src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.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/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/NEON/NEAsymm.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"

 #include <arm_neon.h>
 #include <cstddef>
 #include <cstdint>

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
     ARM_COMPUTE_RETURN_ERROR_ON(min > max);

     // Check biases if exist
     if(bias != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
         ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
     }

     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input);
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
 {
     // Output auto inizialitation if not yet initialized
     auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8));

     // Configure kernel window
     Window win = calculate_max_window(*input, Steps());

     // NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel doesn't need padding so update_window_and_padding() can be skipped
     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

 namespace arm_compute
 {
 class Coordinates;
 } // namespace arm_compute

 template <bool is_bounded_relu>
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window)
 {
     const int32x4_t  result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift);
     const uint8x16_t min_u8                        = vdupq_n_u8(static_cast<uint8_t>(_min));
     const uint8x16_t max_u8                        = vdupq_n_u8(static_cast<uint8_t>(_max));

     ARM_COMPUTE_UNUSED(min_u8);
     ARM_COMPUTE_UNUSED(max_u8);

     const int  window_step_x  = 16;
     const auto window_start_x = static_cast<int>(window.x().start());
     const auto window_end_x   = static_cast<int>(window.x().end());

     Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
     win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

     Iterator in(_input, win_collapsed);
     Iterator out(_output, win_collapsed);
     if(_bias != nullptr)
     {
         Window win_biases;
         win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
         win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));

         Iterator bias(_bias, win_biases);
         execute_window_loop(win_collapsed, [&](const Coordinates &)
         {
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
                     }
                 };

                 const int32x4x4_t bias_s32 =
                 {
                     {
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
                         vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
                     }
                 };

                 // Add the bias to GEMM's result
                 in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
                 in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);

                 vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8, is_bounded_relu));
             }

             // Compute left-over elements
             for(; x < window_end_x; ++x)
             {
                 const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x);
                 int32_t       in_value   = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);

                 // Add bias
                 in_value += bias_value;
                 // Finalize and store the result
                 *(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max), is_bounded_relu);
             }
         },
         in, out, bias);
     }
     else
     {
         execute_window_loop(win_collapsed, [&](const Coordinates &)
         {
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
             {
                 int32x4x4_t in_s32 =
                 {
                     {
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
                         vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
                     }
                 };

                 vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8, is_bounded_relu));
             }

             // Compute left-over elements
             for(; x < window_end_x; ++x)
             {
                 const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);

                 // Finalize and store the result
                 *(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max), is_bounded_relu);
             }
         },
         in, out);
     }
 }

 NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel()
     : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0)
 {
 }

 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
                                                                           int result_offset_after_shift, int min, int max)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), min, max));

     _input                        = input;
     _bias                         = bias;
     _output                       = output;
     _result_fixedpoint_multiplier = result_fixedpoint_multiplier;
     _result_shift                 = result_shift;
     _result_offset_after_shift    = result_offset_after_shift;
     _min                          = min;
     _max                          = max;

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

     // Check if we need to clamp the result using min and max
     const bool is_bounded_relu = !(min <= 0 && max >= 255);
     _func                      = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>;
 }

 Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);

     return Status{};
 }

 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

     (this->*_func)(window);
 }
 } // 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/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/NEON/NEAsymm.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"

	#include <arm_neon.h>
	#include <cstddef>
	#include <cstdint>

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output, int min, int max)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
	ARM_COMPUTE_RETURN_ERROR_ON(min > max);

	// Check biases if exist
	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
	ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
	ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
	}

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input);
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo output)
	{
	// Output auto inizialitation if not yet initialized
	auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8));

	// Configure kernel window
	Window win = calculate_max_window(*input, Steps());

	// NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel doesn't need padding so update_window_and_padding() can be skipped
	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

	namespace arm_compute
	{
	class Coordinates;
	} // namespace arm_compute

	template <bool is_bounded_relu>
	void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window)
	{
	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift);
	const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(_min));
	const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(_max));

	ARM_COMPUTE_UNUSED(min_u8);
	ARM_COMPUTE_UNUSED(max_u8);

	const int window_step_x = 16;
	const auto window_start_x = static_cast<int>(window.x().start());
	const auto window_end_x = static_cast<int>(window.x().end());

	Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
	win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));

	Iterator in(_input, win_collapsed);
	Iterator out(_output, win_collapsed);
	if(_bias != nullptr)
	{
	Window win_biases;
	win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
	win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));

	Iterator bias(_bias, win_biases);
	execute_window_loop(win_collapsed, [&](const Coordinates &)
	{
	// Compute 16 elements per iteration
	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
	}
	};

	const int32x4x4_t bias_s32 =
	{
	{
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
	vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
	}
	};

	// Add the bias to GEMM's result
	in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
	in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
	in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
	in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);

	vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8, is_bounded_relu));
	}

	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	const int32_t bias_value = (reinterpret_cast<const int32_t >(bias.ptr()) + x);
	int32_t in_value = (reinterpret_cast<const int32_t >(in.ptr()) + x);

	// Add bias
	in_value += bias_value;
	// Finalize and store the result
	*(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max), is_bounded_relu);
	}
	},
	in, out, bias);
	}
	else
	{
	execute_window_loop(win_collapsed, [&](const Coordinates &)
	{
	// Compute 16 elements per iteration
	int x = window_start_x;
	for(; x <= (window_end_x - window_step_x); x += window_step_x)
	{
	int32x4x4_t in_s32 =
	{
	{
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
	vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
	}
	};

	vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8, is_bounded_relu));
	}

	// Compute left-over elements
	for(; x < window_end_x; ++x)
	{
	const int32_t in_value = (reinterpret_cast<const int32_t >(in.ptr()) + x);

	// Finalize and store the result
	*(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max), is_bounded_relu);
	}
	},
	in, out);
	}
	}

	NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel()
	: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0)
	{
	}

	void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor input, const ITensor bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
	int result_offset_after_shift, int min, int max)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), min, max));

	_input = input;
	_bias = bias;
	_output = output;
	_result_fixedpoint_multiplier = result_fixedpoint_multiplier;
	_result_shift = result_shift;
	_result_offset_after_shift = result_offset_after_shift;
	_min = min;
	_max = max;

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

	// Check if we need to clamp the result using min and max
	const bool is_bounded_relu = !(min <= 0 && max >= 255);
	_func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>;
	}

	Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output, int min, int max)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);

	return Status{};
	}

	void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

	(this->*_func)(window);
	}
	} // namespace arm_compute