src/core/CL/kernels/CLPoolingLayerKernel.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/CL/kernels/CLPoolingLayerKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/CLValidate.h"
 #include "arm_compute/core/CL/ICLKernel.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/CL/OpenCL.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Window.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"

 #include <set>
 #include <string>
 #include <tuple>

 namespace arm_compute
 {
 using namespace arm_compute::misc::shape_calculator;

 namespace
 {
 // Internal window config info
 using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size

 void auto_init(const ITensorInfo *input, ITensorInfo *output, PoolingLayerInfo pool_info)
 {
     TensorShape out_shape = compute_pool_shape(*input, pool_info);
     auto_init_if_empty(*output, input->clone()->set_tensor_shape(out_shape));
 }

 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type == PoolingType::L2),
                                     "Unsupported combination of parameters!");

     // Checks performed when output is configured
     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
         TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type()));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
     }

     return Status{};
 }

 std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

     // Get data layout
     const DataLayout data_layout = input->data_layout();
     const int        idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const int        idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

     int                 pool_stride_x   = 0;
     int                 pool_stride_y   = 0;
     unsigned int        pooled_w        = 0;
     unsigned int        pooled_h        = 0;
     int                 pool_size_x     = pool_info.is_global_pooling ? input->dimension(idx_width) : pool_info.pool_size.width;
     int                 pool_size_y     = pool_info.is_global_pooling ? input->dimension(idx_height) : pool_info.pool_size.height;
     const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
     std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
     const int  pool_pad_right  = pad_stride_info.pad_right();
     const int  pool_pad_top    = pad_stride_info.pad_top();
     const int  pool_pad_left   = pad_stride_info.pad_left();
     const int  pool_pad_bottom = pad_stride_info.pad_bottom();
     BorderSize border_size     = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);

     auto_init(input, output, pool_info);
     pooled_w = output->tensor_shape()[idx_width];
     pooled_h = output->tensor_shape()[idx_height];

     const DataType data_type = input->data_type();

     const int input_width  = input->dimension(idx_width);
     const int input_height = input->dimension(idx_height);

     unsigned int num_elems_processed_per_iteration = 0;
     bool         window_changed                    = false;
     Window       win{};
     switch(data_layout)
     {
         case DataLayout::NCHW:
         {
             // Change the number of elements processed per iteration
             // for pooling 3x3 with stride less equal than 3
             const bool can_optimize                         = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_quantized(data_type);
             num_elems_processed_per_iteration               = can_optimize ? 4 : 1;
             const unsigned int num_elems_read_per_iteration = (num_elems_processed_per_iteration - 1) * pool_stride_x + pool_size_x;

             // Number of iterations in X dimension
             const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration;

             // Upper limit for the number of right/bottom border elements that are accessed
             const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - input_width;
             const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_height;

             border_size.right  = std::max(upper_bound_w, pool_pad_right);
             border_size.bottom = std::max(upper_bound_h, pool_pad_bottom);

             win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

             AccessWindowRectangle input_access(input, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y,
                                                pool_stride_x, pool_stride_y);
             AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
             window_changed = update_window_and_padding(win, input_access, output_access);
             output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
             break;
         }
         case DataLayout::NHWC:
         {
             num_elems_processed_per_iteration = 8;
             win                               = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

             AccessWindowStatic input_access(input,
                                             0, -1,
                                             ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration), input->dimension(1));
             AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
             window_changed = update_window_and_padding(win, input_access, output_access);
             output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
             break;
         }
         default:
             ARM_COMPUTE_ERROR("Not implemented");
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_tuple(err, win, CLPoolingConfig(num_elems_processed_per_iteration, border_size));
 }
 } // namespace

 CLPoolingLayerKernel::CLPoolingLayerKernel()
     : _input(nullptr), _output(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _border_size(0), _num_elems_processed_per_iteration(1)
 {
 }

