src/core/CL/kernels/CLReductionOperationKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "arm_compute/core/CL/kernels/CLReductionOperationKernel.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/ICLTensor.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"

 #include "support/ToolchainSupport.h"

 namespace arm_compute
 {
 namespace
 {
 // OpenCL kernel requires input width to be a power of 2 for x-axis.
 constexpr unsigned int border_val = 64;

 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, unsigned int width)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     if(input->num_channels() == 1)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32);
     }
     else
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32);
     }
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(op == ReductionOperation::SUM_SQUARE && input->data_type() == DataType::QASYMM8, "Not supported reduction operation for QASYMM8");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
     ARM_COMPUTE_RETURN_ERROR_ON(op == ReductionOperation::MEAN_SUM && axis == 0 && width == 0 && input->data_type() != DataType::QASYMM8);

     if(output->total_size() != 0)
     {
         if(op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8, "Not supported operation for QASYMM8");
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
         }
     }

     return Status{};
 }

 std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int axis, ReductionOperation op)
 {
     // Output tensor auto initialization if not yet initialized
     const bool        is_arg_min_max   = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX);
     const TensorShape output_shape     = arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, !is_arg_min_max);
     const DataType    output_data_type = is_arg_min_max ? DataType::S32 : input->data_type();
     auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));

     const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16;
     Window             win                               = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
     bool               window_changed                    = false;
     const bool         is_serial_op                      = needs_serialized_reduction(op, input->data_type(), axis);

     switch(axis)
     {
         case 0:
         {
             if(is_serial_op)
             {
                 AccessWindowHorizontal input_access(input, 0, input->dimension(0));
                 AccessWindowHorizontal output_access(output, 0, 1);
                 window_changed = update_window_and_padding(win, input_access, output_access);
                 output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
             }
             else
             {
                 const unsigned int     border_width = ((input->dimension(0) % border_val) != 0) ? border_val - input->dimension(0) % border_val : 0;
                 AccessWindowStatic     input_access(input, 0, 0, input->dimension(0) + border_width, 1);
                 AccessWindowHorizontal output_access(output, 0, 1);
                 window_changed = update_window_and_padding(win, input_access, output_access);
                 output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
             }
         }
         break;
         case 1:
         case 2:
         case 3:
         {
             AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
             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 supported");
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};

     return std::make_tuple(err, win);
 }
 } // namespace

 CLReductionOperationKernel::CLReductionOperationKernel()
     : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE), _border_size()
 {
 }

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

 void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, unsigned int width)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op, width));

     _input          = input;
     _output         = output;
     _reduction_axis = axis;
     _op             = op;

     // Set build options
     CLBuildOptions build_opts;
     std::string    data_type_promoted = get_cl_type_from_data_type(input->info()->data_type());
     if(is_data_type_quantized(input->info()->data_type()))
     {
         data_type_promoted = "uint";
     }

     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
     build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted);
     build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE");
     build_opts.add_option_if(op == ReductionOperation::SUM_SQUARE, "-DSUM_SQUARE");
     build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DMEAN");
     build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX");
     build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN");
     build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD");
     build_opts.add_option_if(op == ReductionOperation::MIN, "-DMIN");
     build_opts.add_option_if(op == ReductionOperation::MAX, "-DMAX");
     build_opts.add_option_if(input->info()->num_channels() == 2, "-DCOMPLEX");

     switch(op)
     {
         case ReductionOperation::SUM_SQUARE:
             build_opts.add_option(("-DOPERATION=square_sum"));
             break;
         case ReductionOperation::SUM:
         case ReductionOperation::MEAN_SUM:
             build_opts.add_option(("-DOPERATION=sum"));
             break;
         case ReductionOperation::ARG_IDX_MAX:
         case ReductionOperation::ARG_IDX_MIN:
         case ReductionOperation::MIN:
         case ReductionOperation::MAX:
             break;
         case ReductionOperation::PROD:
             build_opts.add_option(("-DOPERATION=product"));
             break;
         default:
             ARM_COMPUTE_ERROR("Unsupported reduction operation");
     }

