src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.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 "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CPP/Validate.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/NEON/NEAsymm.h"
 #include "arm_compute/core/NEON/NEFixedPoint.h"
 #include "arm_compute/core/NEON/wrapper/wrapper.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "arm_compute/core/utils/misc/Traits.h"

 #include <arm_neon.h>
 #include <cstddef>
 #include <cstdint>

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
                           const DirectConvolutionLayerOutputStageKernelInfo &info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::S32, DataType::F32);

     if(bias != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
         ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)));
         ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
     }

     if(input->data_type() == DataType::S32)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(output == nullptr, "In-place computation not allowed for quantized output");
     }

     // Checks performed when output is configured
     if((output != nullptr) && (output->total_size() != 0))
     {
         if(is_data_type_float(input->data_type()))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
         }
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
     }
     else if(input->data_type() == DataType::S32)
     {
         // In case of quantized computation and unconfigured output, the output data type must be provided through DirectConvolutionLayerOutputStageKernelInfo
         ARM_COMPUTE_RETURN_ERROR_ON((info.output_data_type != DataType::QASYMM8) && (info.output_data_type != DataType::QASYMM8_SIGNED));
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output,
                                                         const DirectConvolutionLayerOutputStageKernelInfo &info)
 {
     ARM_COMPUTE_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);

     const DataType data_type = input->data_type();

     // Auto-initialize output output if required
     if(output != nullptr)
     {
         // Work out expected output data type
         const DataType output_dt = (data_type == DataType::S32) ? info.output_data_type : data_type;
         // Output tensor auto initialization if not yet initialized
         auto_init_if_empty(*output, input->clone()->set_data_type(output_dt));
     }

     bool         window_changed                    = false;
     unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(data_type);

     // Update processed elements when input is S32 (comes from quantization input)
     if(data_type == DataType::S32)
     {
         num_elems_processed_per_iteration = 16;
     }

     // Configure kernel window
     Window                 win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
     AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

     if(output != nullptr && (output->total_size() != 0))
     {
         AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

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

         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
     }
     else
     {
         if(bias == nullptr)
         {
             window_changed = update_window_and_padding(win, input_access);
         }
         else
         {
             if(input->data_layout() == DataLayout::NCHW)
             {
                 AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
                 window_changed = update_window_and_padding(win, input_access, bias_access);
             }
             else
             {
                 AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration);
                 window_changed = update_window_and_padding(win, input_access, bias_access);
             }
         }

         input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
     }

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

 template <typename T, bool has_bias>
 typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
 output_stage_nchw(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                   int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     /** NEON vector tag type. */
     using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

     ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
     ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
     ARM_COMPUTE_UNUSED(result_shift);
     ARM_COMPUTE_UNUSED(result_offset_after_shift);

     Iterator in(input, window);
     Iterator out(output, window);
     execute_window_loop(window, [&](const Coordinates & id)
     {
         // Get bias and pointer to input
         const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
         auto       v_in   = wrapper::vloadq(in_ptr);

         // Accumulate bias
         if(has_bias)
         {
             const auto vb = wrapper::vdup_n(*reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z()))), ExactTagType{});
             v_in          = wrapper::vadd(v_in, vb);
         }

         const auto out_ptr = reinterpret_cast<T *>(out.ptr());
         wrapper::vstore(out_ptr, v_in);
     },
     in, out);
 }

 template <typename T, bool has_bias>
 typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
 output_stage_nhwc(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                   int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
     ARM_COMPUTE_UNUSED(result_shift);
     ARM_COMPUTE_UNUSED(result_offset_after_shift);

     Window window_bias = window;
     window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
     window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
     window_bias.set(3, Window::Dimension(0, 0, 0));

     Iterator in(input, window);
     Iterator bi(bias, window_bias);
     Iterator out(output, window);
     execute_window_loop(window, [&](const Coordinates &)
     {
         // Get bias and pointer to input
         const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
         auto       v_in   = wrapper::vloadq(in_ptr);

