src/gpu/cl/kernels/ClPool3dKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2022 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/ClPool3dKernel.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"
 #include "utils/TypePrinter.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 Pooling3dLayerInfo &pool_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->data_layout() != DataLayout::NDHWC, "Only NDHWC layout supported");

     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.stride.x() == 0 || pool_info.stride.y() == 0 || pool_info.stride.z() == 0), "Strides cannot be zero.");
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32, DataType::QASYMM8_SIGNED, DataType::QASYMM8);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((!is_data_type_float(src->data_type())) && (!pool_info.exclude_padding
                                                                                 && (pool_info.pool_type == PoolingType::AVG)),
                                     "Exclude padding is unsupported for non-float types for Avg op");

     const auto         data_layout       = src->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 int          idx_depth         = get_data_layout_dimension_index(data_layout, DataLayoutDimension::DEPTH);
     const bool         is_global_pooling = pool_info.is_global_pooling;
     const unsigned int pool_size_x       = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
     const unsigned int pool_size_y       = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
     const unsigned int pool_size_z       = is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth;
     int                output_width      = 0;
     int                output_height     = 0;
     int                output_depth      = 0;

     bool round_type_ceil_with_asymm_padding = (pool_info.round_type == DimensionRoundingType::CEIL) && (!is_symmetric(pool_info.padding));
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(round_type_ceil_with_asymm_padding, "Cannot use dimension round type CEIL when padding is asymmetric.");

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_3d_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported");
     std::tie(output_width, output_height, output_depth) = scaled_3d_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height],
                                                                                       src->tensor_shape()[idx_depth], pool_size_x, pool_size_y,
                                                                                       pool_size_z, pool_info);

     ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1 || output_depth < 1), "Calculated output dimension size is invalid");
     // 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_pool3d_shape(src->tensor_shape(), pool_info), 1, dst->data_type()));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &out_info);
     }

     return Status{};
 }
 } // namespace

 ClPool3dKernel::ClPool3dKernel()
 {
     _type = CLKernelType::POOL;
 }

 void ClPool3dKernel::configure(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *dst, const Pooling3dLayerInfo &pool_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info));
     auto padding_info = get_padding_info({ src, dst });

     // Auto init if empty
     TensorShape out_shape = compute_pool3d_shape(src->tensor_shape(), pool_info);
     auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));

     // Set instance variables
     _pool_info   = pool_info;
     _data_layout = src->data_layout();

     _num_elems_processed_per_iteration = (dst->data_type() == DataType::F32) ? 2 : 4;
     _num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(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_depth       = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::DEPTH);
     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 int         pool_size_z     = pool_info.is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth;
     const bool        exclude_padding = pool_info.exclude_padding;
     const int         pool_stride_x   = pool_info.stride.x();
     const int         pool_stride_y   = pool_info.stride.y();
     const int         pool_stride_z   = pool_info.stride.z();
     const int         pool_pad_top    = pool_info.padding.top;
     const int         pool_pad_left   = pool_info.padding.left;
     const int         pool_pad_front  = pool_info.padding.front;
     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("-DSTRIDE_Z=" + support::cpp11::to_string(pool_stride_z));
     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("-DPAD_Z=" + support::cpp11::to_string(pool_pad_front));
     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("-DPOOL_SIZE_Z=" + support::cpp11::to_string(pool_size_z));
     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("-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(idx_depth)));

     // If datatype is quantized add relevant parameters
     if(is_data_type_quantized_asymmetric(data_type) && 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
     // 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)
     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) // Use S32 for avg pooling to allow for integer division
     {
         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("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
     build_opts.add_option("-DDST_DEPTH=" + support::cpp11::to_string(dst->dimension(idx_depth)));
     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 datatype is quantized use quantized kernel function
     std::string kernel_name = (is_data_type_quantized_asymmetric(data_type) ? "pooling_3d_layer_MxN_ndhwc_quantized" : "pooling_3d_layer_MxN_ndhwc");
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

     // 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_3d";
     _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 ClPool3dKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const Pooling3dLayerInfo &pool_info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info));
     return Status{};
 }

 void ClPool3dKernel::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);

     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));

     // Collapse 3D window
     Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);

     // Set CL kernel arguments
     unsigned int idx = 0;
     // Passing of the window not needed, as the steps are not used for the pool3d kernel
     add_5D_tensor_argument(idx, src, window);
     add_5D_tensor_argument(idx, dst, window);
     enqueue(queue, *this, window_collapsed, lws_hint());
 }
 } // namespace kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2022 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/ClPool3dKernel.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"
	#include "utils/TypePrinter.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 Pooling3dLayerInfo &pool_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->data_layout() != DataLayout::NDHWC, "Only NDHWC layout supported");