 BorderSize CLPoolingLayerKernel::border_size() const
 {
     return _border_size;
 }

 void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

     // Set instance variables
     _input       = input;
     _output      = output;
     _pool_info   = pool_info;
     _data_layout = input->info()->data_layout();

     int                 pool_stride_x   = 0;
     int                 pool_stride_y   = 0;
     const PoolingType   pool_type       = pool_info.pool_type;
     const int           idx_width       = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
     const int           idx_height      = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
     const int           idx_channel     = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
     const int           pool_size_x     = pool_info.is_global_pooling ? input->info()->dimension(idx_width) : pool_info.pool_size.width;
     const int           pool_size_y     = pool_info.is_global_pooling ? input->info()->dimension(idx_height) : pool_info.pool_size.height;
     const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
     const bool          exclude_padding = pool_info.exclude_padding;
     std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
     const int pool_pad_top  = pad_stride_info.pad_top();
     const int pool_pad_left = pad_stride_info.pad_left();

     // Set build options
     CLBuildOptions build_opts;

     if(is_data_type_quantized_asymmetric(input->info()->data_type()) && input->info()->quantization_info() != output->info()->quantization_info())
     {
         const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
         const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();

         build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset));
         build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset));
         build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq_info.scale));
         build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
     }

     // Check output dimensions
     auto_init(input->info(), output->info(), pool_info);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info));

     const DataType data_type = input->info()->data_type();

     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
     build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
     build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x));
     build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y));
     build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left));
     build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_top));
     build_opts.add_option("-DPOOL_SIZE_X=" + support::cpp11::to_string(pool_size_x));
     build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y));

     // Set the initial value for the pooling operation accordingly with the data type
     if(pool_type == PoolingType::MAX)
     {
         if(is_data_type_quantized(data_type))
         {
             PixelValue type_min{};
             std::tie(type_min, std::ignore) = get_min_max(data_type);
             build_opts.add_option("-DINITIAL_VALUE=" + support::cpp11::to_string(type_min.get<int32_t>()));
         }
         else
         {
             build_opts.add_option("-DINITIAL_VALUE=" + float_to_string_with_full_precision(std::numeric_limits<float>::lowest()));
         }
     }
     else
     {
         // Pool AVG and Pool L2 initial value
         build_opts.add_option("-DINITIAL_VALUE=0");
     }

     const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision;
     const auto use_wider_accumulator  = use_fp_mixed_precision && (pool_type != PoolingType::MAX);
     const auto acc_data_type          = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : data_type);
     build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
     build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");

     // Create kernel
     switch(_data_layout)
     {
         case DataLayout::NCHW:
         {
             build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
             build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
             if(pool_type != PoolingType::MAX)
             {
                 build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
             }

             if((pool_size_x == 3) && (pool_size_y == 3) && !is_data_type_quantized_asymmetric(data_type))
             {
                 // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
                 // each thread computes 4 output elements
                 const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3);

                 std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_")
                                           + support::cpp11::to_string(pool_size_x);
                 _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
             }
             else // Run general case
             {
                 std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nchw" : "pooling_layer_MxN_nchw";
                 _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
             }
             break;
         }
         case DataLayout::NHWC:
         {
             build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
             build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
             build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
             build_opts.add_option_if(output->info()->tensor_shape().total_size_upper(3) > 1,
                                      "-DDST_DEPTH=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
             std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
             _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
             break;
         }
         default:
             ARM_COMPUTE_ERROR("Not implemented");
     }

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info);

     ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
     ICLKernel::configure_internal(std::get<1>(win_config));

     if(_data_layout == DataLayout::NCHW)
     {
         CLPoolingConfig pooling_config     = std::get<2>(win_config);
         _num_elems_processed_per_iteration = pooling_config.first;
         _border_size                       = pooling_config.second;
     }
     else
     {
         _border_size                       = BorderSize(1, 0, 0, 0);
         _num_elems_processed_per_iteration = 8;
     }

