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

 /*
  * Copyright (c) 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/NEInstanceNormalizationLayerKernel.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/NEMath.h"
 #include "arm_compute/core/NEON/wrapper/wrapper.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 <arm_neon.h>

 namespace arm_compute
 {
 namespace
 {
 template <typename T>
 void instance_normalization_nchw(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window)
 {
     /** NEON vector tag type. */
     using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

     // Clear X/Y dimensions on execution window as we handle the planes manually
     Window win = window;
     win.set(Window::DimX, Window::Dimension(0, 1, 1));
     win.set(Window::DimY, Window::Dimension(0, 1, 1));

     constexpr int      window_step_x  = 16 / sizeof(T);
     const unsigned int elements_plane = input->info()->dimension(0) * output->info()->dimension(1);

     Iterator input_it(input, win);
     execute_window_loop(win, [&](const Coordinates & id)
     {
         Window win_plane = window;
         win_plane.set(Window::DimX, Window::Dimension(0, 1, 1));
         win_plane.set(Window::DimZ, Window::Dimension(id[2], id[2] + 1, 1));
         win_plane.set(3, Window::Dimension(id[3], id[3] + 1, 1));

         Iterator input_plane_it(input, win_plane);
         Iterator output_plane_it(output, win_plane);

         auto sum_h_w         = static_cast<T>(0.f);
         auto sum_squares_h_w = static_cast<T>(0.f);

         execute_window_loop(win_plane, [&](const Coordinates &)
         {
             const auto input_ptr = reinterpret_cast<const T *>(input_plane_it.ptr());

             auto vec_sum_h_w         = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
             auto vec_sum_squares_h_w = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});

             // Compute S elements per iteration
             int x = window.x().start();
             for(; x <= (window.x().end() - window_step_x); x += window_step_x)
             {
                 auto vec_input_val  = wrapper::vloadq(input_ptr + x);
                 vec_sum_h_w         = wrapper::vadd(vec_sum_h_w, vec_input_val);
                 vec_sum_squares_h_w = wrapper::vadd(vec_sum_squares_h_w, wrapper::vmul(vec_input_val, vec_input_val));
             }

             auto vec2_sum_h_w         = wrapper::vpadd(wrapper::vgethigh(vec_sum_h_w), wrapper::vgetlow(vec_sum_h_w));
             auto vec2_sum_squares_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_squares_h_w), wrapper::vgetlow(vec_sum_squares_h_w));
             for(int i = 0; i < window_step_x / 4; ++i)
             {
                 vec2_sum_h_w         = wrapper::vpadd(vec2_sum_h_w, vec2_sum_h_w);
                 vec2_sum_squares_h_w = wrapper::vpadd(vec2_sum_squares_h_w, vec2_sum_squares_h_w);
             }
             sum_h_w += wrapper::vgetlane(vec2_sum_h_w, 0);
             sum_squares_h_w += wrapper::vgetlane(vec2_sum_squares_h_w, 0);

             // Compute left-over elements
             for(; x < window.x().end(); ++x)
             {
                 const auto value = *(input_ptr + x);
                 sum_h_w += value;
                 sum_squares_h_w += value * value;
             }
         },
         input_plane_it, output_plane_it);

         const auto mean_h_w = sum_h_w / elements_plane;
         const auto var_h_w  = sum_squares_h_w / elements_plane - mean_h_w * mean_h_w;

         const auto multip_h_w     = gamma / std::sqrt(var_h_w + epsilon);
         const auto vec_mean_h_w   = wrapper::vdup_n(static_cast<T>(mean_h_w), ExactTagType{});
         const auto vec_multip_h_w = wrapper::vdup_n(static_cast<T>(multip_h_w), ExactTagType{});
         const auto vec_beta       = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{});

         execute_window_loop(win_plane, [&](const Coordinates &)
         {
             auto input_ptr  = reinterpret_cast<T *>(input_plane_it.ptr());
             auto output_ptr = reinterpret_cast<T *>(output_plane_it.ptr());

             // Compute S elements per iteration
             int  x       = window.x().start();
             auto vec_val = wrapper::vdup_n(static_cast<T>(0.0f), ExactTagType{});
             for(; x <= (window.x().end() - window_step_x); x += window_step_x)
             {
                 vec_val = wrapper::vloadq(input_ptr + x);
                 vec_val = wrapper::vadd(wrapper::vmul(wrapper::vsub(vec_val, vec_mean_h_w), vec_multip_h_w), vec_beta);
                 wrapper::vstore(output_ptr + x, vec_val);
             }