         // Accumulate bias
         if(has_bias)
         {
             const auto bias_ptr = reinterpret_cast<T *>(bi.ptr());
             v_in                = wrapper::vadd(v_in, wrapper::vloadq(bias_ptr));
         }

         const auto out_ptr = reinterpret_cast<T *>(out.ptr());
         wrapper::vstore(out_ptr, v_in);

     },
     in, bi, out);
 }

 // Quantized case
 template < typename TOut, bool has_bias, typename std::enable_if < std::is_same<TOut, uint8_t>::value || std::is_same<TOut, int8_t>::value, int >::type = 0 >
 void output_stage_nchw(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                        int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     using VectorType = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>;
     using TagType    = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>;

     const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);

     const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
     const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});

     Iterator in(input, window);
     Iterator out(output, window);

     execute_window_loop(window, [&](const Coordinates & id)
     {
         // Get bias and pointer to input
         const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr());
         int32x4x4_t v_in =
         {
             {
                 wrapper::vloadq(in_ptr),
                 wrapper::vloadq(in_ptr + 4),
                 wrapper::vloadq(in_ptr + 8),
                 wrapper::vloadq(in_ptr + 12)
             }
         };

         // Accumulate bias
         if(has_bias)
         {
             const auto vb = wrapper::vdup_n(*reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))), TagType{});
             v_in =
             {
                 {
                     wrapper::vadd(v_in.val[0], vb),
                     wrapper::vadd(v_in.val[1], vb),
                     wrapper::vadd(v_in.val[2], vb),
                     wrapper::vadd(v_in.val[3], vb)
                 }
             };
         }

         const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
         wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
     },
     in, out);
 }
 template < typename TOut, bool has_bias, typename std::enable_if < std::is_same<TOut, uint8_t>::value || std::is_same<TOut, int8_t>::value, int >::type = 0 >
 void output_stage_nhwc(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                        int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     using VectorType = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>;
     using TagType    = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>;

     const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);

     const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
     const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});

     Window window_bias = window;
     window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
     window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
     window_bias.set(3, Window::Dimension(0, 0, 0));

     Iterator in(input, window);
     Iterator bi(bias, window_bias);

     Iterator out(output, window);
     execute_window_loop(window, [&](const Coordinates &)
     {
         // Get bias and pointer to input
         const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr());
         int32x4x4_t v_in =
         {
             {
                 wrapper::vloadq(in_ptr),
                 wrapper::vloadq(in_ptr + 4),
                 wrapper::vloadq(in_ptr + 8),
                 wrapper::vloadq(in_ptr + 12),
             }
         };

         // Accumulate bias
         if(has_bias)
         {
             const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr());

             wrapper::vadd(v_in.val[0], wrapper::vloadq(bias_ptr));
             wrapper::vadd(v_in.val[1], wrapper::vloadq(bias_ptr + 4));
             wrapper::vadd(v_in.val[2], wrapper::vloadq(bias_ptr + 8));
             wrapper::vadd(v_in.val[3], wrapper::vloadq(bias_ptr + 12));
         }

         const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
         wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
     },
     in, bi, out);
 }
 } // namespace

 NEDirectConvolutionLayerOutputStageKernel::NEDirectConvolutionLayerOutputStageKernel()
     : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0)
 {
 }

 void NEDirectConvolutionLayerOutputStageKernel::configure(ITensor *input, const ITensor *bias, ITensor *output,
                                                           const DirectConvolutionLayerOutputStageKernelInfo &info)
 {
     // Perform validation step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info(), info));

     _func                         = nullptr;
     _bias                         = bias;
     _input                        = input;
     _output                       = (output != nullptr) ? output : input;
     _result_fixedpoint_multiplier = info.result_fixedpoint_multiplier;
     _result_shift                 = info.result_shift;
     _result_offset_after_shift    = info.result_offset_after_shift;

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

     const bool has_bias          = bias != nullptr;
     const bool is_qasymm8_signed = (output != nullptr) ? is_data_type_quantized_asymmetric_signed(output->info()->data_type()) : false;