     // Set config_id for enabling LWS tuning
     _config_id = "pooling_layer_";
     _config_id += lower_string(string_from_data_type(data_type));
     _config_id += "_";
     _config_id += lower_string(string_from_data_layout(_data_layout));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(idx_width));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(idx_height));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(idx_channel));
     _config_id += "_";
     _config_id += lower_string(string_from_data_layout(input->info()->data_layout()));
 }

 Status CLPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info));
     ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info)));

     return Status{};
 }

 void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     unsigned int pool_stride_x = 0;
     unsigned int pool_stride_y = 0;
     std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();

     // Collapse window
     Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);

     switch(_data_layout)
     {
         case DataLayout::NCHW:
         {
             Window slice = window_collapsed.first_slice_window_3D();
             do
             {
                 // Upsample input by pool size
                 Window in_slice(slice);
                 in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - _pool_info.pad_stride_info.pad_left(),
                                                              (in_slice.x().end() - _pool_info.pad_stride_info.pad_left()) * pool_stride_x,
                                                              pool_stride_x * _num_elems_processed_per_iteration));
                 in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - _pool_info.pad_stride_info.pad_top(),
                                                              (in_slice.y().end() - _pool_info.pad_stride_info.pad_top()) * pool_stride_y,
                                                              pool_stride_y));

                 // Set inputs
                 unsigned int idx = 0;
                 add_3D_tensor_argument(idx, _input, in_slice);
                 add_3D_tensor_argument(idx, _output, slice);
                 enqueue(queue, *this, slice, lws_hint());
             }
             while(window_collapsed.slide_window_slice_3D(slice));
             break;
         }
         case DataLayout::NHWC:
         {
             const size_t total_batches = _output->info()->tensor_shape().total_size_upper(3);

             Window slice    = window_collapsed.first_slice_window_4D();
             Window in_slice = window_collapsed.first_slice_window_4D();
             in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration));
             in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x));
             in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y));
             in_slice.set(3, Window::Dimension(0, total_batches, 1));
             do
             {
                 // Set inputs
                 unsigned int idx = 0;
                 add_4D_tensor_argument(idx, _input, in_slice);
                 add_4D_tensor_argument(idx, _output, slice);
                 enqueue(queue, *this, slice, lws_hint());
             }
             while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice));
             break;
         }
         default:
             ARM_COMPUTE_ERROR("Not implemented");
     }
 }
 } // 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/CL/kernels/CLPoolingLayerKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/CLValidate.h"
	#include "arm_compute/core/CL/ICLKernel.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/CL/OpenCL.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Window.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"

	#include <set>
	#include <string>
	#include <tuple>

	namespace arm_compute
	{
	using namespace arm_compute::misc::shape_calculator;

	namespace
	{
	// Internal window config info
	using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size

	void auto_init(const ITensorInfo input, ITensorInfo output, PoolingLayerInfo pool_info)
	{
	TensorShape out_shape = compute_pool_shape(*input, pool_info);
	auto_init_if_empty(*output, input->clone()->set_tensor_shape(out_shape));
	}

	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, const PoolingLayerInfo &pool_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type == PoolingType::L2),
	"Unsupported combination of parameters!");

	// Checks performed when output is configured
	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
	TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type()));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
	}

	return Status{};
	}

	std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITensorInfo input, ITensorInfo output, const PoolingLayerInfo &pool_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

	// Get data layout
	const DataLayout data_layout = input->data_layout();
	const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

	int pool_stride_x = 0;
	int pool_stride_y = 0;
	unsigned int pooled_w = 0;
	unsigned int pooled_h = 0;
	int pool_size_x = pool_info.is_global_pooling ? input->dimension(idx_width) : pool_info.pool_size.width;
	int pool_size_y = pool_info.is_global_pooling ? input->dimension(idx_height) : pool_info.pool_size.height;
	const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
	std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
	const int pool_pad_right = pad_stride_info.pad_right();
	const int pool_pad_top = pad_stride_info.pad_top();
	const int pool_pad_left = pad_stride_info.pad_left();
	const int pool_pad_bottom = pad_stride_info.pad_bottom();
	BorderSize border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);

