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

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.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/utils/misc/ShapeCalculator.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
 {
     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::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
     }
     else
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON(axis == 0);
     }
     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) && (input->dimension(0) == 0) && (input->data_type() != DataType::QASYMM8)
                                 && (input->data_type() != DataType::QASYMM8_SIGNED));
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN), "Not supported reduction operation, use CLArgMinMaxLayer");

     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
     }

     return Status{};
 }
 } // namespace

 CLReductionOperationKernel::CLReductionOperationKernel()
     : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE)
 {
     _type = CLKernelType::ELEMENTWISE;
 }

 void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op)
 {
     configure(CLKernelLibrary::get().get_compile_context(), input, output, axis, op);
 }

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

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

     auto padding_info = get_padding_info({ input, output });

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

     const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, true);
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).reset_padding().set_is_resizable(true));

     // Set build options
     CLBuildOptions build_opts;
     DataType       data_type = input->info()->data_type();
     std::string    data_type_promoted{};

     if(is_data_type_quantized(data_type))
     {
         data_type_promoted = "int";
     }
     else
     {
         data_type_promoted = get_cl_type_from_data_type(data_type);
     }

     const unsigned int width             = input->info()->dimension(0) * input->info()->num_channels();
     unsigned int       vec_size          = (is_data_type_quantized(input->info()->data_type()) && (axis == 0)) ? 1 : 16;
     vec_size                             = adjust_vec_size(vec_size, width);
     const unsigned int vec_size_leftover = width % vec_size;

     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
     build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted);
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size));
     build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover));
     build_opts.add_option_if(is_data_type_float(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::SUM, "-DSUM");
     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(is_data_type_quantized(data_type), "-DOFFSET=" + support::cpp11::to_string(input->info()->quantization_info().uniform().offset));
     build_opts.add_option_if(is_data_type_quantized(data_type), "-DSCALE=" + float_to_string_with_full_precision(input->info()->quantization_info().uniform().scale));

     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::MIN:
         case ReductionOperation::MAX:
             break;
         case ReductionOperation::PROD:
             build_opts.add_option(("-DOPERATION=product"));
             break;
         default:
             ARM_COMPUTE_ERROR("Unsupported reduction operation");
     }

     // Create kernel
     std::string kernel_axis_name;
     const bool  is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);

     switch(axis)
     {
         case 0:
         {
             build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(width));
             kernel_axis_name = ((is_serial_op) ? "non_parallel_x" : "x");
         }
         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 = create_kernel(compile_context, "reduction_operation_" + kernel_axis_name, build_opts.options());

     // Configure kernel window
     Window win = calculate_max_window(*input->info(), Steps(vec_size));
     win.set(Window::DimX, Window::Dimension(win.x().start(), win.x().end() * _input->info()->num_channels(), win.x().step()));
     ICLKernel::configure_internal(win);

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 Status CLReductionOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, 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 out_window{ window };
                 out_window.set(Window::DimX, Window::Dimension(0, 0, 0));

                 Window in_slice  = window_in.first_slice_window_1D();
                 Window out_slice = out_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);
                 }
                 while(window_in.slide_window_slice_1D(in_slice) && out_window.slide_window_slice_1D(out_slice));
             }
             else
             {
                 // Set out window
                 bool   has_collapsed = true;
                 Window window_in     = window.collapse_if_possible(window, 2, &has_collapsed);
                 ARM_COMPUTE_ERROR_ON(!has_collapsed);

                 Window window_out = window_in;
                 window_out.set(0, Window::Dimension());

                 unsigned int idx = 0;
                 add_3D_tensor_argument(idx, _input, window_in);
                 add_3D_tensor_argument(idx, _output, window_out);
                 enqueue(queue, *this, window_in);
             }
         }
         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);
             }
             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);
             }
             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);
             }
             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) 2017-2021 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/core/CL/kernels/CLReductionOperationKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.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/utils/misc/ShapeCalculator.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, unsigned int axis, ReductionOperation op)
	{
	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::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON(axis == 0);
	}
	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) && (input->dimension(0) == 0) && (input->data_type() != DataType::QASYMM8)
	&& (input->data_type() != DataType::QASYMM8_SIGNED));
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((op == ReductionOperation::ARG_IDX_MAX) \|\| (op == ReductionOperation::ARG_IDX_MIN), "Not supported reduction operation, use CLArgMinMaxLayer");