             // Compute left-over elements
             for(; x < window.x().end(); ++x)
             {
                 *(output_ptr + x) = ((*(input_ptr + x)) - mean_h_w) * multip_h_w + beta;
             }
         },
         input_plane_it, output_plane_it);
     },
     input_it);
 }

 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
     ARM_COMPUTE_UNUSED(gamma);
     ARM_COMPUTE_UNUSED(beta);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(epsilon == 0.f, "Epsilon must be different than 0");

     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC, "NHWC data layout is not supported by the kernel directly");

     if(output != nullptr && output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
     }

     return Status{};
 }

 std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
 {
     // We handle the planes manually
     Window win = calculate_max_window(*input, Steps(1));

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());

     // NEInstanceNormalizationLayerKernel 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

 NEInstanceNormalizationLayerKernel::NEInstanceNormalizationLayerKernel()
     : _func(nullptr), _input(nullptr), _output(nullptr), _gamma(1), _beta(0), _epsilon(1e-12)
 {
 }

 void NEInstanceNormalizationLayerKernel::configure(ITensor *input, ITensor *output, float gamma, float beta, float epsilon)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input);

     _input   = input;
     _output  = output == nullptr ? input : output;
     _gamma   = gamma;
     _beta    = beta;
     _epsilon = epsilon;

     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), gamma, beta, epsilon));

     if(_input->info()->data_type() == DataType::F32)
     {
         _func = &instance_normalization_nchw<float>;
     }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
     else if(_input->info()->data_type() == DataType::F16)
     {
         _func = &instance_normalization_nchw<float16_t>;
     }
 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
     else
     {
         ARM_COMPUTE_ERROR("Unsupported data type");
     }

     // Configure kernel window
     auto win_config = validate_and_configure_window(_input->info(), _output->info());
     ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

     INEKernel::configure(std::get<1>(win_config));
 }

 Status NEInstanceNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, gamma, beta, epsilon));
     ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (output == nullptr ? input->clone().get() : output->clone().get()))));
     return Status{};
 }

 void NEInstanceNormalizationLayerKernel::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);
     (*_func)(_input, _output, _gamma, _beta, _epsilon, window);
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 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/NEInstanceNormalizationLayerKernel.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/NEMath.h"
	#include "arm_compute/core/NEON/wrapper/wrapper.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 <arm_neon.h>

	namespace arm_compute
	{
	namespace
	{
	template <typename T>
	void instance_normalization_nchw(ITensor input, ITensor output, float gamma, float beta, float epsilon, const Window &window)
	{
	/** NEON vector tag type. */
	using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

	// Clear X/Y dimensions on execution window as we handle the planes manually
	Window win = window;
	win.set(Window::DimX, Window::Dimension(0, 1, 1));
	win.set(Window::DimY, Window::Dimension(0, 1, 1));

	constexpr int window_step_x = 16 / sizeof(T);
	const unsigned int elements_plane = input->info()->dimension(0) * output->info()->dimension(1);

	Iterator input_it(input, win);
	execute_window_loop(win, [&](const Coordinates & id)
	{
	Window win_plane = window;
	win_plane.set(Window::DimX, Window::Dimension(0, 1, 1));
	win_plane.set(Window::DimZ, Window::Dimension(id[2], id[2] + 1, 1));
	win_plane.set(3, Window::Dimension(id[3], id[3] + 1, 1));

	Iterator input_plane_it(input, win_plane);
	Iterator output_plane_it(output, win_plane);

	auto sum_h_w = static_cast<T>(0.f);
	auto sum_squares_h_w = static_cast<T>(0.f);

	execute_window_loop(win_plane, [&](const Coordinates &)
	{
	const auto input_ptr = reinterpret_cast<const T *>(input_plane_it.ptr());

	auto vec_sum_h_w = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
	auto vec_sum_squares_h_w = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});

	// Compute S elements per iteration
	int x = window.x().start();
	for(; x <= (window.x().end() - window_step_x); x += window_step_x)
	{
	auto vec_input_val = wrapper::vloadq(input_ptr + x);
	vec_sum_h_w = wrapper::vadd(vec_sum_h_w, vec_input_val);
	vec_sum_squares_h_w = wrapper::vadd(vec_sum_squares_h_w, wrapper::vmul(vec_input_val, vec_input_val));
	}

	auto vec2_sum_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_h_w), wrapper::vgetlow(vec_sum_h_w));
	auto vec2_sum_squares_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_squares_h_w), wrapper::vgetlow(vec_sum_squares_h_w));
	for(int i = 0; i < window_step_x / 4; ++i)
	{
	vec2_sum_h_w = wrapper::vpadd(vec2_sum_h_w, vec2_sum_h_w);
	vec2_sum_squares_h_w = wrapper::vpadd(vec2_sum_squares_h_w, vec2_sum_squares_h_w);
	}
	sum_h_w += wrapper::vgetlane(vec2_sum_h_w, 0);
	sum_squares_h_w += wrapper::vgetlane(vec2_sum_squares_h_w, 0);

