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

 /*
  * Copyright (c) 2017-2020 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/core/CL/kernels/CLQuantizationLayerKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F32, DataType::F16);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);

     // Output must always be initialized
     ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM16);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
 {
     // Configure kernel window
     Window win = calculate_max_window(*input, Steps());

     const int  vec_size_x     = 16 / input->element_size();
     const int  input_width_x  = input->tensor_shape().x();
     const bool multi_access_x = (input_width_x / vec_size_x > 0);
     if(multi_access_x)
     {
         win.set(Window::DimX, Window::Dimension(win.x().start(), ceil_to_multiple(win.x().end(), vec_size_x), vec_size_x));
     }

     Coordinates coord;
     coord.set_num_dimensions(output->num_dimensions());
     output->set_valid_region(ValidRegion(coord, output->tensor_shape()));

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

 CLQuantizationLayerKernel::CLQuantizationLayerKernel()
     : _input(nullptr), _output(nullptr)
 {
 }

 void CLQuantizationLayerKernel::configure(const ICLTensor *input, ICLTensor *output)
 {
     configure(CLKernelLibrary::get().get_compile_context(), input, output);
 }

 void CLQuantizationLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));

     _input  = input;
     _output = output;

     const int  vec_size_x     = 16 / input->info()->element_size();
     const int  input_width_x  = input->info()->tensor_shape().x();
     const bool multi_access_x = (input_width_x / vec_size_x > 0);

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), output->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);

     const UniformQuantizationInfo qinfo            = output->info()->quantization_info().uniform();
     const DataType                output_data_type = output->info()->data_type();

     float   scale_to_apply  = qinfo.scale;
     int32_t offset_to_apply = qinfo.offset;
     if(is_data_type_quantized_asymmetric(_input->info()->data_type()))
     {
         /*
          * In case of requantization of a quantized input tensor to an output tensor with another quantization
          * instead of of apply dequantization and then a quantization functions, we just compute new scale and
          * offset to apply.
          *
          * Assuming:
          *   - q_i as input quantized value
          *   - q_o as output quantized value
          *   - z_i as input quantization offset value
          *   - z_o as output quantization offset value
          *   - s_i as input quantization scale value
          *   - s_o as output quantization scale value
          *   - z_n as new quantization offset value
          *   - s_n as new quantization scale value
          *
          * q_o = ( q_i - z_i ) * s_i / s_o + z_o
          *
          * We can rewrite the formula as:
          *
          * q_o = ( q_i * s_i / s_o ) - z_i * s_i / s_o + z_o
          *
          * q_o = q_i / s_n + z_n
          *
          * Where:
          *
          * s_n = s_o / s_i
          *
          * z_n = - z_i * s_i / s_o + z_o
          *
          */
         const UniformQuantizationInfo qinfo_in = _input->info()->quantization_info().uniform();
         scale_to_apply /= qinfo_in.scale;
         // In order to minimize flooring we convert the offset to a float,
         // then compute the new offset in the float domain,
         // finally we convert it back as int32_t
         offset_to_apply -= static_cast<int32_t>(static_cast<float>(qinfo_in.offset) * qinfo_in.scale / qinfo.scale);
     }

     // Create kernel
     CLBuildOptions build_opts;
     build_opts.add_option_if(is_data_type_float(_input->info()->data_type()), "-DIS_FLOAT");
     build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(scale_to_apply));
     build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(offset_to_apply));
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x));
     build_opts.add_option("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input->info()->data_type()));
     build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output_data_type));
     build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max<int>(input_width_x - vec_size_x, 0)));
     std::pair<int, int> min_max_quant_values = quantization::get_min_max_values_from_quantized_data_type(output_data_type);
     build_opts.add_option("-DMIN_QUANT_VAL=" + support::cpp11::to_string(min_max_quant_values.first));
     build_opts.add_option("-DMAX_QUANT_VAL=" + support::cpp11::to_string(min_max_quant_values.second));

     _kernel = create_kernel(compile_context, "quantization_layer", build_opts.options());
 }

 Status CLQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);

     return Status{};
 }

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

     Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), 3);
     Window slice            = window_collapsed.first_slice_window_3D();

     do
     {
         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_collapsed.slide_window_slice_3D(slice));
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2020 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/core/CL/kernels/CLQuantizationLayerKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F32, DataType::F16);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);

