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

 /*
  * Copyright (c) 2018-2019 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/CLDepthwiseConvolutionLayer3x3NHWCKernel.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/ICLKernel.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.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)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QASYMM8);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && ((input->data_type() != DataType::QASYMM8) || ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
                                                                                                            && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
                                                                                                            && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU)
                                                                                                            && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC))),
                                     "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); //COMPMID-1317 add fused activation for F32
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     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()) > 1);

     const bool   is_qasymm      = is_data_type_quantized_asymmetric(input->data_type());
     const size_t weights_width  = 3;
     const size_t weights_height = 3;

     if(is_qasymm)
     {
         DepthwiseConvolutionReshapeInfo info;
         info.c0 = 4;
         ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(0) / info.c0) != weights_width * weights_height);
     }
     else
     {
         ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(1) != weights_width) || (weights->dimension(2) != weights_height));
     }

     if(biases != nullptr)
     {
         if(is_qasymm)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(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)
     {
         const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, weights_width, weights_height, conv_info, depth_multiplier);
         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 size_t weights_width  = 3;
     const size_t weights_height = 3;

     // Get convolved dimensions
     const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, weights_width, weights_height, conv_info, depth_multiplier);

     // Output auto inizialitation if not yet initialized
     auto_init_if_empty(*output,
                        output_shape,
                        1,
                        input->data_type(),
                        input->quantization_info());

     const bool is_qasymm   = is_data_type_quantized_asymmetric(input->data_type());
     const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));

     const unsigned int num_rows_processed_per_iteration = is_stride_1 ? 2 : 1;
     const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->element_size());
     const unsigned int num_rows_read_per_iteration      = num_rows_processed_per_iteration + 2;
     const unsigned int num_rows_written_per_iteration   = std::ceil(num_rows_processed_per_iteration / static_cast<float>(conv_info.stride().first));

     BorderSize border_size;
     border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);

     // Configure kernel window
     Window win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_written_per_iteration));

     AccessWindowStatic input_access(input, 0, -border_size.top, ceil_to_multiple(input->dimension(0), num_elems_accessed_per_iteration),
                                     ceil_to_multiple(input->dimension(1) + border_size.bottom, num_rows_read_per_iteration));
     AccessWindowRectangle output_access(output, 0, 0, num_elems_accessed_per_iteration, num_rows_written_per_iteration);

     bool window_changed = false;

     if(is_qasymm)
     {
         window_changed = update_window_and_padding(win, input_access, output_access);
     }
     else
     {
         AccessWindowStatic    weights_access(weights, 0, 0, ceil_to_multiple(weights->dimension(0), num_elems_accessed_per_iteration), weights->dimension(1));
         window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
     }

     if(bias != nullptr)
     {
         AccessWindowHorizontal bias_access(bias, 0, num_elems_accessed_per_iteration);
         window_changed = window_changed || update_window_and_padding(win, bias_access);
     }

     output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }
 } // namespace

 CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel()
     : _num_rows_processed_per_iteration(1), _num_planes_processed_per_iteration(1)
 {
 }

 BorderSize CLDepthwiseConvolutionLayer3x3NHWCKernel::border_size() const
 {
     return _border_size;
 }

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

     const bool is_qasymm         = is_data_type_quantized_asymmetric(input->info()->data_type());
     const bool is_stride_1       = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
     const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());

     _input                              = input;
     _output                             = output;
     _weights                            = weights;
     _biases                             = biases;
     _conv_stride_y                      = conv_info.stride().second;
     _num_rows_processed_per_iteration   = is_stride_1 ? 2 : 1;
     _num_planes_processed_per_iteration = is_stride_1 ? 2 : 1;

     // If QASYMM8 and the 8 bit dot product is available, force _num_planes_processed_per_iteration to 1
     if(is_dot8_supported && is_qasymm)
     {
         _num_planes_processed_per_iteration = 1;
     }

     _border_size = BorderSize(is_qasymm && is_stride_1 ? 0 : conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);

     const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->info()->element_size());

     CLBuildOptions build_opts;
     build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration));
     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()));

     if(is_qasymm)
     {
         float multiplier        = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
         int   output_multiplier = 0;
         int   output_shift      = 0;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);

         build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
         build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
         build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
         build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
         build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
         build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
         build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));

         if(act_info.enabled())
         {
             const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
             const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
             const int o1    = input->info()->quantization_info().offset;

             build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
             build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
             build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
             build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));

             if(output != nullptr)
             {
                 const float s1 = input->info()->quantization_info().scale;
                 const float s2 = output->info()->quantization_info().scale;
                 const int   o2 = output->info()->quantization_info().offset;

                 build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
                 build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
                 if(o1 != o2 || s1 != s2)
                 {
                     build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
                     build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
                 }
             }
         }
     }
     else
     {
         build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type()));
     }

     if(is_stride_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_2=" + support::cpp11::to_string(_output->info()->dimension(2)));
     }
     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_stride_y));
     }
     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)))));

