EVOLUTION-MANAGER

Edit File: batch_norm_op.h

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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_BATCH_NORM_OP_H_
#define TENSORFLOW_CORE_KERNELS_BATCH_NORM_OP_H_
// Functor definition for BatchNormOp, must be compilable by nvcc.
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"

namespace tensorflow {
namespace functor {

// Functor used by BatchNormOp to do the computations.
template <typename Device, typename T>
struct BatchNorm {
  void operator()(const Device& d, typename TTypes<T, 4>::ConstTensor input,
                  typename TTypes<T>::ConstVec mean,
                  typename TTypes<T>::ConstVec var,
                  typename TTypes<T>::ConstVec beta,
                  typename TTypes<T>::ConstVec gamma, T variance_epsilon,
                  bool scale_after_normalization,
                  typename TTypes<T, 4>::Tensor output) {
    const int depth = mean.dimension(0);
    const int rest_size = input.size() / depth;

Eigen::DSizes<int, 2> rest_by_depth(rest_size, depth);
#if !defined(EIGEN_HAS_INDEX_LIST)
    Eigen::DSizes<int, 2> rest_by_one(rest_size, 1);
    Eigen::DSizes<int, 2> one_by_depth(1, depth);
    Eigen::DSizes<int, 2> depth_by_one(depth, 1);
#else
    Eigen::IndexList<int, Eigen::type2index<1> > rest_by_one;
    rest_by_one.set(0, rest_size);
    Eigen::IndexList<Eigen::type2index<1>, int> one_by_depth;
    one_by_depth.set(1, depth);
    Eigen::IndexList<int, Eigen::type2index<1> > depth_by_one;
    depth_by_one.set(0, depth);
#endif
    if (scale_after_normalization) {
      output.reshape(rest_by_depth).device(d) =
          (input.reshape(rest_by_depth) -
           mean.reshape(one_by_depth).broadcast(rest_by_one)) *
              ((var + var.constant(variance_epsilon)).rsqrt() * gamma)
                  .eval()
                  .reshape(one_by_depth)
                  .broadcast(rest_by_one) +
          beta.reshape(one_by_depth).broadcast(rest_by_one);
    } else {
      output.reshape(rest_by_depth).device(d) =
          (input.reshape(rest_by_depth) -
           mean.reshape(one_by_depth).broadcast(rest_by_one)) *
              ((var + var.constant(variance_epsilon)).rsqrt())
                  .eval()
                  .reshape(one_by_depth)
                  .broadcast(rest_by_one) +
          beta.reshape(one_by_depth).broadcast(rest_by_one);
    }
  }
};

template <typename Device, typename T>
struct BatchNormGrad {
  void operator()(const Device& d, typename TTypes<T, 4>::ConstTensor input,
                  typename TTypes<T>::ConstVec mean,
                  typename TTypes<T>::ConstVec var,
                  typename TTypes<T>::ConstVec gamma,
                  typename TTypes<T, 4>::ConstTensor out_backprop,
                  T variance_epsilon, bool scale_after_normalization,
                  typename TTypes<T, 4>::Tensor dx, typename TTypes<T>::Vec dm,
                  typename TTypes<T>::Vec dv, typename TTypes<T>::Vec db,
                  typename TTypes<T>::Vec dg, typename TTypes<T>::Vec scratch1,
                  typename TTypes<T>::Vec scratch2) {
    const int depth = mean.dimension(0);
    const int rest_size = input.size() / depth;

typedef typename TTypes<T>::ConstVec::Index Index;

Eigen::DSizes<Index, 2> rest_by_depth(rest_size, depth);
#if !defined(EIGEN_HAS_INDEX_LIST)
    Eigen::DSizes<Index, 2> rest_by_one(rest_size, 1);
    Eigen::DSizes<Index, 2> one_by_depth(1, depth);
    Eigen::array<Index, 1> reduction_axis;
    reduction_axis[0] = 0;  // Reduces on first dimension.
#else
    Eigen::IndexList<Index, Eigen::type2index<1> > rest_by_one;
    rest_by_one.set(0, rest_size);
    Eigen::IndexList<Eigen::type2index<1>, Index> one_by_depth;
    one_by_depth.set(1, depth);
    Eigen::IndexList<Eigen::type2index<0> > reduction_axis;
#endif

// db = out_backprop
    //
    // dg = out_backprop * ((x - m) * rsqrt(v + epsilon))
    //
    // dv = sum_over_rest(out_backprop * gamma * (x - m)) *
    //      (-1/2) * (v + epsilon) ^ (-3/2)
    //
    // dm = sum_over_rest(out_backprop * gamma) * (-1 / rsqrt(v + epsilon))
    //
    // dx = out_backprop * (gamma * rsqrt(v + epsilon))
    db.device(d) = out_backprop.reshape(rest_by_depth).sum(reduction_axis);

// scratch1 = rsqrt(v + epsilon)
    scratch1.device(d) = (var + var.constant(variance_epsilon)).rsqrt();

// scratch2 = sum_over_rest(out_backprop * (x - m))
    scratch2.device(d) = (out_backprop.reshape(rest_by_depth) *
                          (input.reshape(rest_by_depth) -
                           mean.reshape(one_by_depth).broadcast(rest_by_one)))
                             .sum(reduction_axis);

if (scale_after_normalization) {
      dx.reshape(rest_by_depth).device(d) =
          out_backprop.reshape(rest_by_depth) * ((scratch1 * gamma)
                                                     .eval()
                                                     .reshape(one_by_depth)
                                                     .broadcast(rest_by_one));
      dm.device(d) = -db * (scratch1 * gamma).eval();
      dg.device(d) = scratch2 * scratch1;
    } else {
      dx.reshape(rest_by_depth).device(d) =
          out_backprop.reshape(rest_by_depth) *
          scratch1.reshape(one_by_depth).broadcast(rest_by_one);
      dm.device(d) = -db * scratch1;
      dg.device(d) = dg.constant(static_cast<T>(0.0));  // Gamma is not learned.
    }

// scratch1 = - 1/2 * (var + epsilon) ^ (-3/2)
    scratch1.device(d) = scratch1 * scratch1.constant(static_cast<T>(-0.5f)) /
                         (var + var.constant(variance_epsilon));

if (scale_after_normalization) {
      dv.device(d) = scratch2 * (scratch1 * gamma).eval();
    } else {
      dv.device(d) = scratch2 * scratch1;
    }
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_BATCH_NORM_OP_H_