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

 /*
  * Copyright (c) 2018-2020 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/CLArgMinMaxLayer.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/runtime/Utils.h"

 namespace arm_compute
 {
 CLArgMinMaxLayer::CLArgMinMaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _results_vector(), _not_reshaped_output(), _reduction_kernels_vector(), _reshape(), _num_of_stages(), _reduction_axis()
 {
 }

 Status CLArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid reduction operation");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= static_cast<int>(TensorShape::num_max_dimensions), "Reduction axis greater than max number of dimensions");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
     const unsigned int num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->dimension(0), axis);

     DataType   output_data_type = DataType::S32;
     TensorInfo not_reshaped_output;
     const auto input_num_channles = input->num_channels();
     const auto input_qinfo        = input->quantization_info();

     if(output->total_size() != 0)
     {
         output_data_type                       = output->data_type();
         const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, false));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output);
     }

     auto shape_before_reshape = input->tensor_shape();
     shape_before_reshape.set(axis, 1);
     auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo)
     {
         ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo);
     };

     initialize_tensorinfo(not_reshaped_output, shape_before_reshape, output_data_type, input_num_channles, input_qinfo);

     if(num_of_stages == 1)
     {
         ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &not_reshaped_output, axis, op));
     }
     else
     {
         // Create temporary tensor infos
         std::vector<TensorInfo> sums_vector(num_of_stages - 1);

         // Create intermediate tensor info
         TensorShape shape{ input->tensor_shape() };

         for(unsigned int i = 0; i < num_of_stages - 1; i++)
         {
             shape.set(0, ceil(shape.x() / 128.f));
             sums_vector[i].set_data_type(input->data_type());
             sums_vector[i].set_tensor_shape(shape);
             sums_vector[i].set_num_channels(input->num_channels());
         }

         // Validate ReductionOperation only on first kernel
         ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &sums_vector[0], axis, op));

         // Validate ReductionOperation on intermediate stages
         for(unsigned int i = 1; i < num_of_stages - 1; ++i)
         {
             ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[i - 1], &sums_vector[i], axis, op));
         }

         // Validate ReductionOperation on the last stage
         const unsigned int last_stage = num_of_stages - 1;
         ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[last_stage - 1], &not_reshaped_output, axis, op));
     }
     ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&not_reshaped_output, output));
     return Status{};
 }

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

 void CLArgMinMaxLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     _num_of_stages  = utils::calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis);
     _reduction_axis = axis;

     const TensorShape output_shape     = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false);
     DataType          output_data_type = (output->info()->data_type() == DataType::UNKNOWN) ? DataType::S32 : output->info()->data_type();
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));

     // Configure reduction operation kernels
     _reduction_kernels_vector.resize(_num_of_stages);

     _memory_group.manage(&_not_reshaped_output);
     // Create temporary tensors
     if(_num_of_stages == 1)
     {
         _reduction_kernels_vector[0].configure(compile_context, input, nullptr, &_not_reshaped_output, axis, op);
     }
     else
     {
         _results_vector.resize(_num_of_stages - 1);
         TensorShape shape{ input->info()->tensor_shape() };
         for(unsigned int i = 0; i < _num_of_stages - 1; i++)
         {
             shape.set(0, ceil(shape.x() / 128.f));
             _results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape).set_data_type(output_data_type));
         }

         // Apply ReductionOperation only on first kernel
         _memory_group.manage(&_results_vector[0]);
         _reduction_kernels_vector[0].configure(compile_context, input, nullptr, &_results_vector[0], axis, op);

         // Apply ReductionOperation on intermediate stages
         for(unsigned int i = 1; i < _num_of_stages - 1; ++i)
         {
             _memory_group.manage(&_results_vector[i]);
             _reduction_kernels_vector[i].configure(compile_context, input, &_results_vector[i - 1], &_results_vector[i], axis, op);
             _results_vector[i - 1].allocator()->allocate();
         }

         // Apply ReductionOperation on the last stage
         const unsigned int last_stage = _num_of_stages - 1;
         _reduction_kernels_vector[last_stage].configure(compile_context, input, &_results_vector[last_stage - 1], &_not_reshaped_output, axis, op);
         _results_vector[last_stage - 1].allocator()->allocate();
     }
     _reshape.configure(compile_context, &_not_reshaped_output, output);
     _not_reshaped_output.allocator()->allocate();
 }

 void CLArgMinMaxLayer::run()
 {
     MemoryGroupResourceScope scope_mg(_memory_group);

     for(unsigned int i = 0; i < _num_of_stages; ++i)
     {
         CLScheduler::get().enqueue(_reduction_kernels_vector[i], false);
     }
     _reshape.run();
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2020 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/CLArgMinMaxLayer.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/runtime/Utils.h"

	namespace arm_compute
	{
	CLArgMinMaxLayer::CLArgMinMaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _results_vector(), _not_reshaped_output(), _reduction_kernels_vector(), _reshape(), _num_of_stages(), _reduction_axis()
	{
	}

