src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2021 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/CLDepthwiseConvolutionLayer3x3NHWCKernel.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/utils/misc/ShapeCalculator.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/CL/ICLKernel.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 *weights, const ITensorInfo *biases, const ITensorInfo *output,
                           const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
 {
     ARM_COMPUTE_UNUSED(act_info);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); // COMPMID-1071 Add depth multiplier support for NHWC

     ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1);
     ARM_COMPUTE_RETURN_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 4);

     ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));

     const size_t weights_width  = 3;
     const size_t weights_height = 3;

     const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation };

     const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(
                                          *input, TensorInfo(TensorShape(weights_width, weights_height), 1, weights->data_type()).set_data_layout(DataLayout::NCHW), info);

     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(1) != weights_width) || (weights->dimension(2) != weights_height));

     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[0]);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);

         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }

     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output,
                                                         const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
 {
     ARM_COMPUTE_UNUSED(weights, bias);
     ARM_COMPUTE_UNUSED(depth_multiplier);

     const bool   is_stride_1_dilation_1           = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1) && dilation.x() == 1 && dilation.y() == 1);
     unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1;

     Window win{};
     Status err{};

     unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->element_size(), input->dimension(0));
     win                                           = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_processed_per_iteration));

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

 CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel()
     : _input(), _output(), _weights(), _biases(), _num_planes_processed_per_iteration(1)
 {
 }

 void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
                                                          const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
 {
     configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
 }

 void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
                                                          const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(),
                                                   conv_info, depth_multiplier, act_info, dilation));

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

     auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(),
                                                     conv_info, depth_multiplier, dilation);

     const bool is_stride_1            = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
     const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);

     _input                              = input;
     _output                             = output;
     _weights                            = weights;
     _biases                             = biases;
     _num_planes_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1;

     unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->info()->element_size(), input->info()->dimension(0));
     unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1;

     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type()));
     build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration));
     build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
     build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2)));
     build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
     build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
     build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(input->info()->dimension(0) % num_elems_accessed_per_iteration));
     build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
     build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1,
                              "-DDST_DEPTH=" + support::cpp11::to_string(static_cast<int>(std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)))));
     build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
     build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));

     if(is_stride_1_dilation_1)
     {
         build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(num_rows_processed_per_iteration));
         build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration));
         build_opts.add_option("-DDST_DIM_1=" + support::cpp11::to_string(_output->info()->dimension(1)));
         build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2)));
         build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string((input->info()->dimension(1) + conv_info.pad_left() + conv_info.pad_right()) % num_rows_processed_per_iteration));
     }
     else
     {
         build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
         build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
         build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
         build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
     }

     // Create kernel
     std::string kernel_name;
     kernel_name = std::string("depthwise_convolution_3x3_nhwc");
     kernel_name += (is_stride_1_dilation_1 ? "_stride1" : "");

     ICLKernel::configure_internal(win_config.second);
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));

     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
     _config_id += "_";
     _config_id += string_from_data_type(input->info()->data_type());
 }

 Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
                                                           const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(),
                                                               biases != nullptr ? biases->clone().get() : nullptr,
                                                               output->clone().get(), conv_info, depth_multiplier, dilation)
                                 .first);
     return Status{};
 }

 void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     const size_t total_batches = _input->info()->tensor_shape().total_size_upper(3);

     Window win = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
     win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)) * total_batches, 1));

     unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor();

     if(_biases != nullptr)
     {
         Window win_biases;
         win_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
         win_biases.set_dimension_step(Window::DimX, window.x().step());
         add_1D_tensor_argument(idx, _biases, win_biases);
     }

     Window slice = win.first_slice_window_4D();
     do
     {
         unsigned int idx = 0;
         add_4D_tensor_argument(idx, _input, slice);
         add_4D_tensor_argument(idx, _output, slice);
         add_3D_tensor_argument(idx, _weights, slice);

         enqueue(queue, *this, slice, lws_hint());
     }
     while(win.slide_window_slice_4D(slice));
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2021 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/CLDepthwiseConvolutionLayer3x3NHWCKernel.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/utils/misc/ShapeCalculator.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/CL/ICLKernel.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 weights, const ITensorInfo biases, const ITensorInfo output,
	const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
	{
	ARM_COMPUTE_UNUSED(act_info);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); // COMPMID-1071 Add depth multiplier support for NHWC

	ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1);
	ARM_COMPUTE_RETURN_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 4);

	ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) \|\| (dilation.y() < 1));

	const size_t weights_width = 3;
	const size_t weights_height = 3;

	const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation };

	const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(
	*input, TensorInfo(TensorShape(weights_width, weights_height), 1, weights->data_type()).set_data_layout(DataLayout::NCHW), info);

	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(1) != weights_width) \|\| (weights->dimension(2) != weights_height));

	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[0]);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);

	ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
	}

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo weights, ITensorInfo bias, ITensorInfo output,
	const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
	{
	ARM_COMPUTE_UNUSED(weights, bias);
	ARM_COMPUTE_UNUSED(depth_multiplier);

	const bool is_stride_1_dilation_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1) && dilation.x() == 1 && dilation.y() == 1);
	unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1;

	Window win{};
	Status err{};

	unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->element_size(), input->dimension(0));
	win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_processed_per_iteration));

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

	CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel()
	: _input(), _output(), _weights(), _biases(), _num_planes_processed_per_iteration(1)
	{
	}

	void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output,
	const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
	{
	configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
	}

	void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const CLCompileContext &compile_context, const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output,
	const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(),
	conv_info, depth_multiplier, act_info, dilation));

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

	auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(),
	conv_info, depth_multiplier, dilation);

	const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
	const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);

	_input = input;
	_output = output;
	_weights = weights;
	_biases = biases;
	_num_planes_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1;

	unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->info()->element_size(), input->info()->dimension(0));
	unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1;

	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type()));
	build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration));
	build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
	build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2)));
	build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
	build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
	build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(input->info()->dimension(0) % num_elems_accessed_per_iteration));
	build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
	build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1,
	"-DDST_DEPTH=" + support::cpp11::to_string(static_cast<int>(std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)))));
	build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
	build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));

	if(is_stride_1_dilation_1)
	{
	build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(num_rows_processed_per_iteration));
	build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration));
	build_opts.add_option("-DDST_DIM_1=" + support::cpp11::to_string(_output->info()->dimension(1)));
	build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2)));
	build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string((input->info()->dimension(1) + conv_info.pad_left() + conv_info.pad_right()) % num_rows_processed_per_iteration));
	}
	else
	{
	build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
	build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
	build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
	build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
	}

	// Create kernel
	std::string kernel_name;
	kernel_name = std::string("depthwise_convolution_3x3_nhwc");
	kernel_name += (is_stride_1_dilation_1 ? "_stride1" : "");

	ICLKernel::configure_internal(win_config.second);
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));

	// Set config_id for enabling LWS tuning
	_config_id = kernel_name;
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(2));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(1));
	_config_id += "_";
	_config_id += string_from_data_type(input->info()->data_type());
	}

	Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output,
	const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(),
	biases != nullptr ? biases->clone().get() : nullptr,
	output->clone().get(), conv_info, depth_multiplier, dilation)
	.first);
	return Status{};
	}

	void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	const size_t total_batches = _input->info()->tensor_shape().total_size_upper(3);

	Window win = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
	win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)) * total_batches, 1));

	unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor();

	if(_biases != nullptr)
	{
	Window win_biases;
	win_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
	win_biases.set_dimension_step(Window::DimX, window.x().step());
	add_1D_tensor_argument(idx, _biases, win_biases);
	}

	Window slice = win.first_slice_window_4D();
	do
	{
	unsigned int idx = 0;
	add_4D_tensor_argument(idx, _input, slice);
	add_4D_tensor_argument(idx, _output, slice);
	add_3D_tensor_argument(idx, _weights, slice);

	enqueue(queue, *this, slice, lws_hint());
	}
	while(win.slide_window_slice_4D(slice));
	}
	} // namespace arm_compute