     // Set appropriate function
     if(input->info()->data_layout() == DataLayout::NCHW)
     {
         switch(input->info()->data_type())
         {
             case DataType::S32:
             {
                 if(is_qasymm8_signed)
                 {
                     _func = (has_bias) ? &output_stage_nchw<int8_t, true> : &output_stage_nchw<int8_t, false>;
                 }
                 else
                 {
                     _func = (has_bias) ? &output_stage_nchw<uint8_t, true> : &output_stage_nchw<uint8_t, false>;
                 }
                 break;
             }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
             case DataType::F16:
             {
                 _func = (has_bias) ? &output_stage_nchw<float16_t, true> : &output_stage_nchw<float16_t, false>;
                 break;
             }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
             case DataType::F32:
             {
                 _func = (has_bias) ? &output_stage_nchw<float, true> : &output_stage_nchw<float, false>;
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
             }
         }
     }
     else
     {
         switch(input->info()->data_type())
         {
             case DataType::S32:
             {
                 if(is_qasymm8_signed)
                 {
                     _func = (has_bias) ? &output_stage_nhwc<int8_t, true> : &output_stage_nhwc<int8_t, false>;
                 }
                 else
                 {
                     _func = (has_bias) ? &output_stage_nhwc<uint8_t, true> : &output_stage_nhwc<uint8_t, false>;
                 }
                 break;
             }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
             case DataType::F16:
             {
                 _func = (has_bias) ? &output_stage_nhwc<float16_t, true> : &output_stage_nhwc<float16_t, false>;
                 break;
             }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
             case DataType::F32:
             {
                 _func = (has_bias) ? &output_stage_nhwc<float, true> : &output_stage_nhwc<float, false>;
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
             }
         }
     }
 }

 Status NEDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
                                                            const DirectConvolutionLayerOutputStageKernelInfo &info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
                                                               bias == nullptr ? nullptr : bias->clone().get(),
                                                               output == nullptr ? nullptr : output->clone().get(),
                                                               info)
                                 .first);

     return Status{};
 }

 void NEDirectConvolutionLayerOutputStageKernel::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);
     ARM_COMPUTE_ERROR_ON(_func == nullptr);

     (*_func)(_input, _bias, window, _output, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift);
 }
 } // 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 "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/CPP/Validate.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/NEON/NEAsymm.h"
	#include "arm_compute/core/NEON/NEFixedPoint.h"
	#include "arm_compute/core/NEON/wrapper/wrapper.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"
	#include "arm_compute/core/utils/misc/Traits.h"

	#include <arm_neon.h>
	#include <cstddef>
	#include <cstdint>

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output,
	const DirectConvolutionLayerOutputStageKernelInfo &info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::S32, DataType::F32);

	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
	ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)));
	ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
	}

	if(input->data_type() == DataType::S32)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(output == nullptr, "In-place computation not allowed for quantized output");
	}

	// Checks performed when output is configured
	if((output != nullptr) && (output->total_size() != 0))
	{
	if(is_data_type_float(input->data_type()))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	}
	else if(input->data_type() == DataType::S32)
	{
	// In case of quantized computation and unconfigured output, the output data type must be provided through DirectConvolutionLayerOutputStageKernelInfo
	ARM_COMPUTE_RETURN_ERROR_ON((info.output_data_type != DataType::QASYMM8) && (info.output_data_type != DataType::QASYMM8_SIGNED));
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo bias, ITensorInfo *output,
	const DirectConvolutionLayerOutputStageKernelInfo &info)
	{
	ARM_COMPUTE_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);

	const DataType data_type = input->data_type();

	// Auto-initialize output output if required
	if(output != nullptr)
	{
	// Work out expected output data type
	const DataType output_dt = (data_type == DataType::S32) ? info.output_data_type : data_type;
	// Output tensor auto initialization if not yet initialized
	auto_init_if_empty(*output, input->clone()->set_data_type(output_dt));
	}

	bool window_changed = false;
	unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(data_type);

