src/core/NEON/kernels/NEBatchNormalizationLayerKernel.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/NEBatchNormalizationLayerKernel.h"

 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "src/core/CPP/Validate.h"
 #include "src/core/NEON/NEFixedPoint.h"
 #include "src/core/NEON/NEMath.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 #include "src/core/NEON/kernels/detail/NEActivationFunctionDetail.h"
 #include "src/core/NEON/wrapper/wrapper.h"

 #include <map>

 namespace arm_compute
 {
 namespace
 {
 Status
 validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var,
                    const ITensorInfo *beta, const ITensorInfo *gamma, float epsilon, ActivationLayerInfo act_info)
 {
     ARM_COMPUTE_UNUSED(epsilon);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);

     if(act_info.enabled())
     {
         ActivationLayerInfo::ActivationFunction act = act_info.activation();
         ARM_COMPUTE_RETURN_ERROR_ON(act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU
                                     && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU
                                     && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU);
         ARM_COMPUTE_RETURN_ERROR_ON(act_info.b() > act_info.a());
     }

     if(nullptr != output)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     }

     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var);
     if(beta != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
     }
     if(gamma != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
     }
     ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0));

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *mean, ITensorInfo *var, ITensorInfo *gamma, ITensorInfo *beta)
 {
     ARM_COMPUTE_UNUSED(mean, var, gamma, beta);

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

     if(output != nullptr)
     {
         // Output auto initialization if not yet initialized
         auto_init_if_empty(*output, *input->clone());

         // NEBatchNormalizationLayerKernel doesn't need padding so update_window_and_padding() can be skipped
         Coordinates coord;
         coord.set_num_dimensions(output->num_dimensions());
         output->set_valid_region(ValidRegion(coord, output->tensor_shape()));
     }

     return std::make_pair(Status{}, win);
 }
 } //namespace

 template <typename T, bool fused_activation, typename F>
 void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &window)
 {
     /** NEON vector tag type. */
     using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

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

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

     Iterator input(_input, win_to_use);
     Iterator output(_output, win_to_use);

     F activation_functor(_act_info);

     // Hold information about the current feature map we are iterating.
     // Only compute denominator and NEON vectors once per feature map.
     int slice = -1;

     const auto input_mean  = reinterpret_cast<const T *>(_mean->ptr_to_element(Coordinates(0, 0)));
     const auto input_var   = reinterpret_cast<const T *>(_var->ptr_to_element(Coordinates(0, 0)));
     const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const T *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
     const auto input_beta  = (_beta != nullptr) ? reinterpret_cast<const T *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;

     T mean        = static_cast<T>(0);
     T var         = static_cast<T>(0);
     T gamma       = static_cast<T>(1);
     T beta        = static_cast<T>(0);
     T denominator = static_cast<T>(0);

     auto       mean_vec        = wrapper::vdup_n(mean, ExactTagType{});
     auto       var_vec         = wrapper::vdup_n(var, ExactTagType{});
     auto       gamma_vec       = wrapper::vdup_n(gamma, ExactTagType{});
     auto       beta_vec        = wrapper::vdup_n(beta, ExactTagType{});
     auto       denominator_vec = wrapper::vdup_n(denominator, ExactTagType{});
     const auto epsilon_vec     = wrapper::vdup_n(static_cast<T>(_epsilon), ExactTagType{});
     execute_window_loop(win_to_use, [&](const Coordinates & id)
     {
         const auto input_ptr  = reinterpret_cast<const T *>(input.ptr());
         const auto output_ptr = reinterpret_cast<T *>(output.ptr());

         if(slice != id.z())
         {
             mean     = input_mean[id.z()];
             var      = input_var[id.z()];
             mean_vec = wrapper::vdup_n(mean, ExactTagType{});
             var_vec  = wrapper::vdup_n(var, ExactTagType{});
             if(input_gamma != nullptr)
             {
                 gamma     = input_gamma[id.z()];
                 gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
             }
             if(input_beta != nullptr)
             {
                 beta     = input_beta[id.z()];
                 beta_vec = wrapper::vdup_n(beta, ExactTagType{});
             }

