src/gpu/cl/kernels/ClPool2dKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2017-2021 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/gpu/cl/kernels/ClPool2dKernel.h"

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/Cast.h"

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

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

     const auto   data_layout       = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.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);
     const bool   is_global_pooling = pool_info.is_global_pooling;
     unsigned int pool_size_x       = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
     unsigned int pool_size_y       = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
     int          output_width      = 0;
     int          output_height     = 0;

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported");

     std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height],
                                                                      pool_size_x, pool_size_y, pool_info.pad_stride_info);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), "Calculated output dimension size is invalid");

     // Check indices
     if(indices)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");

         if(indices->total_size() != 0)
         {
             TensorInfo idx_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, DataType::U32));
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
         }
     }

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

     return Status{};
 }
 } // namespace

 ClPool2dKernel::ClPool2dKernel()
 {
     _type = CLKernelType::POOL;
 }

 void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices));

     auto padding_info = get_padding_info({ src, dst, indices });

     // Auto init if empty
     TensorShape out_shape = compute_pool_shape(*src, pool_info);
     auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));
     if(indices)
     {
         auto_init_if_empty(*indices, src->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
     }

     // Set instance variables
     _pool_info                         = pool_info;
     _data_layout                       = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
     _num_elems_processed_per_iteration = (_data_layout == DataLayout::NCHW) ? 1 : ((dst->data_type() == DataType::F32) ? 2 : 4);
     _num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(0));

     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           idx_batch_size  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
     const int           pool_size_x     = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
     const int           pool_size_y     = pool_info.is_global_pooling ? src->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();
     const DataType data_type     = src->data_type();

     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration));
     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));
     build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
     build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
     build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
     build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));

     // Tensor paddings are used to calculate the indicies for MAX pooling
     if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
     {
         build_opts.add_option("-DSRC_BATCH=" + support::cpp11::to_string(src->tensor_shape().total_size_lower(3)));
     }

     if(is_data_type_quantized_asymmetric(data_type))
     {
         build_opts.add_option("-DQUANTIZED");

         if(src->quantization_info() != dst->quantization_info())
         {
             const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
             const UniformQuantizationInfo oq_info = dst->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));
         }
     }

     // 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");
     }

     // Create kernel
     switch(_data_layout)
     {
         case DataLayout::NCHW:
         {
             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 : (is_data_type_quantized(data_type) ? DataType::S32 : data_type));
             build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
             build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");

             if(pool_type != PoolingType::MAX)
             {
                 build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
             }

             if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
             {
                 // For max pooling with pool2x2, store indicies which will be used in max unpooling
                 std::string kernel_name = "pooling_layer_2_nchw_indices";
                 _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
             }
             else // Run general case
             {
                 std::string kernel_name = "pooling_layer_MxN_nchw";
                 _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
             }
             break;
         }
         case DataLayout::NHWC:
         {
             // Floating point mixed precision is support on F16 only
             const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;

             // Wider accumulation is required to avoid accuracy loss
             // Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation)
             // Cast 2: Quantized (int8/uint8 src data and int32 accumulation )
             DataType acc_data_type = data_type;

             if(use_fp_mixed_precision)
             {
                 acc_data_type = DataType::F32;
             }
             else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX)
             {
                 acc_data_type = DataType::S32;
             }

             build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type));
             build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION");
             build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
             build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
             build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
             build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
             build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel)));
             build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size)));
             build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration));
             if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
             {
                 build_opts.add_option_if(indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");

                 std::string kernel_name = "pooling_layer_2x2_nhwc";
                 _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
             }
             else
             {
                 std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
                 _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
             }
             break;
         }
         default:
             ARM_COMPUTE_ERROR("Not implemented");
     }

     // Configure kernel window
     Window win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration));
     ICLKernel::configure_internal(win);

     // 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(dst->dimension(idx_width));
     _config_id += "_";
     _config_id += support::cpp11::to_string(dst->dimension(idx_height));
     _config_id += "_";
     _config_id += support::cpp11::to_string(dst->dimension(idx_channel));
     _config_id += "_";
     _config_id += lower_string(string_from_data_layout(src->data_layout()));

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 Status ClPool2dKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices));
     return Status{};
 }

 void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::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();

     const auto src     = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
     auto       dst     = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
     auto       indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1));

     // 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
             {
                 // Set srcs
                 unsigned int idx = 0;
                 add_3D_tensor_argument(idx, src, slice);
                 add_3D_tensor_argument(idx, dst, slice);
                 if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
                 {
                     add_3D_tensor_argument(idx, indices, slice);
                 }
                 enqueue(queue, *this, slice, lws_hint());
             }
             while(window_collapsed.slide_window_slice_3D(slice));
             break;
         }
         case DataLayout::NHWC:
         {
             const size_t batch_size = dst->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, src->info()->dimension(0), _num_elems_processed_per_iteration));
             in_slice.set(Window::DimY, Window::Dimension(0, src->info()->dimension(1), pool_stride_x));
             in_slice.set(Window::DimZ, Window::Dimension(0, src->info()->dimension(2), pool_stride_y));
             in_slice.set(3, Window::Dimension(0, batch_size, 1));
             do
             {
                 // Set srcs
                 unsigned int idx = 0;
                 add_4D_tensor_argument(idx, src, in_slice);
                 add_4D_tensor_argument(idx, dst, slice);
                 if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
                 {
                     add_4D_tensor_argument(idx, indices, 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 kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2021 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/gpu/cl/kernels/ClPool2dKernel.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/Cast.h"