	// Update processed elements when input is S32 (comes from quantization input)
	if(data_type == DataType::S32)
	{
	num_elems_processed_per_iteration = 16;
	}

	// Configure kernel window
	Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
	AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

	if(output != nullptr && (output->total_size() != 0))
	{
	AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

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

	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	else
	{
	if(bias == nullptr)
	{
	window_changed = update_window_and_padding(win, input_access);
	}
	else
	{
	if(input->data_layout() == DataLayout::NCHW)
	{
	AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
	window_changed = update_window_and_padding(win, input_access, bias_access);
	}
	else
	{
	AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration);
	window_changed = update_window_and_padding(win, input_access, bias_access);
	}
	}

	input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
	}

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

	template <typename T, bool has_bias>
	typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
	output_stage_nchw(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	/** NEON vector tag type. */
	using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

	ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
	ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
	ARM_COMPUTE_UNUSED(result_shift);
	ARM_COMPUTE_UNUSED(result_offset_after_shift);

	Iterator in(input, window);
	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates & id)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
	auto v_in = wrapper::vloadq(in_ptr);

	// Accumulate bias
	if(has_bias)
	{
	const auto vb = wrapper::vdup_n(reinterpret_cast<const T >(bias->ptr_to_element(Coordinates(id.z()))), ExactTagType{});
	v_in = wrapper::vadd(v_in, vb);
	}

	const auto out_ptr = reinterpret_cast<T *>(out.ptr());
	wrapper::vstore(out_ptr, v_in);
	},
	in, out);
	}

	template <typename T, bool has_bias>
	typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
	output_stage_nhwc(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
	ARM_COMPUTE_UNUSED(result_shift);
	ARM_COMPUTE_UNUSED(result_offset_after_shift);

	Window window_bias = window;
	window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
	window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
	window_bias.set(3, Window::Dimension(0, 0, 0));

	Iterator in(input, window);
	Iterator bi(bias, window_bias);
	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
	auto v_in = wrapper::vloadq(in_ptr);

	// Accumulate bias
	if(has_bias)
	{
	const auto bias_ptr = reinterpret_cast<T *>(bi.ptr());
	v_in = wrapper::vadd(v_in, wrapper::vloadq(bias_ptr));
	}

	const auto out_ptr = reinterpret_cast<T *>(out.ptr());
	wrapper::vstore(out_ptr, v_in);

	},
	in, bi, out);
	}

	// Quantized case
	template < typename TOut, bool has_bias, typename std::enable_if < std::is_same<TOut, uint8_t>::value \|\| std::is_same<TOut, int8_t>::value, int >::type = 0 >
	void output_stage_nchw(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	using VectorType = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>;
	using TagType = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>;

	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);

	const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
	const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});

	Iterator in(input, window);
	Iterator out(output, window);

	execute_window_loop(window, [&](const Coordinates & id)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());
	int32x4x4_t v_in =
	{
	{
	wrapper::vloadq(in_ptr),
	wrapper::vloadq(in_ptr + 4),
	wrapper::vloadq(in_ptr + 8),
	wrapper::vloadq(in_ptr + 12)
	}
	};

	// Accumulate bias
	if(has_bias)
	{
	const auto vb = wrapper::vdup_n(reinterpret_cast<const int32_t >(bias->ptr_to_element(Coordinates(id.z()))), TagType{});
	v_in =
	{
	{
	wrapper::vadd(v_in.val[0], vb),
	wrapper::vadd(v_in.val[1], vb),
	wrapper::vadd(v_in.val[2], vb),
	wrapper::vadd(v_in.val[3], vb)
	}
	};
	}

	const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
	wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
	},
	in, out);
	}
	template < typename TOut, bool has_bias, typename std::enable_if < std::is_same<TOut, uint8_t>::value \|\| std::is_same<TOut, int8_t>::value, int >::type = 0 >
	void output_stage_nhwc(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	using VectorType = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>;
	using TagType = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>;

	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);

	const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
	const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});

	Window window_bias = window;
	window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
	window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
	window_bias.set(3, Window::Dimension(0, 0, 0));