     // Create kernel
     std::string kernel_name = std::string("depthwise_convolution_3x3") + (is_qasymm ? std::string("_quantized") + ((is_dot8_supported
                                                                                                                     && is_stride_1) ? "_dot8" : "") : "") + "_nhwc" + (is_stride_1 ? "_stride1" : "");
     ICLKernel::configure_internal(win_config.second);
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));

     // 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)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info));
     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)
                                 .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);

     // Collapse window
     Window       window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
     const size_t total_batches    = _input->info()->tensor_shape().total_size_upper(3);
     const bool   is_qasymm        = is_data_type_quantized_asymmetric(_input->info()->data_type());

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

     // Create input window and adjust
     Window win_in = win;
     win_in.set_dimension_step(Window::DimY, _num_rows_processed_per_iteration);
     win_in.set_dimension_step(Window::DimZ, _conv_stride_y);

     ARM_COMPUTE_ERROR_ON((win_in.y().step() < window.y().step()) || (win_in.z().step() < window.z().step()));

     Window slice_in  = win_in.first_slice_window_4D();
     Window slice_out = win.first_slice_window_4D();

     unsigned int idx = 2 * num_arguments_per_4D_tensor() + (is_qasymm ? num_arguments_per_2D_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);
     }

     const int max_offset = _input->info()->strides_in_bytes().z() * _input->info()->dimension(2) - (_input->info()->padding().bottom + _input->info()->padding().top) *
                            _input->info()->strides_in_bytes().y();
     _kernel.setArg(idx, max_offset);

     do
     {
         unsigned int idx = 0;
         add_4D_tensor_argument(idx, _input, slice_in);
         add_4D_tensor_argument(idx, _output, slice_out);
         if(is_qasymm)
         {
             add_2D_tensor_argument(idx, _weights, slice_out);
         }
         else
         {
             add_3D_tensor_argument(idx, _weights, slice_out);
         }
         enqueue(queue, *this, slice_out, lws_hint());
     }
     while(win.slide_window_slice_4D(slice_out) && win_in.slide_window_slice_4D(slice_in));
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2019 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/CLDepthwiseConvolutionLayer3x3NHWCKernel.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/ICLKernel.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.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)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QASYMM8);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && ((input->data_type() != DataType::QASYMM8) \|\| ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
	&& (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
	&& (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU)
	&& (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC))),
	"For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); //COMPMID-1317 add fused activation for F32
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	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()) > 1);

	const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
	const size_t weights_width = 3;
	const size_t weights_height = 3;

	if(is_qasymm)
	{
	DepthwiseConvolutionReshapeInfo info;
	info.c0 = 4;
	ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(0) / info.c0) != weights_width * weights_height);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(1) != weights_width) \|\| (weights->dimension(2) != weights_height));
	}

	if(biases != nullptr)
	{
	if(is_qasymm)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(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)
	{
	const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, weights_width, weights_height, conv_info, depth_multiplier);
	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 size_t weights_width = 3;
	const size_t weights_height = 3;

	// Get convolved dimensions
	const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, weights_width, weights_height, conv_info, depth_multiplier);

	// Output auto inizialitation if not yet initialized
	auto_init_if_empty(*output,
	output_shape,
	1,
	input->data_type(),
	input->quantization_info());

	const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
	const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));

	const unsigned int num_rows_processed_per_iteration = is_stride_1 ? 2 : 1;
	const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->element_size());
	const unsigned int num_rows_read_per_iteration = num_rows_processed_per_iteration + 2;
	const unsigned int num_rows_written_per_iteration = std::ceil(num_rows_processed_per_iteration / static_cast<float>(conv_info.stride().first));

	BorderSize border_size;
	border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);

	// Configure kernel window
	Window win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_written_per_iteration));

