src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp - 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.
  */
 #include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.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 <arm_neon.h>
 #include <cstddef>
 #include <cstdint>

 using namespace arm_compute;

 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
     ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
     ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max);

     // Check biases if exist
     if(bias != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
         ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
     }

     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
 {
     // Note: This kernel performs 16 elements per iteration.
     // However, since we use a left-over for loop, we cannot have any read or write out of memory
     // For this reason num_elems_processed_per_iteration is set to 1
     constexpr unsigned int num_elems_processed_per_iteration = 1;

     // Configure kernel window
     Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

     AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

     bool window_changed = update_window_and_padding(win,
                                                     input_access);

     if(output->total_size() != 0)
     {
         AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
         window_changed = window_changed || update_window_and_padding(win, output_result_access);

         output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
     }

     if(bias != nullptr)
     {
         AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
         window_changed = window_changed || update_window_and_padding(win, bias_access);
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }

 inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
 {
     // Add the offset terms to GEMM's result
     in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
     in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
     in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
     in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);

     // Multiply by result_mult_int
     in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
     in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
     in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
     in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
 }

 template <bool    is_bounded_relu>
 inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
 {
     const static int32x4_t zero_s32 = vdupq_n_s32(0);

     // Shift final result (negative value shift right)
     in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
     in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
     in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
     in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);

     // Saturate negative values
     in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
     in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
     in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
     in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);

     // Convert S32 to S16
     const int16x8x2_t in_s16 =
     {
         {
             vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
             vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
         }
     };

     // Convert S16 to U8
     uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1]));

     if(is_bounded_relu)
     {
         out_u8 = vmaxq_u8(out_u8, min_u8);
         out_u8 = vminq_u8(out_u8, max_u8);
     }

     return out_u8;
 }
 } // namespace

 namespace arm_compute
 {
 class Coordinates;
 } // namespace arm_compute

 template <bool is_bounded_relu>
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window)
 {
     const int32x4_t  result_offset_s32 = vdupq_n_s32(_result_offset);
     const int32x4_t  result_shift_s32  = vdupq_n_s32(-_result_shift);
     const uint8x16_t min_u8            = vdupq_n_u8(static_cast<uint8_t>(_min));
     const uint8x16_t max_u8            = vdupq_n_u8(static_cast<uint8_t>(_max));

     ARM_COMPUTE_UNUSED(min_u8);
     ARM_COMPUTE_UNUSED(max_u8);

     const int  window_step_x  = 16;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     Window win(window);
     win.set(Window::DimX, Window::Dimension(0, 1, 1));

     Iterator in(_input, win);
     Iterator out(_output, win);

     if(_bias != nullptr)
     {
         Window win_biases;
         win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
         win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));

         Iterator bias(_bias, win_biases);
         execute_window_loop(win, [&](const Coordinates &)
         {
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
                     }
                 };

                 const int32x4x4_t bias_s32 =
                 {
                     {
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
                     }
                 };

                 // Add the bias to GEMM's result
                 in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
                 in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);

                 // Add the offset terms to GEMM's result and multiply by result_mult_int
                 scale_input(in_s32, result_offset_s32, _result_mult_int);

                 vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
             }

             // Compute left-over elements
             for(; x < window_end_x; ++x)
             {
                 const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
                 int       in_value   = *(reinterpret_cast<const int *>(in.ptr()) + x);

                 // Quantize
                 in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift;

                 // Finalize and store the result
                 if(is_bounded_relu)
                 {
                     *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
                 }
                 else
                 {
                     *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
                 }
             }
         },
         in, bias, out);
     }
     else
     {
         execute_window_loop(win, [&](const Coordinates &)
         {
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
                     }
                 };

                 // Add the offset terms to GEMM's result and multiply by result_mult_int
                 scale_input(in_s32, result_offset_s32, _result_mult_int);

                 vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
             }

             // Compute left-over elements
             for(; x < window_end_x; ++x)
             {
                 int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);

                 // Quantize
                 in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift;

                 // Finalize and store the result
                 if(is_bounded_relu)
                 {
                     *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
                 }
                 else
                 {
                     *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
                 }
             }
         },
         in, out);
     }
 }

 NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel()
     : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_offset(0), _result_mult_int(0), _result_shift(0), _min(0), _max(0)
 {
 }

 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

     // Output auto inizialitation if not yet initialized
     auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8));

     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
                                                   (bias != nullptr) ? bias->info() : nullptr,
                                                   output->info(),
                                                   min,
                                                   max));

     _input           = input;
     _bias            = bias;
     _output          = output;
     _result_offset   = result_offset;
     _result_mult_int = result_mult_int;
     _result_shift    = result_shift;
     _min             = min;
     _max             = max;

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     INEKernel::configure(win_config.second);

     // Check if we need to clamp the result using min and max
     const bool is_bounded_relu = ((min != max) && !(min == 0 && max == 255));
     _func                      = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<false>;
 }

 Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
                                                               (bias != nullptr) ? bias->clone().get() : nullptr,
                                                               output->clone().get())
                                 .first);

     return Status{};
 }

 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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);

