text: /*******************************************************************************
   * Copyright (c) 2015-2018 Skymind, Inc.
   *
   * This program and the accompanying materials are made available under the
   * terms of the Apache License, Version 2.0 which is available at
   * https://www.apache.org/licenses/LICENSE-2.0.
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
   * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
   * License for the specific language governing permissions and limitations
   * under the License.
   *
   * SPDX-License-Identifier: Apache-2.0
   ******************************************************************************/
  
  //
  //  @author sgazeos@gmail.com
  //
  
  #include <ops/declarable/helpers/fake_quantization.h>
  #include <array/NDArrayFactory.h>
  
  namespace sd {
  namespace ops {
  namespace helpers {
  
      //
      // nudge - nudged min max over scale
      // scale = (Max - Min) / (quantMax - quantMin)
      // quantMin = 0 or 1, quantMax = 2^b - 1 == (1 << b) - 1
      //
      template <typename T>
      static void nudge(T min, T max, int quantMin, int quantMax, T* scale, T* nudgedMin, T* nudgedMax) {
          // floating point instead integers
          T quantMaxF = static_cast<T>(quantMax);
          T quantMinF = static_cast<T>(quantMin);
          // compute scale
          *scale = (max - min) / (quantMaxF - quantMinF);
          // compute left bound point
          auto zeroPointFromMin = quantMinF - min / *scale;
          // bound zero point to conform with range [0 or 1, 2^b - 1]
          uint16_t const nudged_zero_point = [zeroPointFromMin, quantMin, quantMax, quantMaxF, quantMinF] {
                  if (zeroPointFromMin < quantMinF) {
                      return static_cast<uint16_t>(quantMin);
                  }
                  if (zeroPointFromMin > quantMaxF) {
                      return static_cast<uint16_t>(quantMax);
                  }
                  return (uint16_t)sd::math::nd4j_round<T,int>(zeroPointFromMin);
          }();
          // compute nudged min and max with computed nudged zero point
          *nudgedMin = (quantMinF - nudged_zero_point) * (*scale);
          *nudgedMax = (quantMaxF - nudged_zero_point) * (*scale);
      }
  
      template <typename T>
      void fakeQuantWithMinMaxVarsPerChannel_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
          int lowIntBound = narrowed ? 1 : 0; // 0 or 1
          int upperIntBound = (1 << numBits) - 1; // 2^b - 1
          auto channels = input->sizeAt(-1); // last dimension
  
          PRAGMA_OMP_PARALLEL_FOR
          for (auto i = 0; i < channels; i++) {
              T scale, nudged_min, nudged_max;
              // nudge min and max first, with scale computing
              nudge<T>(min->t<T>(i), max->t<T>(i), lowIntBound, upperIntBound, &scale, &nudged_min, &nudged_max);
              // slide using last dimension and process all for given channel
              for (auto e = 0; e < input->lengthOf(); e += channels) {
                  T val = input->t<T>(e + i);
                  if ( val <= nudged_min)
                      val = nudged_min;
                  else if (val >= nudged_max)
                      val = nudged_max;
                  // quantization itself
                  output->t<T>(e + i) = math::nd4j_floor<T,T>((val - nudged_min)/scale + T(0.5)) * scale + nudged_min;
              }
          }
      }
  //
  //const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
  //    const auto clamped_shifted = clamped - nudged_min;
  //    outputs.device(d) = (clamped_shifted / nudged_scale_repl + 0.5f).floor() *
  //                            nudged_scale_repl +
  //                        nudged_min;
  //
      template <typename T>
      void fakeQuantWithMinMaxVars_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
          int lowIntBound = narrowed ? 1 : 0;
          int upperIntBound = (1 << numBits) - 1;
  
          T nudgedMin, nudgedMax, scale;
          // nudge with given min and max and compute scale and nudged min and max
          nudge<T>(min->t<T>(0), max->t<T>(0), lowIntBound, upperIntBound, &scale, &nudgedMin, &nudgedMax);
          // quantization as one
          auto fakeQuantizationWithMinMax = LAMBDA_T(x, nudgedMin, nudgedMax, scale) {
              T val = x; // boundign value between nudged min and max
              if (val < nudgedMin) {
                  val = nudgedMin;
              }
              else if (val > nudgedMax)
                  val = nudgedMax;
              // converse value with scale and shifted with nudged min
              val -= nudgedMin;
              return (sd::math::nd4j_floor<T,T>(val / scale + T(0.5f)) * scale + nudgedMin);
          };
  
          input->applyLambda<T>(fakeQuantizationWithMinMax, *output);
      }
  
      void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
          BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVars_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
      }
      void fakeQuantWithMinMaxVarsPerChannel(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
          BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVarsPerChannel_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
      }
  
      BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVars_, (NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
  
  }
  }
  }
  