	AccessWindowStatic input_access(input, 0, -border_size.top, ceil_to_multiple(input->dimension(0), num_elems_accessed_per_iteration),
	ceil_to_multiple(input->dimension(1) + border_size.bottom, num_rows_read_per_iteration));
	AccessWindowRectangle output_access(output, 0, 0, num_elems_accessed_per_iteration, num_rows_written_per_iteration);

	bool window_changed = false;

	if(is_qasymm)
	{
	window_changed = update_window_and_padding(win, input_access, output_access);
	}
	else
	{
	AccessWindowStatic weights_access(weights, 0, 0, ceil_to_multiple(weights->dimension(0), num_elems_accessed_per_iteration), weights->dimension(1));
	window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
	}

	if(bias != nullptr)
	{
	AccessWindowHorizontal bias_access(bias, 0, num_elems_accessed_per_iteration);
	window_changed = window_changed \|\| update_window_and_padding(win, bias_access);
	}

	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}
	} // namespace

	CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel()
	: _num_rows_processed_per_iteration(1), _num_planes_processed_per_iteration(1)
	{
	}

	BorderSize CLDepthwiseConvolutionLayer3x3NHWCKernel::border_size() const
	{
	return _border_size;
	}

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

	const bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type());
	const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
	const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());

	_input = input;
	_output = output;
	_weights = weights;
	_biases = biases;
	_conv_stride_y = conv_info.stride().second;
	_num_rows_processed_per_iteration = is_stride_1 ? 2 : 1;
	_num_planes_processed_per_iteration = is_stride_1 ? 2 : 1;

	// If QASYMM8 and the 8 bit dot product is available, force _num_planes_processed_per_iteration to 1
	if(is_dot8_supported && is_qasymm)
	{
	_num_planes_processed_per_iteration = 1;
	}

	_border_size = BorderSize(is_qasymm && is_stride_1 ? 0 : conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);

	const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->info()->element_size());

	CLBuildOptions build_opts;
	build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration));
	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()));

	if(is_qasymm)
	{
	float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
	int output_multiplier = 0;
	int output_shift = 0;
	quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);

	build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
	build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
	build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
	build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
	build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
	build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
	build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));

	if(act_info.enabled())
	{
	const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
	const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
	const int o1 = input->info()->quantization_info().offset;

	build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
	build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
	build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
	build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));

	if(output != nullptr)
	{
	const float s1 = input->info()->quantization_info().scale;
	const float s2 = output->info()->quantization_info().scale;
	const int o2 = output->info()->quantization_info().offset;

	build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
	build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
	if(o1 != o2 \|\| s1 != s2)
	{
	build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
	build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
	}
	}
	}
	}
	else
	{
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type()));
	}

	if(is_stride_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_2=" + support::cpp11::to_string(_output->info()->dimension(2)));
	}
	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_stride_y));
	}
	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)))));

	// Create kernel
	std::string kernel_name = std::string("depthwise_convolution_3x3") + (is_qasymm ? std::string("_quantized") + ((is_dot8_supported
	&& is_stride_1) ? "_dot8" : "") : "") + "_nhwc" + (is_stride_1 ? "_stride1" : "");
	ICLKernel::configure_internal(win_config.second);
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));

	// 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)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info));
	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)
	.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);

	// Collapse window
	Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
	const size_t total_batches = _input->info()->tensor_shape().total_size_upper(3);
	const bool is_qasymm = is_data_type_quantized_asymmetric(_input->info()->data_type());

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

	// Create input window and adjust
	Window win_in = win;
	win_in.set_dimension_step(Window::DimY, _num_rows_processed_per_iteration);
	win_in.set_dimension_step(Window::DimZ, _conv_stride_y);

	ARM_COMPUTE_ERROR_ON((win_in.y().step() < window.y().step()) \|\| (win_in.z().step() < window.z().step()));

	Window slice_in = win_in.first_slice_window_4D();
	Window slice_out = win.first_slice_window_4D();

	unsigned int idx = 2 * num_arguments_per_4D_tensor() + (is_qasymm ? num_arguments_per_2D_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);
	}

	const int max_offset = _input->info()->strides_in_bytes().z() * _input->info()->dimension(2) - (_input->info()->padding().bottom + _input->info()->padding().top) *
	_input->info()->strides_in_bytes().y();
	_kernel.setArg(idx, max_offset);

	do
	{
	unsigned int idx = 0;
	add_4D_tensor_argument(idx, _input, slice_in);
	add_4D_tensor_argument(idx, _output, slice_out);
	if(is_qasymm)
	{
	add_2D_tensor_argument(idx, _weights, slice_out);
	}
	else
	{
	add_3D_tensor_argument(idx, _weights, slice_out);
	}
	enqueue(queue, *this, slice_out, lws_hint());
	}
	while(win.slide_window_slice_4D(slice_out) && win_in.slide_window_slice_4D(slice_in));
	}
	} // namespace arm_compute