	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.stride.x() == 0 \|\| pool_info.stride.y() == 0 \|\| pool_info.stride.z() == 0), "Strides cannot be zero.");
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32, DataType::QASYMM8_SIGNED, DataType::QASYMM8);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((!is_data_type_float(src->data_type())) && (!pool_info.exclude_padding
	&& (pool_info.pool_type == PoolingType::AVG)),
	"Exclude padding is unsupported for non-float types for Avg op");

	const auto data_layout = src->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 int idx_depth = get_data_layout_dimension_index(data_layout, DataLayoutDimension::DEPTH);
	const bool is_global_pooling = pool_info.is_global_pooling;
	const unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
	const unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
	const unsigned int pool_size_z = is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth;
	int output_width = 0;
	int output_height = 0;
	int output_depth = 0;

	bool round_type_ceil_with_asymm_padding = (pool_info.round_type == DimensionRoundingType::CEIL) && (!is_symmetric(pool_info.padding));
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(round_type_ceil_with_asymm_padding, "Cannot use dimension round type CEIL when padding is asymmetric.");

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_3d_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported");
	std::tie(output_width, output_height, output_depth) = scaled_3d_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height],
	src->tensor_shape()[idx_depth], pool_size_x, pool_size_y,
	pool_size_z, pool_info);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 \|\| output_height < 1 \|\| output_depth < 1), "Calculated output dimension size is invalid");
	// 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_pool3d_shape(src->tensor_shape(), pool_info), 1, dst->data_type()));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &out_info);
	}

	return Status{};
	}
	} // namespace

	ClPool3dKernel::ClPool3dKernel()
	{
	_type = CLKernelType::POOL;
	}

	void ClPool3dKernel::configure(const ClCompileContext &compile_context, const ITensorInfo src, ITensorInfo dst, const Pooling3dLayerInfo &pool_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info));
	auto padding_info = get_padding_info({ src, dst });

	// Auto init if empty
	TensorShape out_shape = compute_pool3d_shape(src->tensor_shape(), pool_info);
	auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));

	// Set instance variables
	_pool_info = pool_info;
	_data_layout = src->data_layout();

	_num_elems_processed_per_iteration = (dst->data_type() == DataType::F32) ? 2 : 4;
	_num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(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_depth = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::DEPTH);
	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 int pool_size_z = pool_info.is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth;
	const bool exclude_padding = pool_info.exclude_padding;
	const int pool_stride_x = pool_info.stride.x();
	const int pool_stride_y = pool_info.stride.y();
	const int pool_stride_z = pool_info.stride.z();
	const int pool_pad_top = pool_info.padding.top;
	const int pool_pad_left = pool_info.padding.left;
	const int pool_pad_front = pool_info.padding.front;
	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("-DSTRIDE_Z=" + support::cpp11::to_string(pool_stride_z));
	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("-DPAD_Z=" + support::cpp11::to_string(pool_pad_front));
	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("-DPOOL_SIZE_Z=" + support::cpp11::to_string(pool_size_z));
	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("-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(idx_depth)));

	// If datatype is quantized add relevant parameters
	if(is_data_type_quantized_asymmetric(data_type) && 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
	// 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)
	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) // Use S32 for avg pooling to allow for integer division
	{
	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("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
	build_opts.add_option("-DDST_DEPTH=" + support::cpp11::to_string(dst->dimension(idx_depth)));
	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 datatype is quantized use quantized kernel function
	std::string kernel_name = (is_data_type_quantized_asymmetric(data_type) ? "pooling_3d_layer_MxN_ndhwc_quantized" : "pooling_3d_layer_MxN_ndhwc");
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());

	// 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_3d";
	_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 ClPool3dKernel::validate(const ITensorInfo src, const ITensorInfo dst, const Pooling3dLayerInfo &pool_info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info));
	return Status{};
	}

	void ClPool3dKernel::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);

	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));

	// Collapse 3D window
	Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);

	// Set CL kernel arguments
	unsigned int idx = 0;
	// Passing of the window not needed, as the steps are not used for the pool3d kernel
	add_5D_tensor_argument(idx, src, window);
	add_5D_tensor_argument(idx, dst, window);
	enqueue(queue, *this, window_collapsed, lws_hint());
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute