src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2023 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/runtime/CL/functions/CLDepthwiseConvolutionLayer.h"

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h"
 #include "src/runtime/heuristics/dwc_native/ClDWCNativeKernelConfig.h"
 #include "src/runtime/heuristics/dwc_native/IClDWCNativeKernelConfig.h"

 #include "src/common/utils/Log.h"

 namespace arm_compute
 {
 using namespace arm_compute::misc;
 using namespace arm_compute::misc::shape_calculator;
 using namespace arm_compute::cl_dwc;

 CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)),
       _dwc_native_kernel(std::make_unique<CLDepthwiseConvolutionLayerNativeKernel>()),
       _permute_input_to_nhwc(),
       _permute_weights_to_nhwc(),
       _permute_output_to_nchw(),
       _permuted_input(),
       _permuted_weights(),
       _permuted_output(),
       _output_multipliers(),
       _output_shifts(),
       _original_weights(),
       _input(),
       _output(),
       _needs_permute(false),
       _is_prepared(false),
       _is_quantized(false)
 {
 }

 CLDepthwiseConvolutionLayer::~CLDepthwiseConvolutionLayer() = default;

 void CLDepthwiseConvolutionLayer::configure(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 CLDepthwiseConvolutionLayer::configure(const CLCompileContext &compile_context, 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);
     ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayer::validate(input->info(),
                                                                      weights->info(),
                                                                      biases != nullptr ? biases->info() : nullptr,
                                                                      output != nullptr ? output->info() : input->info(),
                                                                      conv_info,
                                                                      depth_multiplier,
                                                                      act_info,
                                                                      dilation));
     ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);

     _is_quantized     = is_data_type_quantized(input->info()->data_type());
     _is_prepared      = false;
     _original_weights = weights;
     _input            = input;
     _output           = output;
     _needs_permute    = input->info()->data_layout() == DataLayout::NCHW;

     const GPUTarget gpu_target = CLScheduler::get().target();

     ICLTensor       *input_to_use   = input;
     const ICLTensor *weights_to_use = weights;
     ICLTensor       *output_to_use  = output;
     if(_needs_permute)
     {
         _memory_group.manage(&_permuted_input);
         _memory_group.manage(&_permuted_output);

         // Configure the function to transform the input tensor from NCHW -> NHWC
         _permute_input_to_nhwc.configure(compile_context, input, &_permuted_input, PermutationVector(2U, 0U, 1U));
         _permuted_input.info()->set_data_layout(DataLayout::NHWC);

         // Configure the function to transform the weights tensor from IHW -> HWI
         _permute_weights_to_nhwc.configure(compile_context, weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
         _permuted_weights.info()->set_data_layout(DataLayout::NHWC);

         // Set output quantization info before dwc kernel configure
         _permuted_output.info()->set_quantization_info(output->info()->quantization_info());

         input_to_use   = &_permuted_input;
         weights_to_use = &_permuted_weights;
         output_to_use  = &_permuted_output;
     }

     CLTensor *output_multipliers_to_use = nullptr;
     CLTensor *output_shifts_to_use      = nullptr;
     if(_is_quantized)
     {
         const size_t idx_c       = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL);
         const size_t num_filters = (is_data_type_quantized_per_channel(weights->info()->data_type())) ? weights->info()->dimension(idx_c) : 1;

         _output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
         _output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));

         output_multipliers_to_use = &_output_multipliers;
         output_shifts_to_use      = &_output_shifts;
     }

     // Get the depthwise convolution compute parameters
     auto t = ClDWCNativeKernelConfigurationFactory::create(gpu_target);
     const DWCComputeKernelInfo dwc_native_compute_info = t->configure(input_to_use->info(), weights_to_use->info(), conv_info, dilation, depth_multiplier);

     const ConvolutionInfo conv_kernel_info{ conv_info, depth_multiplier, act_info, dilation };

     _dwc_native_kernel->set_target(gpu_target);
     _dwc_native_kernel->configure(compile_context, input_to_use, weights_to_use, biases, output_to_use,
                                   dwc_native_compute_info, conv_kernel_info, output_multipliers_to_use, output_shifts_to_use);

     if(_needs_permute)
     {
         _permuted_input.allocator()->allocate();

         // Configure the function to transform the convoluted output to NCHW format
         _permuted_output.info()->set_data_layout(DataLayout::NCHW);
         _permute_output_to_nchw.configure(compile_context, &_permuted_output, output, PermutationVector(1U, 2U, 0U));
         _permuted_output.allocator()->allocate();
     }

     if(_is_quantized)
     {
         _output_multipliers.allocator()->allocate();
         _output_shifts.allocator()->allocate();
     }
 }

 Status CLDepthwiseConvolutionLayer::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_ERROR_ON_NULLPTR(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(!weights->are_values_constant(), "Dynamic weights are not supported");

     const bool in_place = input == output || output == nullptr;
     if(in_place)
     {
         output = input;
     }
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
     const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
     const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);

     ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
     ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());

     const GPUTarget gpu_target = CLScheduler::get().target();

     const ConvolutionInfo conv_kernel_info{ conv_info, depth_multiplier, act_info, dilation };

     const bool needs_permute = input->data_layout() == DataLayout::NCHW;

     const bool is_quantized = is_data_type_quantized(input->data_type());

     TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32));
     if(is_quantized)
     {
         if(is_data_type_quantized_per_channel(weights->data_type()))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);

             const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
             output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c)));
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
         }
     }

     if(needs_permute)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(in_place, "In-place is supported only with NHWC data layout");
         TensorShape           permuted_input_shape   = input->tensor_shape();
         TensorShape           permuted_weights_shape = weights->tensor_shape();
         const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation };
         TensorShape           permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, info);

         permute(permuted_input_shape, PermutationVector(2U, 0U, 1U));
         permute(permuted_weights_shape, PermutationVector(2U, 0U, 1U));
         permute(permuted_output_shape, PermutationVector(2U, 0U, 1U));

         const TensorInfo permuted_input   = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC);
         const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC);
         const TensorInfo permuted_output  = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NHWC);

         ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U)));
         ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U)));

         // Get the depthwise convolution compute parameters
         auto t = ClDWCNativeKernelConfigurationFactory::create(gpu_target);
         const DWCComputeKernelInfo dwc_native_compute_info = t->configure(&permuted_input, &permuted_weights, conv_info, dilation, depth_multiplier);

         ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output,
                                                                                       dwc_native_compute_info, conv_kernel_info, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
         ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U)));
     }
     else
     {
         // Get the depthwise convolution compute parameters
         auto t = ClDWCNativeKernelConfigurationFactory::create(gpu_target);
         const DWCComputeKernelInfo dwc_native_compute_info = t->configure(input, weights, conv_info, dilation, depth_multiplier);
         ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_native_compute_info, conv_kernel_info, &output_multipliers_shifts_info,
                                                                                       &output_multipliers_shifts_info));
     }
     return Status{};
 }

 void CLDepthwiseConvolutionLayer::run()
 {
     prepare();

     MemoryGroupResourceScope scope_mg(_memory_group);

     if(_needs_permute)
     {
         _permute_input_to_nhwc.run();
     }
     CLScheduler::get().enqueue(*_dwc_native_kernel);
     if(_needs_permute)
     {
         _permute_output_to_nchw.run();
     }
 }

 void CLDepthwiseConvolutionLayer::prepare()
 {
     if(!_is_prepared)
     {
         if(_is_quantized)
         {
             _output_multipliers.map();
             _output_shifts.map();
             quantization::compute_quantized_multipliers_and_shifts(_input->info(),
                                                                    _original_weights->info(),
                                                                    _output != nullptr ? _output->info() : _input->info(),
                                                                    reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))),
                                                                    reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0))));
             _output_multipliers.unmap();
             _output_shifts.unmap();
         }

         if(_needs_permute)
         {
             ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());