	// Output must always be initialized
	ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape().total_size() == 0);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM16);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo output)
	{
	// Configure kernel window
	Window win = calculate_max_window(*input, Steps());

	const int vec_size_x = 16 / input->element_size();
	const int input_width_x = input->tensor_shape().x();
	const bool multi_access_x = (input_width_x / vec_size_x > 0);
	if(multi_access_x)
	{
	win.set(Window::DimX, Window::Dimension(win.x().start(), ceil_to_multiple(win.x().end(), vec_size_x), vec_size_x));
	}

	Coordinates coord;
	coord.set_num_dimensions(output->num_dimensions());
	output->set_valid_region(ValidRegion(coord, output->tensor_shape()));

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

	CLQuantizationLayerKernel::CLQuantizationLayerKernel()
	: _input(nullptr), _output(nullptr)
	{
	}

	void CLQuantizationLayerKernel::configure(const ICLTensor input, ICLTensor output)
	{
	configure(CLKernelLibrary::get().get_compile_context(), input, output);
	}

	void CLQuantizationLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor input, ICLTensor output)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));

	_input = input;
	_output = output;

	const int vec_size_x = 16 / input->info()->element_size();
	const int input_width_x = input->info()->tensor_shape().x();
	const bool multi_access_x = (input_width_x / vec_size_x > 0);

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), output->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	ICLKernel::configure_internal(win_config.second);

	const UniformQuantizationInfo qinfo = output->info()->quantization_info().uniform();
	const DataType output_data_type = output->info()->data_type();

	float scale_to_apply = qinfo.scale;
	int32_t offset_to_apply = qinfo.offset;
	if(is_data_type_quantized_asymmetric(_input->info()->data_type()))
	{
	/*
	* In case of requantization of a quantized input tensor to an output tensor with another quantization
	* instead of of apply dequantization and then a quantization functions, we just compute new scale and
	* offset to apply.
	*
	* Assuming:
	* - q_i as input quantized value
	* - q_o as output quantized value
	* - z_i as input quantization offset value
	* - z_o as output quantization offset value
	* - s_i as input quantization scale value
	* - s_o as output quantization scale value
	* - z_n as new quantization offset value
	* - s_n as new quantization scale value
	*
	* q_o = ( q_i - z_i ) * s_i / s_o + z_o
	*
	* We can rewrite the formula as:
	*
	* q_o = ( q_i * s_i / s_o ) - z_i * s_i / s_o + z_o
	*
	* q_o = q_i / s_n + z_n
	*
	* Where:
	*
	* s_n = s_o / s_i
	*
	* z_n = - z_i * s_i / s_o + z_o
	*
	*/
	const UniformQuantizationInfo qinfo_in = _input->info()->quantization_info().uniform();
	scale_to_apply /= qinfo_in.scale;
	// In order to minimize flooring we convert the offset to a float,
	// then compute the new offset in the float domain,
	// finally we convert it back as int32_t
	offset_to_apply -= static_cast<int32_t>(static_cast<float>(qinfo_in.offset) * qinfo_in.scale / qinfo.scale);
	}

	// Create kernel
	CLBuildOptions build_opts;
	build_opts.add_option_if(is_data_type_float(_input->info()->data_type()), "-DIS_FLOAT");
	build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(scale_to_apply));
	build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(offset_to_apply));
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x));
	build_opts.add_option("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input->info()->data_type()));
	build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output_data_type));
	build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max<int>(input_width_x - vec_size_x, 0)));
	std::pair<int, int> min_max_quant_values = quantization::get_min_max_values_from_quantized_data_type(output_data_type);
	build_opts.add_option("-DMIN_QUANT_VAL=" + support::cpp11::to_string(min_max_quant_values.first));
	build_opts.add_option("-DMAX_QUANT_VAL=" + support::cpp11::to_string(min_max_quant_values.second));

	_kernel = create_kernel(compile_context, "quantization_layer", build_opts.options());
	}

	Status CLQuantizationLayerKernel::validate(const ITensorInfo input, const ITensorInfo output)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);

	return Status{};
	}

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

	Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), 3);
	Window slice = window_collapsed.first_slice_window_3D();

	do
	{
	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_collapsed.slide_window_slice_3D(slice));
	}
	} // namespace arm_compute