     // Create kernel
     cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange();
     std::string kernel_axis_name;
     const bool  is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);

     switch(axis)
     {
         case 0:
         {
             if(is_serial_op)
             {
                 build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
                 build_opts.add_option_if_else(_input->info()->data_type() == DataType::F16, "-DCOND_DATA_TYPE=short", "-DCOND_DATA_TYPE=int");
                 kernel_axis_name = "non_parallel_x";
             }
             else
             {
                 build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DWIDTH=" + support::cpp11::to_string(width));
                 const unsigned int width_leftover = input->info()->dimension(0) % border_val;
                 const unsigned int border_width   = (width_leftover != 0) ? border_val - width_leftover : 0;
                 const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16);
                 kernel_axis_name                  = "x";

                 // Set the number of WG based on the input size. If input width is < 128
                 // we can use fewer threads than 8.
                 lws_hint     = cl::NDRange(std::min(8U, num_of_threads));
                 _border_size = BorderSize(0, border_width, 0, 0);
             }
         }
         break;
         case 1:
             build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
             kernel_axis_name = "y";
             break;
         case 2:
             build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
             kernel_axis_name = "z";
             break;
         case 3:
             build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
             build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3)));
             kernel_axis_name = "w";
             break;
         default:
             ARM_COMPUTE_ERROR("Not supported");
     }
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reduction_operation_" + kernel_axis_name, build_opts.options()));

     // Configure kernel window
     auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis, op);

     ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

     ICLKernel::configure_internal(std::get<1>(win_config), lws_hint);
 }

 Status CLReductionOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, unsigned int width)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op, width));
     ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), axis, op)));

     return Status{};
 }

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

     const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);
     switch(_reduction_axis)
     {
         case 0:
         {
             // We use parallel reduction only in non quantized types
             if(is_serial_op)
             {
                 // Get first input and output slices
                 Window window_in{ window };
                 window_in.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));

                 Window in_slice  = window.first_slice_window_1D();
                 Window out_slice = window.first_slice_window_1D();

                 do
                 {
                     unsigned int idx = 0;
                     add_1D_tensor_argument(idx, _input, in_slice);
                     add_1D_tensor_argument(idx, _output, out_slice);
                     enqueue(queue, *this, in_slice, lws_hint());
                 }
                 while(window_in.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(out_slice));
             }
             else
             {
                 // Set out window
                 Window out_window(window);
                 out_window.set(Window::DimX, Window::Dimension(0, 0, 0));

                 // Get first input and output slices
                 Window in_slice  = window.first_slice_window_2D();
                 Window out_slice = out_window.first_slice_window_2D();

                 // Reshape window
                 const unsigned int border_width = ((in_slice.x().end() % border_val) != 0) ? border_val - in_slice.x().end() % border_val : 0;
                 in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start(), in_slice.x().end() + border_width, in_slice.x().step()));

                 // Set local sums buffer
                 unsigned int local_res_size = lws_hint()[0] * _input->info()->element_size();
                 _kernel.setArg(num_arguments_per_2D_tensor() * 2, local_res_size, nullptr);

                 do
                 {
                     unsigned int idx = 0;
                     add_2D_tensor_argument(idx, _input, in_slice);
                     add_2D_tensor_argument(idx, _output, out_slice);
                     enqueue(queue, *this, in_slice, lws_hint());
                 }
                 while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
             }
         }
         break;
         case 1:
         {
             // Get first input and output slices
             Window window_in{ window };
             window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
             Window in_slice  = window_in.first_slice_window_2D();
             Window out_slice = window.first_slice_window_2D();

             do
             {
                 unsigned int idx = 0;
                 add_2D_tensor_argument(idx, _input, in_slice);
                 add_2D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
         }
         break;
         case 2:
         {
             // Get first input and output slices
             Window window_in{ window };
             window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
             Window in_slice  = window_in.first_slice_window_3D();
             Window out_slice = window.first_slice_window_3D();

             do
             {
                 unsigned int idx = 0;
                 add_3D_tensor_argument(idx, _input, in_slice);
                 add_3D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
         }
         break;
         case 3:
         {
             // Get first input and output slices
             Window window_in{ window };
             window_in.set(3, Window::Dimension(0, 1, 1));
             Window in_slice  = window_in.first_slice_window_4D();
             Window out_slice = window.first_slice_window_4D();

             do
             {
                 unsigned int idx = 0;
                 add_4D_tensor_argument(idx, _input, in_slice);
                 add_4D_tensor_argument(idx, _output, out_slice);
                 enqueue(queue, *this, in_slice, lws_hint());
             }
             while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice));
         }
         break;
         default:
             ARM_COMPUTE_ERROR("Not supported");
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2019 ARM Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "arm_compute/core/CL/kernels/CLReductionOperationKernel.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/ICLTensor.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"