             _permuted_weights.allocator()->allocate();
             _permute_weights_to_nhwc.run();
             _original_weights->mark_as_unused();
         }
         _is_prepared = true;
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2023 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/runtime/CL/functions/CLDepthwiseConvolutionLayer.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "arm_compute/runtime/CL/CLScheduler.h"
	#include "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h"
	#include "src/runtime/heuristics/dwc_native/ClDWCNativeKernelConfig.h"
	#include "src/runtime/heuristics/dwc_native/IClDWCNativeKernelConfig.h"

	#include "src/common/utils/Log.h"

	namespace arm_compute
	{
	using namespace arm_compute::misc;
	using namespace arm_compute::misc::shape_calculator;
	using namespace arm_compute::cl_dwc;

	CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)),
	_dwc_native_kernel(std::make_unique<CLDepthwiseConvolutionLayerNativeKernel>()),
	_permute_input_to_nhwc(),
	_permute_weights_to_nhwc(),
	_permute_output_to_nchw(),
	_permuted_input(),
	_permuted_weights(),
	_permuted_output(),
	_output_multipliers(),
	_output_shifts(),
	_original_weights(),
	_input(),
	_output(),
	_needs_permute(false),
	_is_prepared(false),
	_is_quantized(false)
	{
	}

	CLDepthwiseConvolutionLayer::~CLDepthwiseConvolutionLayer() = default;

	void CLDepthwiseConvolutionLayer::configure(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 CLDepthwiseConvolutionLayer::configure(const CLCompileContext &compile_context, 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);
	ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayer::validate(input->info(),
	weights->info(),
	biases != nullptr ? biases->info() : nullptr,
	output != nullptr ? output->info() : input->info(),
	conv_info,
	depth_multiplier,
	act_info,
	dilation));
	ARM_COMPUTE_LOG_PARAMS(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);

	_is_quantized = is_data_type_quantized(input->info()->data_type());
	_is_prepared = false;
	_original_weights = weights;
	_input = input;
	_output = output;
	_needs_permute = input->info()->data_layout() == DataLayout::NCHW;

	const GPUTarget gpu_target = CLScheduler::get().target();

	ICLTensor *input_to_use = input;
	const ICLTensor *weights_to_use = weights;
	ICLTensor *output_to_use = output;
	if(_needs_permute)
	{
	_memory_group.manage(&_permuted_input);
	_memory_group.manage(&_permuted_output);

	// Configure the function to transform the input tensor from NCHW -> NHWC
	_permute_input_to_nhwc.configure(compile_context, input, &_permuted_input, PermutationVector(2U, 0U, 1U));
	_permuted_input.info()->set_data_layout(DataLayout::NHWC);

	// Configure the function to transform the weights tensor from IHW -> HWI
	_permute_weights_to_nhwc.configure(compile_context, weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
	_permuted_weights.info()->set_data_layout(DataLayout::NHWC);

	// Set output quantization info before dwc kernel configure
	_permuted_output.info()->set_quantization_info(output->info()->quantization_info());

	input_to_use = &_permuted_input;
	weights_to_use = &_permuted_weights;
	output_to_use = &_permuted_output;
	}

	CLTensor *output_multipliers_to_use = nullptr;
	CLTensor *output_shifts_to_use = nullptr;
	if(_is_quantized)
	{
	const size_t idx_c = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL);
	const size_t num_filters = (is_data_type_quantized_per_channel(weights->info()->data_type())) ? weights->info()->dimension(idx_c) : 1;

	_output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
	_output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));

	output_multipliers_to_use = &_output_multipliers;
	output_shifts_to_use = &_output_shifts;
	}

	// Get the depthwise convolution compute parameters
	auto t = ClDWCNativeKernelConfigurationFactory::create(gpu_target);
	const DWCComputeKernelInfo dwc_native_compute_info = t->configure(input_to_use->info(), weights_to_use->info(), conv_info, dilation, depth_multiplier);

	const ConvolutionInfo conv_kernel_info{ conv_info, depth_multiplier, act_info, dilation };