	Status CLArgMinMaxLayer::validate(const ITensorInfo input, int axis, const ITensorInfo output, const ReductionOperation &op)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid reduction operation");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= static_cast<int>(TensorShape::num_max_dimensions), "Reduction axis greater than max number of dimensions");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
	const unsigned int num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->dimension(0), axis);

	DataType output_data_type = DataType::S32;
	TensorInfo not_reshaped_output;
	const auto input_num_channles = input->num_channels();
	const auto input_qinfo = input->quantization_info();

	if(output->total_size() != 0)
	{
	output_data_type = output->data_type();
	const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, false));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output);
	}

	auto shape_before_reshape = input->tensor_shape();
	shape_before_reshape.set(axis, 1);
	auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo)
	{
	ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo);
	};

	initialize_tensorinfo(not_reshaped_output, shape_before_reshape, output_data_type, input_num_channles, input_qinfo);

	if(num_of_stages == 1)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &not_reshaped_output, axis, op));
	}
	else
	{
	// Create temporary tensor infos
	std::vector<TensorInfo> sums_vector(num_of_stages - 1);

	// Create intermediate tensor info
	TensorShape shape{ input->tensor_shape() };

	for(unsigned int i = 0; i < num_of_stages - 1; i++)
	{
	shape.set(0, ceil(shape.x() / 128.f));
	sums_vector[i].set_data_type(input->data_type());
	sums_vector[i].set_tensor_shape(shape);
	sums_vector[i].set_num_channels(input->num_channels());
	}

	// Validate ReductionOperation only on first kernel
	ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &sums_vector[0], axis, op));

	// Validate ReductionOperation on intermediate stages
	for(unsigned int i = 1; i < num_of_stages - 1; ++i)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[i - 1], &sums_vector[i], axis, op));
	}

	// Validate ReductionOperation on the last stage
	const unsigned int last_stage = num_of_stages - 1;
	ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[last_stage - 1], &not_reshaped_output, axis, op));
	}
	ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&not_reshaped_output, output));
	return Status{};
	}

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

	void CLArgMinMaxLayer::configure(const CLCompileContext &compile_context, const ICLTensor input, int axis, ICLTensor output, const ReductionOperation &op)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	_num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis);
	_reduction_axis = axis;

	const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false);
	DataType output_data_type = (output->info()->data_type() == DataType::UNKNOWN) ? DataType::S32 : output->info()->data_type();
	auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));

	// Configure reduction operation kernels
	_reduction_kernels_vector.resize(_num_of_stages);

	_memory_group.manage(&_not_reshaped_output);
	// Create temporary tensors
	if(_num_of_stages == 1)
	{
	_reduction_kernels_vector[0].configure(compile_context, input, nullptr, &_not_reshaped_output, axis, op);
	}
	else
	{
	_results_vector.resize(_num_of_stages - 1);
	TensorShape shape{ input->info()->tensor_shape() };
	for(unsigned int i = 0; i < _num_of_stages - 1; i++)
	{
	shape.set(0, ceil(shape.x() / 128.f));
	_results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape).set_data_type(output_data_type));
	}

	// Apply ReductionOperation only on first kernel
	_memory_group.manage(&_results_vector[0]);
	_reduction_kernels_vector[0].configure(compile_context, input, nullptr, &_results_vector[0], axis, op);

	// Apply ReductionOperation on intermediate stages
	for(unsigned int i = 1; i < _num_of_stages - 1; ++i)
	{
	_memory_group.manage(&_results_vector[i]);
	_reduction_kernels_vector[i].configure(compile_context, input, &_results_vector[i - 1], &_results_vector[i], axis, op);
	_results_vector[i - 1].allocator()->allocate();
	}

	// Apply ReductionOperation on the last stage
	const unsigned int last_stage = _num_of_stages - 1;
	_reduction_kernels_vector[last_stage].configure(compile_context, input, &_results_vector[last_stage - 1], &_not_reshaped_output, axis, op);
	_results_vector[last_stage - 1].allocator()->allocate();
	}
	_reshape.configure(compile_context, &_not_reshaped_output, output);
	_not_reshaped_output.allocator()->allocate();
	}

	void CLArgMinMaxLayer::run()
	{
	MemoryGroupResourceScope scope_mg(_memory_group);

	for(unsigned int i = 0; i < _num_of_stages; ++i)
	{
	CLScheduler::get().enqueue(_reduction_kernels_vector[i], false);
	}
	_reshape.run();
	}
	} // namespace arm_compute