EVOLUTION-MANAGER

Edit File: softmax_op_functor.h

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

#ifndef TENSORFLOW_CORE_KERNELS_SOFTMAX_OP_FUNCTOR_H_
#define TENSORFLOW_CORE_KERNELS_SOFTMAX_OP_FUNCTOR_H_
// Functor definition for SoftmaxOp, 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 SoftmaxOp to do the computations.
template <typename Device, typename T>
struct SoftmaxFunctor {
  // Computes Softmax or LogSoftmax activation.
  //
  // logits: dim: batch_size, num_classes.
  // softmax: dims: batch_size, num_classes.
  // log: boolean
  void operator()(const Device& d, typename TTypes<T>::ConstMatrix logits,
                  typename TTypes<T>::Matrix softmax, const bool log);
};

// Eigen code implementing SoftmaxFunctor::operator() or
// LogSoftmaxFunctor::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 SoftmaxEigenImpl {
  static void Compute(const Device& d, typename TTypes<T>::ConstMatrix logits,
                      typename TTypes<T>::Matrix softmax, const bool log) {
    const int kBatchDim = 0;
    const int kClassDim = 1;

const int batch_size = logits.dimension(kBatchDim);
    const int num_classes = logits.dimension(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::DSizes<int, 1> along_class(kClassDim);
    Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
    Eigen::DSizes<int, 2> 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<Eigen::type2index<1>, int> one_by_class;
    one_by_class.set(1, num_classes);
#endif
    // shifted_logits = logits - max(logits along classes);
    auto shifted_logits = (logits - logits.maximum(along_class)
                                        .eval()
                                        .reshape(batch_by_one)
                                        .broadcast(one_by_class));
    if (log) {
      // Calculate the log of the softmax
      // softmax = logits - max(logits along classes);
      softmax.device(d) = shifted_logits;
      // softmax = softmax - log(sum(exp(softmax along classes)));
      softmax.device(d) = (softmax - softmax.exp()
                                         .sum(along_class)
                                         .log()
                                         .eval()
                                         .reshape(batch_by_one)
                                         .broadcast(one_by_class));
    } else {
      // NOTE(touts): If you modify this implementation please run
      // the BM_ImageNetSoftmaxFwd benchmark in nn_ops_test.cc.
      //
      // softmax = exp(logits - max(logits along classes));
      softmax.device(d) = shifted_logits.exp();
      // softmax = softmax * (1 / sum(softmax along classes));
      softmax.device(d) = (softmax * softmax.sum(along_class)
                                         .inverse()
                                         .eval()
                                         .reshape(batch_by_one)
                                         .broadcast(one_by_class));
    }
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_SOFTMAX_OP_FUNCTOR_H_