	auto_init(input, output, pool_info);
	pooled_w = output->tensor_shape()[idx_width];
	pooled_h = output->tensor_shape()[idx_height];

	const DataType data_type = input->data_type();

	const int input_width = input->dimension(idx_width);
	const int input_height = input->dimension(idx_height);

	unsigned int num_elems_processed_per_iteration = 0;
	bool window_changed = false;
	Window win{};
	switch(data_layout)
	{
	case DataLayout::NCHW:
	{
	// Change the number of elements processed per iteration
	// for pooling 3x3 with stride less equal than 3
	const bool can_optimize = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_quantized(data_type);
	num_elems_processed_per_iteration = can_optimize ? 4 : 1;
	const unsigned int num_elems_read_per_iteration = (num_elems_processed_per_iteration - 1) * pool_stride_x + pool_size_x;

	// Number of iterations in X dimension
	const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration;

	// Upper limit for the number of right/bottom border elements that are accessed
	const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - input_width;
	const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_height;

	border_size.right = std::max(upper_bound_w, pool_pad_right);
	border_size.bottom = std::max(upper_bound_h, pool_pad_bottom);

	win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

	AccessWindowRectangle input_access(input, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y,
	pool_stride_x, pool_stride_y);
	AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
	window_changed = update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	break;
	}
	case DataLayout::NHWC:
	{
	num_elems_processed_per_iteration = 8;
	win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));

	AccessWindowStatic input_access(input,
	0, -1,
	ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration), input->dimension(1));
	AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
	window_changed = update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Not implemented");
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_tuple(err, win, CLPoolingConfig(num_elems_processed_per_iteration, border_size));
	}
	} // namespace

	CLPoolingLayerKernel::CLPoolingLayerKernel()
	: _input(nullptr), _output(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _border_size(0), _num_elems_processed_per_iteration(1)
	{
	}

	BorderSize CLPoolingLayerKernel::border_size() const
	{
	return _border_size;
	}

	void CLPoolingLayerKernel::configure(const ICLTensor input, ICLTensor output, const PoolingLayerInfo &pool_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

	// Set instance variables
	_input = input;
	_output = output;
	_pool_info = pool_info;
	_data_layout = input->info()->data_layout();

	int pool_stride_x = 0;
	int pool_stride_y = 0;
	const PoolingType pool_type = pool_info.pool_type;
	const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
	const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
	const int pool_size_x = pool_info.is_global_pooling ? input->info()->dimension(idx_width) : pool_info.pool_size.width;
	const int pool_size_y = pool_info.is_global_pooling ? input->info()->dimension(idx_height) : pool_info.pool_size.height;
	const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
	const bool exclude_padding = pool_info.exclude_padding;
	std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
	const int pool_pad_top = pad_stride_info.pad_top();
	const int pool_pad_left = pad_stride_info.pad_left();

	// Set build options
	CLBuildOptions build_opts;

	if(is_data_type_quantized_asymmetric(input->info()->data_type()) && input->info()->quantization_info() != output->info()->quantization_info())
	{
	const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
	const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();

	build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset));
	build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset));
	build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq_info.scale));
	build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
	}

	// Check output dimensions
	auto_init(input->info(), output->info(), pool_info);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info));

	const DataType data_type = input->info()->data_type();

	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
	build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
	build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x));
	build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y));
	build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left));
	build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_top));
	build_opts.add_option("-DPOOL_SIZE_X=" + support::cpp11::to_string(pool_size_x));
	build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y));

	// Set the initial value for the pooling operation accordingly with the data type
	if(pool_type == PoolingType::MAX)
	{
	if(is_data_type_quantized(data_type))
	{
	PixelValue type_min{};
	std::tie(type_min, std::ignore) = get_min_max(data_type);
	build_opts.add_option("-DINITIAL_VALUE=" + support::cpp11::to_string(type_min.get<int32_t>()));
	}
	else
	{
	build_opts.add_option("-DINITIAL_VALUE=" + float_to_string_with_full_precision(std::numeric_limits<float>::lowest()));
	}
	}
	else
	{
	// Pool AVG and Pool L2 initial value
	build_opts.add_option("-DINITIAL_VALUE=0");
	}

	const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision;
	const auto use_wider_accumulator = use_fp_mixed_precision && (pool_type != PoolingType::MAX);
	const auto acc_data_type = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : data_type);
	build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
	build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");