             // Calculate denominator
             denominator_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
             denominator     = wrapper::vgetlane(denominator_vec, 0);
             slice           = id.z();
         }

         // Perform core calculations using vector operations
         int x = window_start_x;
         for(; x <= (window_end_x - window_step_x); x += window_step_x)
         {
             // Calculate x bar
             const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec);
             const auto x_bar     = wrapper::vmul(numerator, denominator_vec);
             auto       res       = wrapper::vmla(beta_vec, x_bar, gamma_vec);

             // Perform fused activation
             if(fused_activation)
             {
                 activation_functor(res);
             }

             // Store results
             wrapper::vstore(output_ptr + x, res);
         }

         // Compute left-over elements
         for(; x < window_end_x; ++x)
         {
             const T numerator = input_ptr[x] - mean;
             const T x_bar     = numerator * denominator;
             T       res       = beta + x_bar * gamma;

             // Perform fused activation
             if(fused_activation)
             {
                 activation_functor(res);
             }

             // Store results
             *(output_ptr + x) = res;
         }
     },
     input, output);
 }

 template <typename T, bool fused_activation, typename F>
 void NEBatchNormalizationLayerKernel::batch_normalization_nhwc(const Window &window)
 {
     /** NEON vector tag type. */
     using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

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

     Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
     win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

     Iterator input(_input, win_collapsed);
     Iterator output(_output, win_collapsed);

     F activation_functor(_act_info);

     const auto input_mean  = reinterpret_cast<const T *>(_mean->ptr_to_element(Coordinates(0, 0)));
     const auto input_var   = reinterpret_cast<const T *>(_var->ptr_to_element(Coordinates(0, 0)));
     const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const T *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
     const auto input_beta  = (_beta != nullptr) ? reinterpret_cast<const T *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;

     const auto epsilon_vec = wrapper::vdup_n(static_cast<T>(_epsilon), ExactTagType{});
     execute_window_loop(win_collapsed, [&](const Coordinates &)
     {
         const auto input_ptr  = reinterpret_cast<const T *>(input.ptr());
         const auto output_ptr = reinterpret_cast<T *>(output.ptr());

         // Perform core calculations using vector operations
         int x = window_start_x;
         for(; x <= (window_end_x - window_step_x); x += window_step_x)
         {
             // Conctruct vectors
             const auto mean_vec  = wrapper::vloadq(input_mean + x);
             const auto var_vec   = wrapper::vloadq(input_var + x);
             const auto gamma_vec = (input_gamma != nullptr) ? wrapper::vloadq(input_gamma + x) : wrapper::vdup_n(static_cast<T>(1.f), ExactTagType{});
             const auto beta_vec  = (input_beta != nullptr) ? wrapper::vloadq(input_beta + x) : wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});

             // Calculate denominator
             const auto denominator = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));

             // Calculate x bar
             const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec);
             const auto x_bar     = wrapper::vmul(numerator, denominator);
             auto       res       = wrapper::vmla(beta_vec, x_bar, gamma_vec);

             // Perform fused activation
             if(fused_activation)
             {
                 activation_functor(res);
             }

             // Store results
             wrapper::vstore(output_ptr + x, res);
         }

         // Compute left-over elements
         for(; x < window_end_x; ++x)
         {
             // Conctruct vectors
             const T gamma = (input_gamma != nullptr) ? input_gamma[x] : 1.f;
             const T beta  = (input_beta != nullptr) ? input_beta[x] : 0.f;

             const T denominator = sqrt(input_var[x] + _epsilon);
             const T numerator   = input_ptr[x] - input_mean[x];
             const T x_bar       = numerator / denominator;
             T       res         = beta + x_bar * gamma;

             // Perform fused activation
             if(fused_activation)
             {
                 activation_functor(res);
             }

