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

 /*
  * Copyright (c) 2018 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/CLDirectConvolutionLayerOutputStageKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLValidate.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Window.h"

 #include <cstddef>
 #include <cstdint>

 using namespace arm_compute;

 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16,
                                                          DataType::F32);

     if(bias != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(bias);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32, DataType::F16, DataType::F32);

         if(is_data_type_quantized_asymmetric(input->data_type()))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
         }

         ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
     }
     else
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_float(input->data_type()),
                                         "Calling output stage kernel with floating point arguments");
     }

     // Checks performed on output
     if(input->data_type() == DataType::S32)
     {
         // Quantized configuration checks
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
     }
     else
     {
         // In case of out-of-place computation (supported for non-quantized configurations)
         if((output != nullptr) && (output->total_size() != 0))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         }
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
 {
     bool         window_changed                    = false;
     unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->data_type());

     // Update processed elements when input is S32 (comes from quantization input)
     if(input->data_type() == DataType::S32)
     {
         num_elems_processed_per_iteration = 16;
     }

     // Configure kernel window
     Window                 win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
     AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

     if(output != nullptr && (output->total_size() != 0))
     {
         AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

         if(bias == nullptr)
         {
             window_changed = update_window_and_padding(win, input_access, output_access);
         }
         else
         {
             AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
             window_changed = update_window_and_padding(win, input_access, output_access, bias_access);
         }

         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
     }
     else
     {
         if(bias == nullptr)
         {
             window_changed = update_window_and_padding(win, input_access);
         }
         else
         {
             AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
             window_changed = update_window_and_padding(win, input_access, bias_access);
         }

         input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
     }

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

 CLDirectConvolutionLayerOutputStageKernel::CLDirectConvolutionLayerOutputStageKernel()
     : _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0)
 {
 }

 void CLDirectConvolutionLayerOutputStageKernel::configure(ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
                                                           int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input);

     // Auto-initialize output if required
     if(output != nullptr)
     {
         // Work out expected output data type
         const DataType output_dt = (input->info()->data_type() == DataType::S32) ? DataType::QASYMM8 : input->info()->data_type();
         // Output tensor auto initialization if not yet initialized
         auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_dt));
     }

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info()));

     _bias                         = bias;
     _input                        = input;
     _output                       = output;
     _result_fixedpoint_multiplier = result_fixedpoint_multiplier;
     _result_shift                 = result_shift;
     _result_offset_after_shift    = result_offset_after_shift;

     // Create kernel
     CLBuildOptions build_opts;
     build_opts.add_option_if(bias != nullptr, "-DHAS_BIAS");
     build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout()));
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("output_stage_quantized", build_opts.options()));

     // Set static kernel arguments
     int idx = 2 * num_arguments_per_3D_tensor() + ((bias != nullptr) ? num_arguments_per_1D_tensor() : 0);
     _kernel.setArg<int>(idx++, _result_offset_after_shift);
     _kernel.setArg<int>(idx++, _result_fixedpoint_multiplier);
     _kernel.setArg<int>(idx++, _result_shift);

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure(win_config.second);
 }

 Status CLDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), bias->clone().get(), output == nullptr ? nullptr : output->clone().get()).first);

     return Status{};
 }

 void CLDirectConvolutionLayerOutputStageKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

     Window slice = window.first_slice_window_3D();

     // Set bias vector
     if(_bias != nullptr)
     {
         unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
         Window       slice_biases;
         slice_biases.use_tensor_dimensions(_bias->info()->tensor_shape());
         add_1D_tensor_argument(idx1, _bias, slice_biases);
     }

     // Run kernel
     do
     {
         // Set arguments
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, slice);
         add_3D_tensor_argument(idx, _output, slice);
         enqueue(queue, *this, slice, _lws_hint);
     }
     while(window.slide_window_slice_3D(slice));
 }
	/*
	* Copyright (c) 2018 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/CLDirectConvolutionLayerOutputStageKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/CL/CLValidate.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Window.h"

	#include <cstddef>
	#include <cstdint>

	using namespace arm_compute;

	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16,
	DataType::F32);

	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(bias);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32, DataType::F16, DataType::F32);

	if(is_data_type_quantized_asymmetric(input->data_type()))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
	}

	ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_float(input->data_type()),
	"Calling output stage kernel with floating point arguments");
	}

	// Checks performed on output
	if(input->data_type() == DataType::S32)
	{
	// Quantized configuration checks
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
	}
	else
	{
	// In case of out-of-place computation (supported for non-quantized configurations)
	if((output != nullptr) && (output->total_size() != 0))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo bias, ITensorInfo *output)
	{
	bool window_changed = false;
	unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->data_type());

	// Update processed elements when input is S32 (comes from quantization input)
	if(input->data_type() == DataType::S32)
	{
	num_elems_processed_per_iteration = 16;
	}

	// Configure kernel window
	Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
	AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

	if(output != nullptr && (output->total_size() != 0))
	{
	AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

	if(bias == nullptr)
	{
	window_changed = update_window_and_padding(win, input_access, output_access);
	}
	else
	{
	AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
	window_changed = update_window_and_padding(win, input_access, output_access, bias_access);
	}

	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	else
	{
	if(bias == nullptr)
	{
	window_changed = update_window_and_padding(win, input_access);
	}
	else
	{
	AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
	window_changed = update_window_and_padding(win, input_access, bias_access);
	}

	input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
	}

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

	CLDirectConvolutionLayerOutputStageKernel::CLDirectConvolutionLayerOutputStageKernel()
	: _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0)
	{
	}

	void CLDirectConvolutionLayerOutputStageKernel::configure(ICLTensor input, const ICLTensor bias, ICLTensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input);

	// Auto-initialize output if required
	if(output != nullptr)
	{
	// Work out expected output data type
	const DataType output_dt = (input->info()->data_type() == DataType::S32) ? DataType::QASYMM8 : input->info()->data_type();
	// Output tensor auto initialization if not yet initialized
	auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_dt));
	}

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info()));

	_bias = bias;
	_input = input;
	_output = output;
	_result_fixedpoint_multiplier = result_fixedpoint_multiplier;
	_result_shift = result_shift;
	_result_offset_after_shift = result_offset_after_shift;

	// Create kernel
	CLBuildOptions build_opts;
	build_opts.add_option_if(bias != nullptr, "-DHAS_BIAS");
	build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout()));
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("output_stage_quantized", build_opts.options()));

	// Set static kernel arguments
	int idx = 2 * num_arguments_per_3D_tensor() + ((bias != nullptr) ? num_arguments_per_1D_tensor() : 0);
	_kernel.setArg<int>(idx++, _result_offset_after_shift);
	_kernel.setArg<int>(idx++, _result_fixedpoint_multiplier);
	_kernel.setArg<int>(idx++, _result_shift);

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	ICLKernel::configure(win_config.second);
	}

	Status CLDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), bias->clone().get(), output == nullptr ? nullptr : output->clone().get()).first);

	return Status{};
	}

	void CLDirectConvolutionLayerOutputStageKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

	Window slice = window.first_slice_window_3D();

	// Set bias vector
	if(_bias != nullptr)
	{
	unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
	Window slice_biases;
	slice_biases.use_tensor_dimensions(_bias->info()->tensor_shape());
	add_1D_tensor_argument(idx1, _bias, slice_biases);
	}

	// Run kernel
	do
	{
	// Set arguments
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, slice);
	add_3D_tensor_argument(idx, _output, slice);
	enqueue(queue, *this, slice, _lws_hint);
	}
	while(window.slide_window_slice_3D(slice));
	}