     (this->*_func)(window);
 }
	/*
	* 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.
	*/
	#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.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 <arm_neon.h>
	#include <cstddef>
	#include <cstdint>

	using namespace arm_compute;

	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output, int min, int max)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
	ARM_COMPUTE_RETURN_ERROR_ON(min < 0 \|\| min > max);

	// Check biases if exist
	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
	ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
	ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
	}

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo bias, ITensorInfo *output)
	{
	// Note: This kernel performs 16 elements per iteration.
	// However, since we use a left-over for loop, we cannot have any read or write out of memory
	// For this reason num_elems_processed_per_iteration is set to 1
	constexpr unsigned int num_elems_processed_per_iteration = 1;

	// Configure kernel window
	Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

	AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

	bool window_changed = update_window_and_padding(win,
	input_access);

	if(output->total_size() != 0)
	{
	AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
	window_changed = window_changed \|\| update_window_and_padding(win, output_result_access);

	output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}

	if(bias != nullptr)
	{
	AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
	window_changed = window_changed \|\| update_window_and_padding(win, bias_access);
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}

	inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
	{
	// Add the offset terms to GEMM's result
	in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
	in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
	in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
	in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);

	// Multiply by result_mult_int
	in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
	in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
	in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
	in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
	}

	template <bool is_bounded_relu>
	inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
	{
	const static int32x4_t zero_s32 = vdupq_n_s32(0);

	// Shift final result (negative value shift right)
	in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
	in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
	in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
	in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);

	// Saturate negative values
	in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
	in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
	in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
	in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);

	// Convert S32 to S16
	const int16x8x2_t in_s16 =
	{
	{
	vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
	vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
	}
	};

	// Convert S16 to U8
	uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1]));

	if(is_bounded_relu)
	{
	out_u8 = vmaxq_u8(out_u8, min_u8);
	out_u8 = vminq_u8(out_u8, max_u8);
	}

	return out_u8;
	}
	} // namespace

	namespace arm_compute
	{
	class Coordinates;
	} // namespace arm_compute

	template <bool is_bounded_relu>
	void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window)
	{
	const int32x4_t result_offset_s32 = vdupq_n_s32(_result_offset);
	const int32x4_t result_shift_s32 = vdupq_n_s32(-_result_shift);
	const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(_min));
	const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(_max));

	ARM_COMPUTE_UNUSED(min_u8);
	ARM_COMPUTE_UNUSED(max_u8);

	const int window_step_x = 16;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	Window win(window);
	win.set(Window::DimX, Window::Dimension(0, 1, 1));

	Iterator in(_input, win);
	Iterator out(_output, win);

	if(_bias != nullptr)
	{
	Window win_biases;
	win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
	win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));

	Iterator bias(_bias, win_biases);
	execute_window_loop(win, [&](const Coordinates &)
	{
	// Compute 16 elements per iteration
	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
	}
	};

	const int32x4x4_t bias_s32 =
	{
	{
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
	}
	};

	// Add the bias to GEMM's result
	in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
	in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
	in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
	in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);

	// Add the offset terms to GEMM's result and multiply by result_mult_int
	scale_input(in_s32, result_offset_s32, _result_mult_int);

	vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
	}

	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	const int bias_value = (reinterpret_cast<const int >(bias.ptr()) + x);
	int in_value = (reinterpret_cast<const int >(in.ptr()) + x);

	// Quantize
	in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift;

	// Finalize and store the result
	if(is_bounded_relu)
	{
	*(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
	}
	else
	{
	*(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
	}
	}
	},
	in, bias, out);
	}
	else
	{
	execute_window_loop(win, [&](const Coordinates &)
	{
	// Compute 16 elements per iteration
	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
	}
	};

	// Add the offset terms to GEMM's result and multiply by result_mult_int
	scale_input(in_s32, result_offset_s32, _result_mult_int);

	vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
	}

	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	int in_value = (reinterpret_cast<const int >(in.ptr()) + x);

	// Quantize
	in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift;

	// Finalize and store the result
	if(is_bounded_relu)
	{
	*(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
	}
	else
	{
	*(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
	}
	}
	},
	in, out);
	}
	}

	NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel()
	: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_offset(0), _result_mult_int(0), _result_shift(0), _min(0), _max(0)
	{
	}

	void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor input, const ITensor bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

	// Output auto inizialitation if not yet initialized
	auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8));

	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
	(bias != nullptr) ? bias->info() : nullptr,
	output->info(),
	min,
	max));

	_input = input;
	_bias = bias;
	_output = output;
	_result_offset = result_offset;
	_result_mult_int = result_mult_int;
	_result_shift = result_shift;
	_min = min;
	_max = max;

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	INEKernel::configure(win_config.second);

	// Check if we need to clamp the result using min and max
	const bool is_bounded_relu = ((min != max) && !(min == 0 && max == 255));
	_func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<false>;
	}

	Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output, int min, int max)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
	(bias != nullptr) ? bias->clone().get() : nullptr,
	output->clone().get())
	.first);

	return Status{};
	}

	void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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);

	(this->*_func)(window);
	}