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

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

	const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.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);
	const bool is_global_pooling = pool_info.is_global_pooling;
	unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
	unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
	int output_width = 0;
	int output_height = 0;

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported");

	std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height],
	pool_size_x, pool_size_y, pool_info.pad_stride_info);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 \|\| output_height < 1), "Calculated output dimension size is invalid");

	// Check indices
	if(indices)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");

	if(indices->total_size() != 0)
	{
	TensorInfo idx_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, DataType::U32));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
	}
	}

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

	return Status{};
	}
	} // namespace

	ClPool2dKernel::ClPool2dKernel()
	{
	_type = CLKernelType::POOL;
	}

	void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorInfo src, ITensorInfo dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices));

	auto padding_info = get_padding_info({ src, dst, indices });

	// Auto init if empty
	TensorShape out_shape = compute_pool_shape(*src, pool_info);
	auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));
	if(indices)
	{
	auto_init_if_empty(*indices, src->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
	}

	// Set instance variables
	_pool_info = pool_info;
	_data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
	_num_elems_processed_per_iteration = (_data_layout == DataLayout::NCHW) ? 1 : ((dst->data_type() == DataType::F32) ? 2 : 4);
	_num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(0));

	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 idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
	const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
	const int pool_size_y = pool_info.is_global_pooling ? src->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();
	const DataType data_type = src->data_type();

	// Set build options
	CLBuildOptions build_opts;
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration));
	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));
	build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
	build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
	build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
	build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));

	// Tensor paddings are used to calculate the indicies for MAX pooling
	if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
	{
	build_opts.add_option("-DSRC_BATCH=" + support::cpp11::to_string(src->tensor_shape().total_size_lower(3)));
	}

	if(is_data_type_quantized_asymmetric(data_type))
	{
	build_opts.add_option("-DQUANTIZED");

	if(src->quantization_info() != dst->quantization_info())
	{
	const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
	const UniformQuantizationInfo oq_info = dst->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));
	}
	}

	// 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");
	}

	// Create kernel
	switch(_data_layout)
	{
	case DataLayout::NCHW:
	{
	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 : (is_data_type_quantized(data_type) ? DataType::S32 : data_type));
	build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
	build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");

	if(pool_type != PoolingType::MAX)
	{
	build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
	}

	if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type))
	{
	// For max pooling with pool2x2, store indicies which will be used in max unpooling
	std::string kernel_name = "pooling_layer_2_nchw_indices";
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
	}
	else // Run general case
	{
	std::string kernel_name = "pooling_layer_MxN_nchw";
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
	}
	break;
	}
	case DataLayout::NHWC:
	{
	// Floating point mixed precision is support on F16 only
	const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;

	// Wider accumulation is required to avoid accuracy loss
	// Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation)
	// Cast 2: Quantized (int8/uint8 src data and int32 accumulation )
	DataType acc_data_type = data_type;

	if(use_fp_mixed_precision)
	{
	acc_data_type = DataType::F32;
	}
	else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX)
	{
	acc_data_type = DataType::S32;
	}

	build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type));
	build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION");
	build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
	build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
	build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
	build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
	build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel)));
	build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size)));
	build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration));
	if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
	{
	build_opts.add_option_if(indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");

	std::string kernel_name = "pooling_layer_2x2_nhwc";
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
	}
	else
	{
	std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
	}
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Not implemented");
	}

	// Configure kernel window
	Window win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration));
	ICLKernel::configure_internal(win);

	// 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(dst->dimension(idx_width));
	_config_id += "_";
	_config_id += support::cpp11::to_string(dst->dimension(idx_height));
	_config_id += "_";
	_config_id += support::cpp11::to_string(dst->dimension(idx_channel));
	_config_id += "_";
	_config_id += lower_string(string_from_data_layout(src->data_layout()));

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClPool2dKernel::validate(const ITensorInfo src, const ITensorInfo dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices));
	return Status{};
	}

	void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::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();

	const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
	auto indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1));

	// 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
	{
	// Set srcs
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src, slice);
	add_3D_tensor_argument(idx, dst, slice);
	if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
	{
	add_3D_tensor_argument(idx, indices, slice);
	}
	enqueue(queue, *this, slice, lws_hint());
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	break;
	}
	case DataLayout::NHWC:
	{
	const size_t batch_size = dst->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, src->info()->dimension(0), _num_elems_processed_per_iteration));
	in_slice.set(Window::DimY, Window::Dimension(0, src->info()->dimension(1), pool_stride_x));
	in_slice.set(Window::DimZ, Window::Dimension(0, src->info()->dimension(2), pool_stride_y));
	in_slice.set(3, Window::Dimension(0, batch_size, 1));
	do
	{
	// Set srcs
	unsigned int idx = 0;
	add_4D_tensor_argument(idx, src, in_slice);
	add_4D_tensor_argument(idx, dst, slice);
	if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
	{
	add_4D_tensor_argument(idx, indices, 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 kernels
	} // namespace opencl
	} // namespace arm_compute