             // Store results
             *reinterpret_cast<T *>(output_ptr + x) = res;
         }
     },
     input, output);
 }

 void NEBatchNormalizationLayerKernel::configure_non_fused()
 {
     const bool is_nhwc = _input->info()->data_layout() == DataLayout::NHWC;
     switch(_input->info()->data_type())
     {
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F16:
             _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, false, detail::dummy<float16_t, 8>> :
                     &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, false, detail::dummy<float16_t, 8>>;
             break;
 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F32:
             _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, false, detail::dummy<float, 4>> :
                     &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, false, detail::dummy<float, 4>>;
             break;
         default:
             ARM_COMPUTE_ERROR("Element size not supported");
             break;
     }
 }

 void NEBatchNormalizationLayerKernel::configure_fused()
 {
     // NCHW Fused Batched Normalization with activation functions : FP32
     static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw =
     {
         { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::relu<float, 4>> },
         { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::brelu<float, 4>> },
         { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::lubrelu<float, 4>> }
     };
     // NHWC Fused Batched Normalization with activation functions : FP32
     static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nhwc =
     {
         { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::relu<float, 4>> },
         { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::brelu<float, 4>> },
         { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::lubrelu<float, 4>> }
     };
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
     // NCHW Fused Batched Normalization with activation functions : FP16
     static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw =
     {
         { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::relu<float16_t, 8>> },
         { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::brelu<float16_t, 8>> },
         { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::lubrelu<float16_t, 8>> }
     };
     // NHWC Fused Batched Normalization with activation functions : FP16
     static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nhwc =
     {
         { ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::relu<float16_t, 8>> },
         { ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::brelu<float16_t, 8>> },
         { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::lubrelu<float16_t, 8>> }
     };
 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

     switch(_input->info()->data_type())
     {
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F16:
             _func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f16_nhwc[_act_info.activation()] : bn_fused_map_f16_nchw[_act_info.activation()];
             break;
 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F32:
             _func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f32_nhwc[_act_info.activation()] : bn_fused_map_f32_nchw[_act_info.activation()];
             break;
         default:
             ARM_COMPUTE_ERROR("Element size not supported");
             break;
     }
 }

 NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
     : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon(), _act_info()
 {
 }

 void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output,
                                                 const ITensor *mean, const ITensor *var,
                                                 const ITensor *beta, const ITensor *gamma,
                                                 float epsilon, ActivationLayerInfo act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var);

     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr,
                                                   mean->info(), var->info(),
                                                   (beta != nullptr) ? beta->info() : nullptr,
                                                   (gamma != nullptr) ? gamma->info() : nullptr,
                                                   epsilon, act_info));

     _input    = input;
     _output   = input;
     _mean     = mean;
     _var      = var;
     _gamma    = gamma;
     _beta     = beta;
     _epsilon  = epsilon;
     _act_info = act_info;

     const bool run_in_place = (output == nullptr) || (output == input);
     if(!run_in_place)
     {
         _output = output;
     }

     // Configure activation function to run
     if(_act_info.enabled())
     {
         configure_fused();
     }
     else
     {
         configure_non_fused();
     }

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), (run_in_place) ? nullptr : output->info(), mean->info(), var->info(), (gamma != nullptr) ? gamma->info() : nullptr,
                                                     (beta != nullptr) ? beta->info() : nullptr);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     INEKernel::configure(win_config.second);
 }

 Status NEBatchNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output,
                                                  const ITensorInfo *mean, const ITensorInfo *var,
                                                  const ITensorInfo *beta, const ITensorInfo *gamma,
                                                  float epsilon, ActivationLayerInfo act_info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon, act_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output ? output->clone().get() : nullptr, mean->clone().get(), var->clone().get(),
                                                               (gamma != nullptr) ? gamma->clone().get() : nullptr, (beta != nullptr) ? beta->clone().get() : nullptr)
                                 .first);

     return Status{};
 }

 void NEBatchNormalizationLayerKernel::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);

