text: /*
   * Copyright (c) 2019 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/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.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(max > 127);
      ARM_COMPUTE_RETURN_ERROR_ON(min < -128 || 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_SIGNED);
          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 initialization if not yet initialized
      auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8_SIGNED));
  
      // Configure kernel window
      Window win = calculate_max_window(*input, Steps());
  
      // NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel 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
  
  template <bool is_bounded_relu>
  void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run(const Window &window)
  {
      const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift);
      const int8x16_t min_s8                        = vdupq_n_s8(static_cast<int8_t>(_min));
      const int8x16_t max_s8                        = vdupq_n_s8(static_cast<int8_t>(_max));
  
      ARM_COMPUTE_UNUSED(min_s8, max_s8);
  
      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_s8(reinterpret_cast<int8_t *>(out.ptr() + x),
                           finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_s8, max_s8));
              }
  
              // 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
                  *reinterpret_cast<int8_t *>(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift,
                                                                                                      static_cast<int8_t>(_min), static_cast<int8_t>(_max));
              }
          },
          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_s8(reinterpret_cast<int8_t *>(out.ptr() + x),
                           finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_s8, max_s8));
              }
  
              // 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
                  *reinterpret_cast<int8_t *>(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift,
                                                                                                      static_cast<int8_t>(_min), static_cast<int8_t>(_max));
              }
          },
          in, out);
      }
  }
  
  NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel()
      : _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 NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::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 != max) && !(min == -128 && max == 127));
      _func                      = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run<false>;
  }
  
  Status NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::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 NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::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