EVOLUTION-MANAGER

Edit File: relu_op_functor.h

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

#ifndef TENSORFLOW_CORE_KERNELS_RELU_OP_FUNCTOR_H_
#define TENSORFLOW_CORE_KERNELS_RELU_OP_FUNCTOR_H_
// Functor definition for ReluOp and ReluGradOp, 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 ReluOp to do the computations.
template <typename Device, typename T>
struct Relu {
  // Computes Relu activation.
  //
  // features: any shape.
  // activations: same shape as "features".
  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor activations) {
    activations.device(d) = features.cwiseMax(static_cast<T>(0));
  }
};

// Functor used by ReluGradOp to do the computations.
template <typename Device, typename T>
struct ReluGrad {
  // Computes ReluGrad backprops.
  //
  // gradients: gradients backpropagated to the Relu op.
  // features: either the inputs that were passed to the Relu or, or its
  //           outputs (using either one yields the same result here).
  // backprops: gradients to backpropagate to the Relu inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor backprops) {
    // NOTE: When the activation is exactly zero, we do not propagate the
    // associated gradient value. This allows the output of the Relu to be used,
    // as well as its input.
    backprops.device(d) =
        gradients * (features > static_cast<T>(0)).template cast<T>();
  }
};

// Functor used by Relu6Op to do the computations.
template <typename Device, typename T>
struct Relu6 {
  // Computes Relu6 activation.
  //
  // features: any shape.
  // activations: same shape as "features".
  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor activations) {
    activations.device(d) =
        features.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(6));
  }
};

// Functor used by ReluGradOp to do the computations.
template <typename Device, typename T>
struct Relu6Grad {
  // Computes Relu6Grad backprops.
  //
  // gradients: gradients backpropagated to the Relu6 op.
  // features: inputs that where passed to the Relu6 op, or its outputs.
  // backprops: gradients to backpropagate to the Relu6 inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor backprops) {
    // NOTE: When the activation is exactly zero or six, we
    // make sure not to propagate the associated gradient
    // value. This allows "features" to be either the input or the output of
    // the relu6.
    backprops.device(d) = gradients * ((features > static_cast<T>(0)) *
                                       (features < static_cast<T>(6)))
                                          .template cast<T>();
  }
};

// Functor used by LeakyReluOp to do the computations.
template <typename Device, typename T>
struct LeakyRelu {
  // Computes LeakyRelu activation.
  //
  // features: any shape.
  // activations: same shape as "features".

// Need to bundle the args (to the LeakyRelu functor) within a struct
  // Not doing so leads to Eigen kernel args not getting populated
  // corretly for Eigen::half type (when building on the ROCM platform)
  struct LeakyReluArgs {
    const Device& d;
    typename TTypes<T>::ConstTensor features;
    T alpha;
    typename TTypes<T>::Tensor activations;
  };
  void operator()(LeakyReluArgs args) {
    // Note that alpha might be > 1 or < 0, so we don't use cwiseMax here.
    args.activations.device(args.d) =
        (args.features > static_cast<T>(0))
            .select(args.features, args.features * args.alpha);
  }
};

// Functor used by LeakyReluGradOp to do the computations.
template <typename Device, typename T>
struct LeakyReluGrad {
  // Computes LeakyReluGrad backprops.
  //
  // gradients: gradients backpropagated to the LeakyRelu op.
  // features: either the inputs that were passed to the LeakyRelu or, or its
  //           outputs (using either one yields the same result here).
  // backprops: gradients to backpropagate to the LeakyRelu inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor features, T alpha,
                  typename TTypes<T>::Tensor backprops) {
    backprops.device(d) =
        (features > static_cast<T>(0)).select(gradients, gradients * alpha);
  }
};

// Functor used by EluOp to do the computations.
template <typename Device, typename T>
struct Elu {
  // Computes Elu activation.
  //
  // features: any shape.
  // activations: same shape as "features".
  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor activations) {
    // features.constant(?)
    activations.device(d) =
        (features < static_cast<T>(0))
            .select(features.exp() - features.constant(static_cast<T>(1)),
                    features);
  }
};

// Functor used by EluGradOp to do the computations.
template <typename Device, typename T>
struct EluGrad {
  // Computes EluGrad backprops.
  //
  // gradients: gradients backpropagated to the Elu op.
  // activations: outputs of the Elu op.
  // backprops: gradients to backpropagate to the Elu inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor activations,
                  typename TTypes<T>::Tensor backprops) {
    backprops.device(d) =
        (activations < static_cast<T>(0))
            .select((activations + static_cast<T>(1)) * gradients, gradients);
  }
};

// Functor used by SeluOp to do the computations.
template <typename Device, typename T>
struct Selu {
  // Computes Selu activation.
  //
  // features: any shape.
  // activations: same shape as "features".
  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor activations) {
    // features.constant(?)
    const auto scale = static_cast<T>(1.0507009873554804934193349852946);
    const auto scale_alpha = static_cast<T>(1.7580993408473768599402175208123);
    const auto one = static_cast<T>(1);
    const auto zero = static_cast<T>(0);
    activations.device(d) =
        (features < zero)
            .select(scale_alpha * (features.exp() - features.constant(one)),
                    scale * features);
  }
};

// Functor used by SeluGradOp to do the computations.
template <typename Device, typename T>
struct SeluGrad {
  // Computes SeluGrad backprops.
  //
  // gradients: gradients backpropagated to the Selu op.
  // activations: outputs of the Selu op.
  // backprops: gradients to backpropagate to the Selu inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor activations,
                  typename TTypes<T>::Tensor backprops) {
    const auto scale = static_cast<T>(1.0507009873554804934193349852946);
    const auto scale_alpha = static_cast<T>(1.7580993408473768599402175208123);
    backprops.device(d) =
        (activations < static_cast<T>(0))
            .select(gradients * (activations + scale_alpha), gradients * scale);
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_RELU_OP_FUNCTOR_H_