text: /*
   * Copyright (c) 2019-2022 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 "src/cpu/kernels/meanstddevnorm/generic/neon/impl.h"
  #include "src/core/NEON/wrapper/wrapper.h"
  
  namespace arm_compute
  {
  namespace cpu
  {
  template <typename ScalarType, int size>
  void mean_stddev_normalization(ITensor *input, ITensor *output, float epsilon, const Window &window)
  {
      using ExactTagType = typename wrapper::traits::neon_vector<ScalarType, size>::tag_type;
  
      // Set build options
      Window win = window;
      win.set(Window::DimX, Window::Dimension(0, 1, 1));
  
      const int  window_step_x  = size;
      const auto window_start_x = static_cast<int>(window.x().start());
      const auto window_end_x   = static_cast<int>(window.x().end());
  
      Iterator input_itr(input, win);
      Iterator output_itr(output, win);
  
      execute_window_loop(win, [&](const Coordinates &)
      {
          int  x       = window_start_x;
          auto in_ptr  = reinterpret_cast<const ScalarType *>(input_itr.ptr());
          auto out_ptr = reinterpret_cast<ScalarType *>(output_itr.ptr());
  
          auto sum_vec    = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{});
          auto sum_sq_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{});
  
          for(; x <= (window_end_x - window_step_x); x += window_step_x)
          {
              auto data  = wrapper::vloadq(in_ptr + x);
              sum_vec    = wrapper::vadd(sum_vec, data);
              sum_sq_vec = wrapper::vadd(sum_sq_vec, wrapper::vmul(data, data));
          }
  
          auto sum_carry_res    = wrapper::vpadd(wrapper::vgethigh(sum_vec), wrapper::vgetlow(sum_vec));
          auto sum_sq_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_sq_vec), wrapper::vgetlow(sum_sq_vec));
          for(int i = 0; i < size / 4; ++i)
          {
              sum_carry_res    = wrapper::vpadd(sum_carry_res, sum_carry_res);
              sum_sq_carry_res = wrapper::vpadd(sum_sq_carry_res, sum_sq_carry_res);
          }
  
          auto sum    = wrapper::vgetlane(sum_carry_res, 0);
          auto sum_sq = wrapper::vgetlane(sum_sq_carry_res, 0);
  
          // Compute left-over elements
          for(; x < window_end_x; ++x)
          {
              ScalarType data = *(in_ptr + x);
              sum += data;
              sum_sq += data * data;
          }
  
          ScalarType mean       = sum / input->info()->dimension(0);
          ScalarType var        = (sum_sq / input->info()->dimension(0)) - (mean * mean);
          ScalarType stddev_inv = 1.f / sqrt(var + epsilon);
  
          auto mean_vec       = wrapper::vdup_n(mean, ExactTagType{});
          auto stddev_inv_vec = wrapper::vdup_n(stddev_inv, ExactTagType{});
          for(x = window_start_x; x <= (window_end_x - window_step_x); x += window_step_x)
          {
              auto data = wrapper::vloadq(in_ptr + x);
              auto res  = wrapper::vmul(wrapper::vsub(data, mean_vec), stddev_inv_vec);
              // Store results
              wrapper::vstore(out_ptr + x, res);
          }
          for(; x < window_end_x; ++x)
          {
              *(out_ptr + x) = (*(in_ptr + x) - mean) * stddev_inv;
          }
      },
      input_itr, output_itr);
  }
  template void mean_stddev_normalization<float, 4>(ITensor *input, ITensor *output, float epsilon, const Window &window);
  
  #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
  template void mean_stddev_normalization<float16_t, 8>(ITensor *input, ITensor *output, float epsilon, const Window &window);
  #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
  
  } // namespace cpu
  } // namespace arm_compute
  