	_dwc_native_kernel->set_target(gpu_target);
	_dwc_native_kernel->configure(compile_context, input_to_use, weights_to_use, biases, output_to_use,
	dwc_native_compute_info, conv_kernel_info, output_multipliers_to_use, output_shifts_to_use);

	if(_needs_permute)
	{
	_permuted_input.allocator()->allocate();

	// Configure the function to transform the convoluted output to NCHW format
	_permuted_output.info()->set_data_layout(DataLayout::NCHW);
	_permute_output_to_nchw.configure(compile_context, &_permuted_output, output, PermutationVector(1U, 2U, 0U));
	_permuted_output.allocator()->allocate();
	}

	if(_is_quantized)
	{
	_output_multipliers.allocator()->allocate();
	_output_shifts.allocator()->allocate();
	}
	}

	Status CLDepthwiseConvolutionLayer::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_ERROR_ON_NULLPTR(input, weights);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(!weights->are_values_constant(), "Dynamic weights are not supported");

	const bool in_place = input == output \|\| output == nullptr;
	if(in_place)
	{
	output = input;
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
	const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
	const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);

	ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
	ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());

	const GPUTarget gpu_target = CLScheduler::get().target();

	const ConvolutionInfo conv_kernel_info{ conv_info, depth_multiplier, act_info, dilation };

	const bool needs_permute = input->data_layout() == DataLayout::NCHW;

	const bool is_quantized = is_data_type_quantized(input->data_type());

	TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32));
	if(is_quantized)
	{
	if(is_data_type_quantized_per_channel(weights->data_type()))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);

	const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
	output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c)));
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	}
	}

	if(needs_permute)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(in_place, "In-place is supported only with NHWC data layout");
	TensorShape permuted_input_shape = input->tensor_shape();
	TensorShape permuted_weights_shape = weights->tensor_shape();
	const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation };
	TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(input, weights, info);

	permute(permuted_input_shape, PermutationVector(2U, 0U, 1U));
	permute(permuted_weights_shape, PermutationVector(2U, 0U, 1U));
	permute(permuted_output_shape, PermutationVector(2U, 0U, 1U));

	const TensorInfo permuted_input = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC);
	const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC);
	const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NHWC);

	ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U)));
	ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U)));

	// Get the depthwise convolution compute parameters
	auto t = ClDWCNativeKernelConfigurationFactory::create(gpu_target);
	const DWCComputeKernelInfo dwc_native_compute_info = t->configure(&permuted_input, &permuted_weights, conv_info, dilation, depth_multiplier);

	ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output,
	dwc_native_compute_info, conv_kernel_info, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
	ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U)));
	}
	else
	{
	// Get the depthwise convolution compute parameters
	auto t = ClDWCNativeKernelConfigurationFactory::create(gpu_target);
	const DWCComputeKernelInfo dwc_native_compute_info = t->configure(input, weights, conv_info, dilation, depth_multiplier);
	ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_native_compute_info, conv_kernel_info, &output_multipliers_shifts_info,
	&output_multipliers_shifts_info));
	}
	return Status{};
	}

	void CLDepthwiseConvolutionLayer::run()
	{
	prepare();

	MemoryGroupResourceScope scope_mg(_memory_group);

	if(_needs_permute)
	{
	_permute_input_to_nhwc.run();
	}
	CLScheduler::get().enqueue(*_dwc_native_kernel);
	if(_needs_permute)
	{
	_permute_output_to_nchw.run();
	}
	}

	void CLDepthwiseConvolutionLayer::prepare()
	{
	if(!_is_prepared)
	{
	if(_is_quantized)
	{
	_output_multipliers.map();
	_output_shifts.map();
	quantization::compute_quantized_multipliers_and_shifts(_input->info(),
	_original_weights->info(),
	_output != nullptr ? _output->info() : _input->info(),
	reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))),
	reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0))));
	_output_multipliers.unmap();
	_output_shifts.unmap();
	}

	if(_needs_permute)
	{
	ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());

	_permuted_weights.allocator()->allocate();
	_permute_weights_to_nhwc.run();
	_original_weights->mark_as_unused();
	}
	_is_prepared = true;
	}
	}
	} // namespace arm_compute