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
	}

	return Status{};
	}
	} // namespace

	CLReductionOperationKernel::CLReductionOperationKernel()
	: _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE)
	{
	_type = CLKernelType::ELEMENTWISE;
	}

	void CLReductionOperationKernel::configure(const ICLTensor input, ICLTensor output, unsigned int axis, ReductionOperation op)
	{
	configure(CLKernelLibrary::get().get_compile_context(), input, output, axis, op);
	}

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

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

	auto padding_info = get_padding_info({ input, output });

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

	const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, true);
	auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).reset_padding().set_is_resizable(true));

	// Set build options
	CLBuildOptions build_opts;
	DataType data_type = input->info()->data_type();
	std::string data_type_promoted{};

	if(is_data_type_quantized(data_type))
	{
	data_type_promoted = "int";
	}
	else
	{
	data_type_promoted = get_cl_type_from_data_type(data_type);
	}

	const unsigned int width = input->info()->dimension(0) * input->info()->num_channels();
	unsigned int vec_size = (is_data_type_quantized(input->info()->data_type()) && (axis == 0)) ? 1 : 16;
	vec_size = adjust_vec_size(vec_size, width);
	const unsigned int vec_size_leftover = width % vec_size;

	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
	build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted);
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size));
	build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover));
	build_opts.add_option_if(is_data_type_float(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::SUM, "-DSUM");
	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(is_data_type_quantized(data_type), "-DOFFSET=" + support::cpp11::to_string(input->info()->quantization_info().uniform().offset));
	build_opts.add_option_if(is_data_type_quantized(data_type), "-DSCALE=" + float_to_string_with_full_precision(input->info()->quantization_info().uniform().scale));

	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::MIN:
	case ReductionOperation::MAX:
	break;
	case ReductionOperation::PROD:
	build_opts.add_option(("-DOPERATION=product"));
	break;
	default:
	ARM_COMPUTE_ERROR("Unsupported reduction operation");
	}

	// Create kernel
	std::string kernel_axis_name;
	const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);

	switch(axis)
	{
	case 0:
	{
	build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(width));
	kernel_axis_name = ((is_serial_op) ? "non_parallel_x" : "x");
	}
	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 = create_kernel(compile_context, "reduction_operation_" + kernel_axis_name, build_opts.options());

	// Configure kernel window
	Window win = calculate_max_window(*input->info(), Steps(vec_size));
	win.set(Window::DimX, Window::Dimension(win.x().start(), win.x().end() * _input->info()->num_channels(), win.x().step()));
	ICLKernel::configure_internal(win);

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status CLReductionOperationKernel::validate(const ITensorInfo input, const ITensorInfo output, unsigned int axis, ReductionOperation op)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, 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 out_window{ window };
	out_window.set(Window::DimX, Window::Dimension(0, 0, 0));

	Window in_slice = window_in.first_slice_window_1D();
	Window out_slice = out_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);
	}
	while(window_in.slide_window_slice_1D(in_slice) && out_window.slide_window_slice_1D(out_slice));
	}
	else
	{
	// Set out window
	bool has_collapsed = true;
	Window window_in = window.collapse_if_possible(window, 2, &has_collapsed);
	ARM_COMPUTE_ERROR_ON(!has_collapsed);

	Window window_out = window_in;
	window_out.set(0, Window::Dimension());

	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, window_in);
	add_3D_tensor_argument(idx, _output, window_out);
	enqueue(queue, *this, window_in);
	}
	}
	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);
	}
	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);
	}
	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);
	}
	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