	#include "support/ToolchainSupport.h"

	namespace arm_compute
	{
	namespace
	{
	// OpenCL kernel requires input width to be a power of 2 for x-axis.
	constexpr unsigned int border_val = 64;

	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, unsigned int axis, ReductionOperation op, unsigned int width)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	if(input->num_channels() == 1)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(op == ReductionOperation::SUM_SQUARE && input->data_type() == DataType::QASYMM8, "Not supported reduction operation for QASYMM8");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
	ARM_COMPUTE_RETURN_ERROR_ON(op == ReductionOperation::MEAN_SUM && axis == 0 && width == 0 && input->data_type() != DataType::QASYMM8);

	if(output->total_size() != 0)
	{
	if(op == ReductionOperation::ARG_IDX_MAX \|\| op == ReductionOperation::ARG_IDX_MIN)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8, "Not supported operation for QASYMM8");
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
	}
	}

	return Status{};
	}

	std::tuple<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo output, unsigned int axis, ReductionOperation op)
	{
	// Output tensor auto initialization if not yet initialized
	const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN \|\| op == ReductionOperation::ARG_IDX_MAX);
	const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, !is_arg_min_max);
	const DataType output_data_type = is_arg_min_max ? DataType::S32 : input->data_type();
	auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));

	const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16;
	Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
	bool window_changed = false;
	const bool is_serial_op = needs_serialized_reduction(op, input->data_type(), axis);

	switch(axis)
	{
	case 0:
	{
	if(is_serial_op)
	{
	AccessWindowHorizontal input_access(input, 0, input->dimension(0));
	AccessWindowHorizontal output_access(output, 0, 1);
	window_changed = update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	else
	{
	const unsigned int border_width = ((input->dimension(0) % border_val) != 0) ? border_val - input->dimension(0) % border_val : 0;
	AccessWindowStatic input_access(input, 0, 0, input->dimension(0) + border_width, 1);
	AccessWindowHorizontal output_access(output, 0, 1);
	window_changed = update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	}
	break;
	case 1:
	case 2:
	case 3:
	{
	AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
	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 supported");
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};

	return std::make_tuple(err, win);
	}
	} // namespace

	CLReductionOperationKernel::CLReductionOperationKernel()
	: _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE), _border_size()
	{
	}

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

	void CLReductionOperationKernel::configure(const ICLTensor input, ICLTensor output, unsigned int axis, ReductionOperation op, unsigned int width)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op, width));

	_input = input;
	_output = output;
	_reduction_axis = axis;
	_op = op;

	// Set build options
	CLBuildOptions build_opts;
	std::string data_type_promoted = get_cl_type_from_data_type(input->info()->data_type());
	if(is_data_type_quantized(input->info()->data_type()))
	{
	data_type_promoted = "uint";
	}

	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
	build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted);
	build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE");
	build_opts.add_option_if(op == ReductionOperation::SUM_SQUARE, "-DSUM_SQUARE");
	build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DMEAN");
	build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX");
	build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN");
	build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD");
	build_opts.add_option_if(op == ReductionOperation::MIN, "-DMIN");
	build_opts.add_option_if(op == ReductionOperation::MAX, "-DMAX");
	build_opts.add_option_if(input->info()->num_channels() == 2, "-DCOMPLEX");

	switch(op)
	{
	case ReductionOperation::SUM_SQUARE:
	build_opts.add_option(("-DOPERATION=square_sum"));
	break;
	case ReductionOperation::SUM:
	case ReductionOperation::MEAN_SUM:
	build_opts.add_option(("-DOPERATION=sum"));
	break;
	case ReductionOperation::ARG_IDX_MAX:
	case ReductionOperation::ARG_IDX_MIN:
	case ReductionOperation::MIN:
	case ReductionOperation::MAX:
	break;
	case ReductionOperation::PROD:
	build_opts.add_option(("-DOPERATION=product"));
	break;
	default:
	ARM_COMPUTE_ERROR("Unsupported reduction operation");
	}

