EVOLUTION-MANAGER

Edit File: cwise_ops_gradients.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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http://www.apache.org/licenses/LICENSE-2.0

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

#ifndef TENSORFLOW_CORE_KERNELS_CWISE_OPS_GRADIENTS_H_
#define TENSORFLOW_CORE_KERNELS_CWISE_OPS_GRADIENTS_H_

#define EIGEN_USE_THREADS
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/kernels/cwise_ops.h"

namespace Eigen {
namespace internal {

// Gradient for the tanh function
template <typename T>
struct scalar_tanh_gradient_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_gradient_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T
  operator()(const T& output, const T& output_gradient) const {
    return output_gradient * (T(1) - output * output);
  }
  template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
  packetOp(const Packet& output, const Packet& output_gradient) const {
    return pmul(output_gradient,
                psub(pset1<Packet>(T(1)), pmul(output, output)));
  }
};
template <typename T>
struct functor_traits<scalar_tanh_gradient_op<T>> {
  enum {
    Cost = NumTraits<T>::AddCost + 2 * NumTraits<T>::MulCost,
    PacketAccess = packet_traits<T>::HasSub && packet_traits<T>::HasMul,
  };
};

// Gradient for the sigmoid function
template <typename T>
struct scalar_sigmoid_gradient_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_gradient_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T
  operator()(const T& output, const T& output_gradient) const {
    return output_gradient * output * (T(1) - output);
  }
  template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
  packetOp(const Packet& output, const Packet& output_gradient) const {
    return pmul(output_gradient,
                pmul(output, psub(pset1<Packet>(T(1)), output)));
  }
};
template <typename T>
struct functor_traits<scalar_sigmoid_gradient_op<T>> {
  enum {
    Cost = NumTraits<T>::AddCost + 2 * NumTraits<T>::MulCost,
    PacketAccess = packet_traits<T>::HasSub && packet_traits<T>::HasMul,
  };
};

// Gradient for the inverse function
template <typename T>
struct scalar_inverse_gradient_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_gradient_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T
  operator()(const T& output, const T& output_gradient) const {
    if (output_gradient == T(0)) {
      return T(0);
    } else {
      const T out_conj = numext::conj(output);
      return -out_conj * out_conj * output_gradient;
    }
  }
  template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
  packetOp(const Packet& output, const Packet& output_gradient) const {
    const Packet out_conj = pconj(output);
    return mul_no_nan_op<T>().packetOp(pnegate(pmul(out_conj, out_conj)),
                                       output_gradient);
  }
};
template <typename T>
struct functor_traits<scalar_inverse_gradient_op<T>> {
  enum {
    Cost = NumTraits<T>::AddCost + 2 * NumTraits<T>::MulCost,
    PacketAccess = packet_traits<T>::HasMul,
  };
};

// Gradient for the sqrt function
template <typename T>
struct scalar_sqrt_gradient_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_gradient_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T
  operator()(const T& output, const T& output_gradient) const {
    if (output_gradient == T(0)) {
      return T(0);
    } else {
      const T out_conj = numext::conj(output);
      return (static_cast<T>(0.5) * output_gradient) / out_conj;
    }
  }
  template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
  packetOp(const Packet& output, const Packet& output_gradient) const {
    const Packet const_half = pset1<Packet>(static_cast<T>(0.5));
    const Packet out_conj = pconj(output);
    return mul_no_nan_op<T>().packetOp(pdiv(const_half, out_conj),
                                       output_gradient);
  }
};
template <typename T>
struct functor_traits<scalar_sqrt_gradient_op<T>> {
  enum {
    PacketAccess = packet_traits<T>::HasMul & packet_traits<T>::HasDiv,
    Cost = NumTraits<T>::MulCost + scalar_div_cost<T, PacketAccess>::value,
  };
};

// Gradient for the rsqrt function
template <typename T>
struct scalar_rsqrt_gradient_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_rsqrt_gradient_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T
  operator()(const T& output, const T& output_gradient) const {
    if (output_gradient == T(0)) {
      return T(0);
    } else {
      const T out_conj = numext::conj(output);
      return static_cast<T>(-0.5) * (output_gradient * out_conj) *
             (out_conj * out_conj);
    }
  }
  template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
  packetOp(const Packet& output, const Packet& output_gradient) const {
    const Packet const_half = pset1<Packet>(static_cast<T>(-0.5));
    const Packet out_conj = pconj(output);
    auto safe_pmul = [](const Packet& a, const Packet& b) {
      return mul_no_nan_op<T>().packetOp(a, b);
    };
    return safe_pmul(pmul(const_half, pmul(out_conj, out_conj)),
                     safe_pmul(out_conj, output_gradient));
  }
};
template <typename T>
struct functor_traits<scalar_rsqrt_gradient_op<T>> {
  enum {
    Cost = 4 * NumTraits<T>::MulCost,
    PacketAccess = packet_traits<T>::HasMul,
  };
};

}  // end namespace internal
}  // end namespace Eigen

namespace tensorflow {

namespace functor {

template <typename Device, typename Functor>
struct SimpleBinaryFunctor {
  void operator()(const Device& d, typename Functor::tout_type out,
                  typename Functor::tin_type in0,
                  typename Functor::tin_type in1);
};

// Partial specialization of BinaryFunctor for CPU devices
typedef Eigen::ThreadPoolDevice CPUDevice;

template <typename Functor>
struct SimpleBinaryFunctor<CPUDevice, Functor> {
  void operator()(const CPUDevice& d, typename Functor::tout_type out,
                  typename Functor::tin_type in0,
                  typename Functor::tin_type in1) {
    out.device(d) = in0.binaryExpr(in1, typename Functor::func());
  }
};

template <typename T>
struct tanh_grad : base<T, Eigen::internal::scalar_tanh_gradient_op<T>> {};

template <typename T>
struct sigmoid_grad : base<T, Eigen::internal::scalar_sigmoid_gradient_op<T>> {
};

template <typename T>
struct inverse_grad : base<T, Eigen::internal::scalar_inverse_gradient_op<T>> {
};

template <typename T>
struct sqrt_grad : base<T, Eigen::internal::scalar_sqrt_gradient_op<T>> {};

template <typename T>
struct rsqrt_grad : base<T, Eigen::internal::scalar_rsqrt_gradient_op<T>> {};

template <typename T>
struct igamma_grad_a : base<T, Eigen::internal::scalar_igamma_der_a_op<T>> {};

}  // end namespace functor

}  // end namespace tensorflow
#endif  // TENSORFLOW_CORE_KERNELS_CWISE_OPS_GRADIENTS_H_