	// Create kernel
	switch(_data_layout)
	{
	case DataLayout::NCHW:
	{
	build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
	build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
	if(pool_type != PoolingType::MAX)
	{
	build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
	}

	if((pool_size_x == 3) && (pool_size_y == 3) && !is_data_type_quantized_asymmetric(data_type))
	{
	// Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
	// each thread computes 4 output elements
	const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3);

	std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_")
	+ support::cpp11::to_string(pool_size_x);
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
	}
	else // Run general case
	{
	std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nchw" : "pooling_layer_MxN_nchw";
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
	}
	break;
	}
	case DataLayout::NHWC:
	{
	build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
	build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
	build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
	build_opts.add_option_if(output->info()->tensor_shape().total_size_upper(3) > 1,
	"-DDST_DEPTH=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
	std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Not implemented");
	}

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info);

	ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
	ICLKernel::configure_internal(std::get<1>(win_config));

	if(_data_layout == DataLayout::NCHW)
	{
	CLPoolingConfig pooling_config = std::get<2>(win_config);
	_num_elems_processed_per_iteration = pooling_config.first;
	_border_size = pooling_config.second;
	}
	else
	{
	_border_size = BorderSize(1, 0, 0, 0);
	_num_elems_processed_per_iteration = 8;
	}

	// Set config_id for enabling LWS tuning
	_config_id = "pooling_layer_";
	_config_id += lower_string(string_from_data_type(data_type));
	_config_id += "_";
	_config_id += lower_string(string_from_data_layout(_data_layout));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(idx_width));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(idx_height));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(idx_channel));
	_config_id += "_";
	_config_id += lower_string(string_from_data_layout(input->info()->data_layout()));
	}

	Status CLPoolingLayerKernel::validate(const ITensorInfo input, const ITensorInfo output, const PoolingLayerInfo &pool_info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info));
	ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info)));

	return Status{};
	}

	void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	unsigned int pool_stride_x = 0;
	unsigned int pool_stride_y = 0;
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();

	// Collapse window
	Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);

	switch(_data_layout)
	{
	case DataLayout::NCHW:
	{
	Window slice = window_collapsed.first_slice_window_3D();
	do
	{
	// Upsample input by pool size
	Window in_slice(slice);
	in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - _pool_info.pad_stride_info.pad_left(),
	(in_slice.x().end() - _pool_info.pad_stride_info.pad_left()) * pool_stride_x,
	pool_stride_x * _num_elems_processed_per_iteration));
	in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - _pool_info.pad_stride_info.pad_top(),
	(in_slice.y().end() - _pool_info.pad_stride_info.pad_top()) * pool_stride_y,
	pool_stride_y));

	// Set inputs
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, in_slice);
	add_3D_tensor_argument(idx, _output, slice);
	enqueue(queue, *this, slice, lws_hint());
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	break;
	}
	case DataLayout::NHWC:
	{
	const size_t total_batches = _output->info()->tensor_shape().total_size_upper(3);

	Window slice = window_collapsed.first_slice_window_4D();
	Window in_slice = window_collapsed.first_slice_window_4D();
	in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration));
	in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x));
	in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y));
	in_slice.set(3, Window::Dimension(0, total_batches, 1));
	do
	{
	// Set inputs
	unsigned int idx = 0;
	add_4D_tensor_argument(idx, _input, in_slice);
	add_4D_tensor_argument(idx, _output, slice);
	enqueue(queue, *this, slice, lws_hint());
	}
	while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice));
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Not implemented");
	}
	}
	} // namespace arm_compute