_sum_row
	Window win_vector_sum_row(collapsed_window);
	win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
	win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
	win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));

	Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);

	const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();

	execute_window_loop(collapsed_window, [&](const Coordinates & id)
	{
	const int batch_id = id.z() / depth_input;
	auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());

	// Compute the leftover term due to b_offset.
	int32_t b_offset_term_s32 = (reinterpret_cast<const int32_t >(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input);
	b_offset_term_s32 *= b_offset;

	const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32);

	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(mm_result_ptr + x + 0),
	vld1q_s32(mm_result_ptr + x + 4),
	vld1q_s32(mm_result_ptr + x + 8),
	vld1q_s32(mm_result_ptr + x + 12)
	}
	};

	// Add the offset terms to GEMM's result
	in_s32.val[0] = vaddq_s32(in_s32.val[0], b_offset_term_s32_vec);
	in_s32.val[1] = vaddq_s32(in_s32.val[1], b_offset_term_s32_vec);
	in_s32.val[2] = vaddq_s32(in_s32.val[2], b_offset_term_s32_vec);
	in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32_vec);

	// Store the result with the offset contribution
	vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
	vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
	vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
	vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
	}

	// Left-overs loop
	for(; x < window_end_x; ++x)
	{
	// Add the offset terms to GEMM's result
	// Store the result with the offset contribution
	mm_result_ptr[x] += b_offset_term_s32;
	}
	},
	vector_sum_row_it, mm_result_it);
	}
	else if((a_offset != 0) && (b_offset == 0) && (vector_sum_col != nullptr)) // true, false
	{
	// Set window for vector_sum_col
	Window win_vector_sum_col(collapsed_window);
	win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
	win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));

	Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);

	// Offset in case vector_sum_col is batched
	const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;

	execute_window_loop(collapsed_window, [&](const Coordinates & id)
	{
	const int batch_id = id.z() / depth_input;
	auto vector_sum_col_ptr = reinterpret_cast<const int32_t >(vector_sum_col_it.ptr() + batch_id vector_sum_col_batch_offset);
	auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());

	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	// Compute the leftover term due to a_offset.
	int32x4x4_t a_offset_term_s32 =
	{
	{
	vld1q_s32(vector_sum_col_ptr + x + 0),
	vld1q_s32(vector_sum_col_ptr + x + 4),
	vld1q_s32(vector_sum_col_ptr + x + 8),
	vld1q_s32(vector_sum_col_ptr + x + 12)
	}
	};

	a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
	a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
	a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
	a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);

	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(mm_result_ptr + x + 0),
	vld1q_s32(mm_result_ptr + x + 4),
	vld1q_s32(mm_result_ptr + x + 8),
	vld1q_s32(mm_result_ptr + x + 12)
	}
	};

	// Add the offset terms to GEMM's result
	in_s32.val[0] = vaddq_s32(in_s32.val[0], a_offset_term_s32.val[0]);
	in_s32.val[1] = vaddq_s32(in_s32.val[1], a_offset_term_s32.val[1]);
	in_s32.val[2] = vaddq_s32(in_s32.val[2], a_offset_term_s32.val[2]);
	in_s32.val[3] = vaddq_s32(in_s32.val[3], a_offset_term_s32.val[3]);

	// Store the result with the offset contribution
	vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
	vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
	vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
	vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
	}

	// Left-overs loop
	for(; x < window_end_x; ++x)
	{
	// Compute the leftover term due to a_offset.
	const int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x);

	// Add the offset terms to GEMM's result
	// Store the result with the offset contribution
	mm_result_ptr[x] += a_offset_term_s32 * a_offset;
	}
	},
	vector_sum_col_it, mm_result_it);
	}
	else // false, false
	{
	// No offset contribution from matrix A and matrix B
	return;
	}
	}
	} // namespace

	NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel()
	: _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true)
	{
	}

	void NEGEMMLowpOffsetContributionKernel::configure(ITensor mm_result, const ITensor vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
	vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
	vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
	a_offset, b_offset)); // NOLINT

	_vector_sum_col = vector_sum_col;
	_vector_sum_row = vector_sum_row;
	_mm_result = mm_result;
	_a_offset = a_offset;
	_b_offset = b_offset;
	_k_offset = a_offset * b_offset * k;

	// If a_offset == 0, vector_sum_col can be a nullptr
	if(a_offset != 0)
	{
	// Check if vector_sum_col_shape should be slidden or not
	// Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
	// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
	_slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
	}

	// Configure kernel window
	Window win = calculate_max_window(*mm_result->info(), Steps());
	Coordinates coord;
	coord.set_num_dimensions(mm_result->info()->num_dimensions());
	mm_result->info()->set_valid_region(ValidRegion(coord, mm_result->info()->tensor_shape()));
	INEKernel::configure(win);
	}

	Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo mm_result, const ITensorInfo vector_sum_col, const ITensorInfo *vector_sum_row,
	int32_t a_offset, int32_t b_offset)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));

	return Status{};
	}

	void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

	// Check if input is a 3D reinterpretation
	const bool reinterpret_as_3d = _vector_sum_row != nullptr
	&& _mm_result->info()->num_dimensions() > 1
	&& _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x();

	run_offset_contribution(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, reinterpret_as_3d);
	}
	} // namespace arm_compute