	// Compute left-over elements
	for(; x < window.x().end(); ++x)
	{
	const auto value = *(input_ptr + x);
	sum_h_w += value;
	sum_squares_h_w += value * value;
	}
	},
	input_plane_it, output_plane_it);

	const auto mean_h_w = sum_h_w / elements_plane;
	const auto var_h_w = sum_squares_h_w / elements_plane - mean_h_w * mean_h_w;

	const auto multip_h_w = gamma / std::sqrt(var_h_w + epsilon);
	const auto vec_mean_h_w = wrapper::vdup_n(static_cast<T>(mean_h_w), ExactTagType{});
	const auto vec_multip_h_w = wrapper::vdup_n(static_cast<T>(multip_h_w), ExactTagType{});
	const auto vec_beta = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{});

	execute_window_loop(win_plane, [&](const Coordinates &)
	{
	auto input_ptr = reinterpret_cast<T *>(input_plane_it.ptr());
	auto output_ptr = reinterpret_cast<T *>(output_plane_it.ptr());

	// Compute S elements per iteration
	int x = window.x().start();
	auto vec_val = wrapper::vdup_n(static_cast<T>(0.0f), ExactTagType{});
	for(; x <= (window.x().end() - window_step_x); x += window_step_x)
	{
	vec_val = wrapper::vloadq(input_ptr + x);
	vec_val = wrapper::vadd(wrapper::vmul(wrapper::vsub(vec_val, vec_mean_h_w), vec_multip_h_w), vec_beta);
	wrapper::vstore(output_ptr + x, vec_val);
	}

	// Compute left-over elements
	for(; x < window.x().end(); ++x)
	{
	(output_ptr + x) = (((input_ptr + x)) - mean_h_w) * multip_h_w + beta;
	}
	},
	input_plane_it, output_plane_it);
	},
	input_it);
	}

	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, float gamma, float beta, float epsilon)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
	ARM_COMPUTE_UNUSED(gamma);
	ARM_COMPUTE_UNUSED(beta);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(epsilon == 0.f, "Epsilon must be different than 0");

	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC, "NHWC data layout is not supported by the kernel directly");

	if(output != nullptr && output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
	}

	return Status{};
	}

	std::tuple<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo output)
	{
	// We handle the planes manually
	Window win = calculate_max_window(*input, Steps(1));

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());

	// NEInstanceNormalizationLayerKernel 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

	NEInstanceNormalizationLayerKernel::NEInstanceNormalizationLayerKernel()
	: _func(nullptr), _input(nullptr), _output(nullptr), _gamma(1), _beta(0), _epsilon(1e-12)
	{
	}

	void NEInstanceNormalizationLayerKernel::configure(ITensor input, ITensor output, float gamma, float beta, float epsilon)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input);

	_input = input;
	_output = output == nullptr ? input : output;
	_gamma = gamma;
	_beta = beta;
	_epsilon = epsilon;

	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), gamma, beta, epsilon));

	if(_input->info()->data_type() == DataType::F32)
	{
	_func = &instance_normalization_nchw<float>;
	}
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	else if(_input->info()->data_type() == DataType::F16)
	{
	_func = &instance_normalization_nchw<float16_t>;
	}
	#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	else
	{
	ARM_COMPUTE_ERROR("Unsupported data type");
	}

	// Configure kernel window
	auto win_config = validate_and_configure_window(_input->info(), _output->info());
	ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

	INEKernel::configure(std::get<1>(win_config));
	}

	Status NEInstanceNormalizationLayerKernel::validate(const ITensorInfo input, const ITensorInfo output, float gamma, float beta, float epsilon)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, gamma, beta, epsilon));
	ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (output == nullptr ? input->clone().get() : output->clone().get()))));
	return Status{};
	}

	void NEInstanceNormalizationLayerKernel::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);
	(*_func)(_input, _output, _gamma, _beta, _epsilon, window);
	}
	} // namespace arm_compute