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#ifndef TENSORFLOW_KERNELS_BATCH_NORM_OP_H_
#define TENSORFLOW_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, float 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,
float 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);
Eigen::DSizes<Index, 2> rest_by_one(rest_size, 1);
Eigen::DSizes<Index, 2> one_by_depth(1, depth);
// 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))
Eigen::array<Index, 1> reduction_axis;
reduction_axis[0] = 0; // Reduces on first dimension.
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_KERNELS_BATCH_NORM_OP_H_
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