	Iterator in(input, window);
	Iterator bi(bias, window_bias);

	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());
	int32x4x4_t v_in =
	{
	{
	wrapper::vloadq(in_ptr),
	wrapper::vloadq(in_ptr + 4),
	wrapper::vloadq(in_ptr + 8),
	wrapper::vloadq(in_ptr + 12),
	}
	};

	// Accumulate bias
	if(has_bias)
	{
	const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr());

	wrapper::vadd(v_in.val[0], wrapper::vloadq(bias_ptr));
	wrapper::vadd(v_in.val[1], wrapper::vloadq(bias_ptr + 4));
	wrapper::vadd(v_in.val[2], wrapper::vloadq(bias_ptr + 8));
	wrapper::vadd(v_in.val[3], wrapper::vloadq(bias_ptr + 12));
	}

	const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
	wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
	},
	in, bi, out);
	}
	} // namespace

	NEDirectConvolutionLayerOutputStageKernel::NEDirectConvolutionLayerOutputStageKernel()
	: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0)
	{
	}

	void NEDirectConvolutionLayerOutputStageKernel::configure(ITensor input, const ITensor bias, ITensor *output,
	const DirectConvolutionLayerOutputStageKernelInfo &info)
	{
	// Perform validation step
	ARM_COMPUTE_ERROR_ON_NULLPTR(input);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info(), info));

	_func = nullptr;
	_bias = bias;
	_input = input;
	_output = (output != nullptr) ? output : input;
	_result_fixedpoint_multiplier = info.result_fixedpoint_multiplier;
	_result_shift = info.result_shift;
	_result_offset_after_shift = info.result_offset_after_shift;

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

	const bool has_bias = bias != nullptr;
	const bool is_qasymm8_signed = (output != nullptr) ? is_data_type_quantized_asymmetric_signed(output->info()->data_type()) : false;

	// Set appropriate function
	if(input->info()->data_layout() == DataLayout::NCHW)
	{
	switch(input->info()->data_type())
	{
	case DataType::S32:
	{
	if(is_qasymm8_signed)
	{
	_func = (has_bias) ? &output_stage_nchw<int8_t, true> : &output_stage_nchw<int8_t, false>;
	}
	else
	{
	_func = (has_bias) ? &output_stage_nchw<uint8_t, true> : &output_stage_nchw<uint8_t, false>;
	}
	break;
	}
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	{
	_func = (has_bias) ? &output_stage_nchw<float16_t, true> : &output_stage_nchw<float16_t, false>;
	break;
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
	case DataType::F32:
	{
	_func = (has_bias) ? &output_stage_nchw<float, true> : &output_stage_nchw<float, false>;
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
	}
	}
	}
	else
	{
	switch(input->info()->data_type())
	{
	case DataType::S32:
	{
	if(is_qasymm8_signed)
	{
	_func = (has_bias) ? &output_stage_nhwc<int8_t, true> : &output_stage_nhwc<int8_t, false>;
	}
	else
	{
	_func = (has_bias) ? &output_stage_nhwc<uint8_t, true> : &output_stage_nhwc<uint8_t, false>;
	}
	break;
	}
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	{
	_func = (has_bias) ? &output_stage_nhwc<float16_t, true> : &output_stage_nhwc<float16_t, false>;
	break;
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
	case DataType::F32:
	{
	_func = (has_bias) ? &output_stage_nhwc<float, true> : &output_stage_nhwc<float, false>;
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
	}
	}
	}
	}

	Status NEDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output,
	const DirectConvolutionLayerOutputStageKernelInfo &info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
	bias == nullptr ? nullptr : bias->clone().get(),
	output == nullptr ? nullptr : output->clone().get(),
	info)
	.first);

	return Status{};
	}

	void NEDirectConvolutionLayerOutputStageKernel::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);
	ARM_COMPUTE_ERROR_ON(_func == nullptr);

	(*_func)(_input, _bias, window, _output, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift);
	}
	} // namespace arm_compute