	// Create kernel
	cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange();
	std::string kernel_axis_name;
	const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);

	switch(axis)
	{
	case 0:
	{
	if(is_serial_op)
	{
	build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
	build_opts.add_option_if_else(_input->info()->data_type() == DataType::F16, "-DCOND_DATA_TYPE=short", "-DCOND_DATA_TYPE=int");
	kernel_axis_name = "non_parallel_x";
	}
	else
	{
	build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DWIDTH=" + support::cpp11::to_string(width));
	const unsigned int width_leftover = input->info()->dimension(0) % border_val;
	const unsigned int border_width = (width_leftover != 0) ? border_val - width_leftover : 0;
	const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16);
	kernel_axis_name = "x";

	// Set the number of WG based on the input size. If input width is < 128
	// we can use fewer threads than 8.
	lws_hint = cl::NDRange(std::min(8U, num_of_threads));
	_border_size = BorderSize(0, border_width, 0, 0);
	}
	}
	break;
	case 1:
	build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
	kernel_axis_name = "y";
	break;
	case 2:
	build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
	kernel_axis_name = "z";
	break;
	case 3:
	build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
	build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3)));
	kernel_axis_name = "w";
	break;
	default:
	ARM_COMPUTE_ERROR("Not supported");
	}
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reduction_operation_" + kernel_axis_name, build_opts.options()));

	// Configure kernel window
	auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis, op);

	ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));

	ICLKernel::configure_internal(std::get<1>(win_config), lws_hint);
	}

	Status CLReductionOperationKernel::validate(const ITensorInfo input, const ITensorInfo output, unsigned int axis, ReductionOperation op, unsigned int width)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op, width));
	ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), axis, op)));

	return Status{};
	}

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

	const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);
	switch(_reduction_axis)
	{
	case 0:
	{
	// We use parallel reduction only in non quantized types
	if(is_serial_op)
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));

	Window in_slice = window.first_slice_window_1D();
	Window out_slice = window.first_slice_window_1D();

	do
	{
	unsigned int idx = 0;
	add_1D_tensor_argument(idx, _input, in_slice);
	add_1D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(out_slice));
	}
	else
	{
	// Set out window
	Window out_window(window);
	out_window.set(Window::DimX, Window::Dimension(0, 0, 0));

	// Get first input and output slices
	Window in_slice = window.first_slice_window_2D();
	Window out_slice = out_window.first_slice_window_2D();

	// Reshape window
	const unsigned int border_width = ((in_slice.x().end() % border_val) != 0) ? border_val - in_slice.x().end() % border_val : 0;
	in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start(), in_slice.x().end() + border_width, in_slice.x().step()));

	// Set local sums buffer
	unsigned int local_res_size = lws_hint()[0] * _input->info()->element_size();
	_kernel.setArg(num_arguments_per_2D_tensor() * 2, local_res_size, nullptr);

	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, in_slice);
	add_2D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
	}
	}
	break;
	case 1:
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
	Window in_slice = window_in.first_slice_window_2D();
	Window out_slice = window.first_slice_window_2D();

	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, in_slice);
	add_2D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
	}
	break;
	case 2:
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
	Window in_slice = window_in.first_slice_window_3D();
	Window out_slice = window.first_slice_window_3D();

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, in_slice);
	add_3D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
	}
	break;
	case 3:
	{
	// Get first input and output slices
	Window window_in{ window };
	window_in.set(3, Window::Dimension(0, 1, 1));
	Window in_slice = window_in.first_slice_window_4D();
	Window out_slice = window.first_slice_window_4D();

	do
	{
	unsigned int idx = 0;
	add_4D_tensor_argument(idx, _input, in_slice);
	add_4D_tensor_argument(idx, _output, out_slice);
	enqueue(queue, *this, in_slice, lws_hint());
	}
	while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice));
	}
	break;
	default:
	ARM_COMPUTE_ERROR("Not supported");
	}
	}
	} // namespace arm_compute