     (this->*_func)(window);
 }
 } // 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/NEBatchNormalizationLayerKernel.h"

	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"
	#include "src/core/CPP/Validate.h"
	#include "src/core/NEON/NEFixedPoint.h"
	#include "src/core/NEON/NEMath.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	#include "src/core/NEON/kernels/detail/NEActivationFunctionDetail.h"
	#include "src/core/NEON/wrapper/wrapper.h"

	#include <map>

	namespace arm_compute
	{
	namespace
	{
	Status
	validate_arguments(const ITensorInfo input, const ITensorInfo output, const ITensorInfo mean, const ITensorInfo var,
	const ITensorInfo beta, const ITensorInfo gamma, float epsilon, ActivationLayerInfo act_info)
	{
	ARM_COMPUTE_UNUSED(epsilon);
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);

	if(act_info.enabled())
	{
	ActivationLayerInfo::ActivationFunction act = act_info.activation();
	ARM_COMPUTE_RETURN_ERROR_ON(act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU
	&& act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU
	&& act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU);
	ARM_COMPUTE_RETURN_ERROR_ON(act_info.b() > act_info.a());
	}

	if(nullptr != output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}

	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var);
	if(beta != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
	}
	if(gamma != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
	}
	ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0));

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo output, ITensorInfo mean, ITensorInfo var, ITensorInfo gamma, ITensorInfo beta)
	{
	ARM_COMPUTE_UNUSED(mean, var, gamma, beta);

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

	if(output != nullptr)
	{
	// Output auto initialization if not yet initialized
	auto_init_if_empty(output, input->clone());

	// NEBatchNormalizationLayerKernel doesn't need padding so update_window_and_padding() can be skipped
	Coordinates coord;
	coord.set_num_dimensions(output->num_dimensions());
	output->set_valid_region(ValidRegion(coord, output->tensor_shape()));
	}

	return std::make_pair(Status{}, win);
	}
	} //namespace

	template <typename T, bool fused_activation, typename F>
	void NEBatchNormalizationLayerKernel::batch_normalization_nchw(const Window &window)
	{
	/** NEON vector tag type. */
	using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

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

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

	Iterator input(_input, win_to_use);
	Iterator output(_output, win_to_use);

	F activation_functor(_act_info);

	// Hold information about the current feature map we are iterating.
	// Only compute denominator and NEON vectors once per feature map.
	int slice = -1;

	const auto input_mean = reinterpret_cast<const T *>(_mean->ptr_to_element(Coordinates(0, 0)));
	const auto input_var = reinterpret_cast<const T *>(_var->ptr_to_element(Coordinates(0, 0)));
	const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const T *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
	const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const T *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;

	T mean = static_cast<T>(0);
	T var = static_cast<T>(0);
	T gamma = static_cast<T>(1);
	T beta = static_cast<T>(0);
	T denominator = static_cast<T>(0);

	auto mean_vec = wrapper::vdup_n(mean, ExactTagType{});
	auto var_vec = wrapper::vdup_n(var, ExactTagType{});
	auto gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
	auto beta_vec = wrapper::vdup_n(beta, ExactTagType{});
	auto denominator_vec = wrapper::vdup_n(denominator, ExactTagType{});
	const auto epsilon_vec = wrapper::vdup_n(static_cast<T>(_epsilon), ExactTagType{});
	execute_window_loop(win_to_use, [&](const Coordinates & id)
	{
	const auto input_ptr = reinterpret_cast<const T *>(input.ptr());
	const auto output_ptr = reinterpret_cast<T *>(output.ptr());

	if(slice != id.z())
	{
	mean = input_mean[id.z()];
	var = input_var[id.z()];
	mean_vec = wrapper::vdup_n(mean, ExactTagType{});
	var_vec = wrapper::vdup_n(var, ExactTagType{});
	if(input_gamma != nullptr)
	{
	gamma = input_gamma[id.z()];
	gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
	}
	if(input_beta != nullptr)
	{
	beta = input_beta[id.z()];
	beta_vec = wrapper::vdup_n(beta, ExactTagType{});
	}

	// Calculate denominator
	denominator_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
	denominator = wrapper::vgetlane(denominator_vec, 0);
	slice = id.z();
	}

	// Perform core calculations using vector operations
	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	// Calculate x bar
	const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec);
	const auto x_bar = wrapper::vmul(numerator, denominator_vec);
	auto res = wrapper::vmla(beta_vec, x_bar, gamma_vec);

	// Perform fused activation
	if(fused_activation)
	{
	activation_functor(res);
	}

	// Store results
	wrapper::vstore(output_ptr + x, res);
	}

	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	const T numerator = input_ptr[x] - mean;
	const T x_bar = numerator * denominator;
	T res = beta + x_bar * gamma;

	// Perform fused activation
	if(fused_activation)
	{
	activation_functor(res);
	}

	// Store results
	*(output_ptr + x) = res;
	}
	},
	input, output);
	}

	template <typename T, bool fused_activation, typename F>
	void NEBatchNormalizationLayerKernel::batch_normalization_nhwc(const Window &window)
	{
	/** NEON vector tag type. */
	using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

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

	Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
	win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

	Iterator input(_input, win_collapsed);
	Iterator output(_output, win_collapsed);

	F activation_functor(_act_info);

	const auto input_mean = reinterpret_cast<const T *>(_mean->ptr_to_element(Coordinates(0, 0)));
	const auto input_var = reinterpret_cast<const T *>(_var->ptr_to_element(Coordinates(0, 0)));
	const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const T *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
	const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const T *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;

	const auto epsilon_vec = wrapper::vdup_n(static_cast<T>(_epsilon), ExactTagType{});
	execute_window_loop(win_collapsed, [&](const Coordinates &)
	{
	const auto input_ptr = reinterpret_cast<const T *>(input.ptr());
	const auto output_ptr = reinterpret_cast<T *>(output.ptr());

	// Perform core calculations using vector operations
	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	// Conctruct vectors
	const auto mean_vec = wrapper::vloadq(input_mean + x);
	const auto var_vec = wrapper::vloadq(input_var + x);
	const auto gamma_vec = (input_gamma != nullptr) ? wrapper::vloadq(input_gamma + x) : wrapper::vdup_n(static_cast<T>(1.f), ExactTagType{});
	const auto beta_vec = (input_beta != nullptr) ? wrapper::vloadq(input_beta + x) : wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});

	// Calculate denominator
	const auto denominator = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));

	// Calculate x bar
	const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec);
	const auto x_bar = wrapper::vmul(numerator, denominator);
	auto res = wrapper::vmla(beta_vec, x_bar, gamma_vec);

	// Perform fused activation
	if(fused_activation)
	{
	activation_functor(res);
	}

	// Store results
	wrapper::vstore(output_ptr + x, res);
	}

	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	// Conctruct vectors
	const T gamma = (input_gamma != nullptr) ? input_gamma[x] : 1.f;
	const T beta = (input_beta != nullptr) ? input_beta[x] : 0.f;

	const T denominator = sqrt(input_var[x] + _epsilon);
	const T numerator = input_ptr[x] - input_mean[x];
	const T x_bar = numerator / denominator;
	T res = beta + x_bar * gamma;

	// Perform fused activation
	if(fused_activation)
	{
	activation_functor(res);
	}

	// Store results
	reinterpret_cast<T >(output_ptr + x) = res;
	}
	},
	input, output);
	}

	void NEBatchNormalizationLayerKernel::configure_non_fused()
	{
	const bool is_nhwc = _input->info()->data_layout() == DataLayout::NHWC;
	switch(_input->info()->data_type())
	{
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	_func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, false, detail::dummy<float16_t, 8>> :
	&NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, false, detail::dummy<float16_t, 8>>;
	break;
	#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F32:
	_func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, false, detail::dummy<float, 4>> :
	&NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, false, detail::dummy<float, 4>>;
	break;
	default:
	ARM_COMPUTE_ERROR("Element size not supported");
	break;
	}
	}

	void NEBatchNormalizationLayerKernel::configure_fused()
	{
	// NCHW Fused Batched Normalization with activation functions : FP32
	static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw =
	{
	{ ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::relu<float, 4>> },
	{ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::brelu<float, 4>> },
	{ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float, true, detail::lubrelu<float, 4>> }
	};
	// NHWC Fused Batched Normalization with activation functions : FP32
	static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nhwc =
	{
	{ ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::relu<float, 4>> },
	{ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::brelu<float, 4>> },
	{ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float, true, detail::lubrelu<float, 4>> }
	};
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	// NCHW Fused Batched Normalization with activation functions : FP16
	static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw =
	{
	{ ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::relu<float16_t, 8>> },
	{ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::brelu<float16_t, 8>> },
	{ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nchw<float16_t, true, detail::lubrelu<float16_t, 8>> }
	};
	// NHWC Fused Batched Normalization with activation functions : FP16
	static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nhwc =
	{
	{ ActivationLayerInfo::ActivationFunction::RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::relu<float16_t, 8>> },
	{ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::brelu<float16_t, 8>> },
	{ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, &NEBatchNormalizationLayerKernel::batch_normalization_nhwc<float16_t, true, detail::lubrelu<float16_t, 8>> }
	};
	#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

	switch(_input->info()->data_type())
	{
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	_func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f16_nhwc[_act_info.activation()] : bn_fused_map_f16_nchw[_act_info.activation()];
	break;
	#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F32:
	_func = (_input->info()->data_layout() == DataLayout::NHWC) ? bn_fused_map_f32_nhwc[_act_info.activation()] : bn_fused_map_f32_nchw[_act_info.activation()];
	break;
	default:
	ARM_COMPUTE_ERROR("Element size not supported");
	break;
	}
	}

	NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
	: _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon(), _act_info()
	{
	}

	void NEBatchNormalizationLayerKernel::configure(ITensor input, ITensor output,
	const ITensor mean, const ITensor var,
	const ITensor beta, const ITensor gamma,
	float epsilon, ActivationLayerInfo act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var);

	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr,
	mean->info(), var->info(),
	(beta != nullptr) ? beta->info() : nullptr,
	(gamma != nullptr) ? gamma->info() : nullptr,
	epsilon, act_info));

	_input = input;
	_output = input;
	_mean = mean;
	_var = var;
	_gamma = gamma;
	_beta = beta;
	_epsilon = epsilon;
	_act_info = act_info;

	const bool run_in_place = (output == nullptr) \|\| (output == input);
	if(!run_in_place)
	{
	_output = output;
	}

	// Configure activation function to run
	if(_act_info.enabled())
	{
	configure_fused();
	}
	else
	{
	configure_non_fused();
	}

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), (run_in_place) ? nullptr : output->info(), mean->info(), var->info(), (gamma != nullptr) ? gamma->info() : nullptr,
	(beta != nullptr) ? beta->info() : nullptr);
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	INEKernel::configure(win_config.second);
	}

	Status NEBatchNormalizationLayerKernel::validate(const ITensorInfo input, const ITensorInfo output,
	const ITensorInfo mean, const ITensorInfo var,
	const ITensorInfo beta, const ITensorInfo gamma,
	float epsilon, ActivationLayerInfo act_info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon, act_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output ? output->clone().get() : nullptr, mean->clone().get(), var->clone().get(),
	(gamma != nullptr) ? gamma->clone().get() : nullptr, (beta != nullptr) ? beta->clone().get() : nullptr)
	.first);

	return Status{};
	}

	void NEBatchNormalizationLayerKernel::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);

	(this->*_func)(window);
	}
	} // namespace arm_compute