EVOLUTION-MANAGER

Edit File: xent_op.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_XENT_OP_H_
#define TENSORFLOW_CORE_KERNELS_XENT_OP_H_
// Functor definition for XentOp, 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 XentOp to do the computations.
template <typename Device, typename T>
struct XentFunctor {
  // Computes Cross Entropy loss and backprop.
  //
  // logits: batch_size, num_classes.
  // labels: batch_size, num_classes.
  // scratch: temporary tensor, dims: batch_size, 1
  // loss: output tensor for the loss, dims: batch_size.
  // backprop: output tensor for the backprop, dims: batch_size, num_classes.
  void operator()(const Device &d,
                  const Eigen::DSizes<Eigen::DenseIndex, 2> &shape,
                  const Eigen::array<Eigen::DenseIndex, 2> &logits_bcast,
                  const Eigen::array<Eigen::DenseIndex, 2> &labels_bcast,
                  typename TTypes<T>::ConstMatrix logits,
                  typename TTypes<T>::ConstMatrix labels,
                  typename TTypes<T>::Matrix scratch,
                  typename TTypes<T>::Vec loss,
                  typename TTypes<T>::Matrix backprop);
};

// Eigen code implementing XentFunctor::operator().
// This code works for both CPU and GPU and is used by the functor
// specializations for both device types.
template <typename Device, typename T>
struct XentEigenImpl {
  static void Compute(const Device &d,
                      const Eigen::DSizes<Eigen::DenseIndex, 2> &shape,
                      const Eigen::array<Eigen::DenseIndex, 2> &logits_bcast,
                      const Eigen::array<Eigen::DenseIndex, 2> &labels_bcast,
                      typename TTypes<T>::ConstMatrix logits,
                      typename TTypes<T>::ConstMatrix labels,
                      typename TTypes<T>::Matrix scratch,
                      typename TTypes<T>::Vec loss,
                      typename TTypes<T>::Matrix backprop) {
    // NOTE(touts): This duplicates some of the computations in softmax_op
    // because we need the intermediate (logits -max(logits)) values to
    // avoid a log(exp()) in the computation of the loss.

const int kBatchDim = 0;
    const int kClassDim = 1;

const int batch_size = shape[kBatchDim];
    const int num_classes = shape[kClassDim];

// These arrays are used to reduce along the class dimension, and broadcast
// the resulting value to all classes.
#if !defined(EIGEN_HAS_INDEX_LIST)
    Eigen::array<int, 1> along_class;
    along_class[0] = kClassDim;
    Eigen::array<int, 1> batch_only;
    batch_only[0] = batch_size;
    Eigen::array<int, 2> batch_by_one;
    batch_by_one[0] = batch_size;
    batch_by_one[1] = 1;
    Eigen::array<int, 2> one_by_class;
    one_by_class[0] = 1;
    one_by_class[1] = num_classes;
#else
    Eigen::IndexList<Eigen::type2index<kClassDim> > along_class;
    Eigen::IndexList<int, Eigen::type2index<1> > batch_by_one;
    batch_by_one.set(0, batch_size);
    Eigen::IndexList<int> batch_only;
    batch_only.set(0, batch_size);
    Eigen::IndexList<Eigen::type2index<1>, int> one_by_class;
    one_by_class.set(1, num_classes);
#endif

// max_logits along classes.
    scratch.reshape(batch_only).device(d) =
        logits.broadcast(logits_bcast).maximum(along_class);

// logits - max_logits.
    backprop.device(d) =
        logits.broadcast(logits_bcast) - scratch.broadcast(one_by_class);

// sum(exp(logits - max_logits)) along classes.
    scratch.reshape(batch_only).device(d) = backprop.exp().sum(along_class);

// NOTE(keveman): Eigen on GPU dispatches to an optimized implementation
    // for an expression of the form lhs = rhs.sum().
    // lhs = -rhs.sum() doesn't match the above pattern, so folding in the
    // negation before calling sum().
    //  sum(-labels *
    //     ((logits - max_logits) - log(sum(exp(logits - max_logits)))))
    //  along classes
    loss.device(d) = (labels.broadcast(labels_bcast) *
                      (scratch.log().eval().broadcast(one_by_class) - backprop))
                         .eval()
                         .sum(along_class);

// backprop: prob - labels, where
    //   prob = exp(logits - max_logits) / sum(exp(logits - max_logits))
    backprop.device(d) = (backprop.exp() / scratch.broadcast(one_by_class)) -
                         labels.broadcast(labels_bcast);
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_XENT_OP_H_