EVOLUTION-MANAGER

Edit File: scan_ops.h

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

#ifndef TENSORFLOW_CORE_KERNELS_SCAN_OPS_H_
#define TENSORFLOW_CORE_KERNELS_SCAN_OPS_H_

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"

namespace tensorflow {
namespace functor {

typedef Eigen::Index Index;

// TODO(b/154339590): Needs to be vectorized.
template <typename Device, typename Reducer, typename T>
struct Scan {
  void operator()(const Device& d, typename TTypes<T, 3>::ConstTensor in,
                  typename TTypes<T, 3>::Tensor out, const Reducer& reducer,
                  const bool reverse, const bool exclusive) {
    // Perform the reverse ops directly with Eigen, which avoids copying the
    // tensor twice compared to using individual ops.
    Eigen::array<bool, 3> dims;
    dims[0] = false;
    dims[1] = reverse;
    dims[2] = false;
    To32Bit(out).device(d) =
        To32Bit(in).reverse(dims).scan(1, reducer, exclusive).reverse(dims);
  }
};

template <typename T>
struct LogSumExp {
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& a,
                                                     const T& b) const {
    auto mi = Eigen::internal::scalar_min_op<T>()(a, b);
    auto ma = Eigen::internal::scalar_max_op<T>()(a, b);

auto sub = Eigen::internal::scalar_difference_op<T>();
    auto add = Eigen::internal::scalar_sum_op<T>();
    auto exp = Eigen::internal::scalar_exp_op<T>();
    auto log1p = Eigen::internal::scalar_log1p_op<T>();
    auto cmp_lt =
        Eigen::internal::scalar_cmp_op<T, T, Eigen::internal::cmp_LT>();

auto logsumexp = add(log1p(exp(sub(mi, ma))), ma);
    return cmp_lt(ma, Eigen::NumTraits<T>::lowest()) ? ma : logsumexp;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const T& a,
                                                   const T& b) const {
    auto mi = Eigen::internal::pmin(a, b);
    auto ma = Eigen::internal::pmax(a, b);
    using Eigen::internal::padd;
    using Eigen::internal::pcmp_lt;
    using Eigen::internal::pexp;
    using Eigen::internal::plog1p;
    using Eigen::internal::pset1;
    using Eigen::internal::psub;

auto logsumexp = padd(plog1p(pexp(psub(mi, ma))), ma);
    return pselect(pcmp_lt(ma, pset1(Eigen::NumTraits<T>::lowest())), ma,
                   logsumexp);
  }
};

template <typename T>
struct LogSumExpReducer {
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
    LogSumExp<T> logsumexp;
    *accum = logsumexp(*accum, t);
  }

template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p,
                                                          Packet* accum) const {
    LogSumExp<T> logsumexp;
    *accum = logsumexp.packetOp(*accum, p);
  }

EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
    return -Eigen::NumTraits<T>::infinity();
  }

template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
    return Eigen::internal::pset1(initialize());
  }

EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
    return accum;
  }

template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
  finalizePacket(const Packet& vaccum) const {
    return vaccum;
  }

template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T
  finalizeBoth(const T saccum, const Packet& vaccum) const {
    auto max_reducer = Eigen::internal::MaxReducer<T, Eigen::PropagateNaN>();
    auto sum_reducer = Eigen::internal::SumReducer<T>();
    auto exp = Eigen::internal::scalar_exp_op<T>();
    auto cmp_lt =
        Eigen::internal::scalar_cmp_op<T, T, Eigen::internal::cmp_LT>();
    auto log = Eigen::internal::scalar_log_op<T>();
    auto add = Eigen::internal::scalar_sum_op<T>();

using Eigen::internal::pexp;
    using Eigen::internal::psub;

// `ma = max(x1, ..., xn)`
    // If the max of all of the `xi` is `-infinity` then the result is
    // -infinity. If the max is larger than `-infinity` then it's safe to use
    // for normalization even if the other elements are `-infinity`.
    //
    // `logsumexp(x1, ..., xn) = ma + log (exp(x1 - ma) + ... + exp(xn - ma))`
    auto ma = max_reducer.finalizeBoth(saccum, vaccum);
    auto logsumexp = add(log(sum_reducer.finalizeBoth(
                             exp(saccum - ma), pexp(psub(vaccum, pset1(ma))))),
                         ma);
    return cmp_lt(ma, Eigen::NumTraits<T>::lowest()) ? initialize() : logsumexp;
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_SCAN_OPS_H_