activation_op.cc

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/fluid/operators/activation_op.h"
#include <memory>
#include <string>
#include <type_traits>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/operators/mkldnn/mkldnn_activation_op.h"
#include "paddle/fluid/platform/port.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif

DECLARE_bool(use_mkldnn);

namespace paddle {
namespace operators {

using paddle::framework::Tensor;

template <typename GradFunctor>
static constexpr bool CanInplaceAct() {
  return GradFunctor::FwdDeps() == kDepOut || GradFunctor::FwdDeps() == kNoDeps;
}

#define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT)                    \
  class OP_NAME##OpMaker                                                     \
      : public ::paddle::framework::OpProtoAndCheckerMaker {                 \
   public:                                                                   \
    void Make() override {                                                   \
      AddInput("X", "Input of " #OP_NAME                                     \
                    " operator, an N-D Tensor, with data type float32, "     \
                    "float64 or float16.");                                  \
      AddOutput("Out", "Output of " #OP_NAME                                 \
                       " operator, a Tensor with shape same as input.");     \
      AddAttr<bool>("use_mkldnn",                                            \
                    "(bool, default false) Only used in mkldnn kernel")      \
          .SetDefault(false);                                                \
      AddAttr<bool>("use_cudnn",                                             \
                    "(bool, default false) Only used in cudnn kernel, need " \
                    "install cudnn")                                         \
          .SetDefault(false);                                                \
      AddAttr<bool>(                                                         \
          "is_test",                                                         \
          "(bool, default false) Set to true for inference only, false "     \
          "for training. Some layers may run faster when this is true.")     \
          .SetDefault(false);                                                \
      AddComment(OP_COMMENT);                                                \
    }                                                                        \
  }

template <ActBwdOpFwdDeps kDepValue, typename T>
class ActivationGradOpMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  std::unique_ptr<T> Apply() const override {
    std::unique_ptr<T> op(new T());
    op->SetType(this->ForwardOpType() + "_grad");
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetAttrMap(this->Attrs());

    if ((static_cast<int>(kDepValue) &
         static_cast<int>(ActBwdOpFwdDeps::kDepX)) ||
        FLAGS_use_mkldnn || (op->HasAttr("use_mkldnn") &&
                             boost::get<bool>(op->GetAttr("use_mkldnn")))) {
      op->SetInput("X", this->Input("X"));
    }

    if (static_cast<int>(kDepValue) &
        static_cast<int>(ActBwdOpFwdDeps::kDepOut)) {
      op->SetInput("Out", this->Output("Out"));
    }

    return op;
  }
};

framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
                                      const framework::OperatorWithKernel& oper,
                                      const std::string& name) {
  framework::LibraryType library{framework::LibraryType::kPlain};
  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
// FIXME(liuwei1031) temporarily disable the code to unblock users
// TODO(liuwei1031) figure out the reason behind
// https://github.com/PaddlePaddle/Paddle/issues/16096
// and re-enable this in the future
// #ifdef PADDLE_WITH_CUDA
//   auto it1 = oper.Attrs().find("use_cudnn");
//   if (it1 != oper.Attrs().end() && platform::CanCUDNNBeUsed(ctx)) {
//     library = framework::LibraryType::kCUDNN;
//   }
// #endif
#ifdef PADDLE_WITH_MKLDNN
  auto it = oper.Attrs().find("use_mkldnn");
  if (library == framework::LibraryType::kPlain && it != oper.Attrs().end() &&
      platform::CanMKLDNNBeUsed(ctx)) {
    library = framework::LibraryType::kMKLDNN;
    layout = framework::DataLayout::kMKLDNN;
  }
#endif
  return framework::OpKernelType(oper.IndicateVarDataType(ctx, name),
                                 ctx.GetPlace(), layout, library);
}

class ActivationOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    ctx->ShareDim("X", /*->*/ "Out");
    ctx->ShareLoD("X", /*->*/ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return GetKernelType(ctx, *this, "X");
  }
};

class ActivationOpInferVarType
    : public framework::PassInDtypeAndVarTypeToOutput {
 protected:
  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
      const override {
    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
  }
};

class ActivationOpGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    auto out_grad_name = framework::GradVarName("Out");
    ctx->ShareDim(out_grad_name, framework::GradVarName("X"));
    ctx->ShareLoD(out_grad_name, framework::GradVarName("X"));
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return GetKernelType(ctx, *this, framework::GradVarName("Out"));
  }
};

UNUSED constexpr char SigmoidDoc[] = R"DOC(
Sigmoid Activation Operator

$$out = \\frac{1}{1 + e^{-x}}$$

)DOC";

UNUSED constexpr char LogSigmoidDoc[] = R"DOC(
Logsigmoid Activation Operator

$$out = \\log \\frac{1}{1 + e^{-x}}$$

)DOC";

UNUSED constexpr char ExpDoc[] = R"DOC(
Exp Operator. Computes exp of x element-wise with a natural number :math:`e` as the base.

$out = e^x$

)DOC";

UNUSED constexpr char ReluDoc[] = R"DOC(
Relu Activation Operator.

$out = \max(x, 0)$

)DOC";

UNUSED constexpr char GeluDoc[] = R"DOC(
Gelu Activation Operator.

$out = \\frac{1 + erf(\\frac{x}{\\sqrt{2}})}{2} x$

)DOC";

UNUSED constexpr char TanhDoc[] = R"DOC(
Tanh Activation Operator.

$$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$

)DOC";

UNUSED constexpr char TanhShrinkDoc[] = R"DOC(
TanhShrink Activation Operator.

$$out = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$

)DOC";

UNUSED constexpr char SqrtDoc[] = R"DOC(
Sqrt Activation Operator.

.. math:: out=\sqrt x=x^{1/2}

**Note**:
  input value must be greater than or equal to zero.

)DOC";

UNUSED constexpr char RsqrtDoc[] = R"DOC(
Rsqrt Activation Operator.

Please make sure input is legal in case of numeric errors.

$out = \frac{1}{\sqrt{x}}$

)DOC";

UNUSED constexpr char AbsDoc[] = R"DOC(
Abs Activation Operator.

$out = |x|$

)DOC";

UNUSED constexpr char CeilDoc[] = R"DOC(
Ceil Operator. Computes ceil of x element-wise.

$out = \left \lceil x \right \rceil$

)DOC";

UNUSED constexpr char FloorDoc[] = R"DOC(
Floor Activation Operator.

$out = \left \lfloor x \right \rfloor$

)DOC";

UNUSED constexpr char CosDoc[] = R"DOC(
Cosine Operator. Computes cosine of x element-wise.

$out = cos(x)$

)DOC";

UNUSED constexpr char SinDoc[] = R"DOC(
Sine Activation Operator.

$out = sin(x)$

)DOC";

UNUSED constexpr char RoundDoc[] = R"DOC(
The OP rounds the values in the input to the nearest integer value.

.. code-block:: python

  input:
    x.shape = [4]
    x.data = [1.2, -0.9, 3.4, 0.9]

  output:
    out.shape = [4]
    out.data = [1., -1., 3., 1.]

)DOC";

UNUSED constexpr char ReciprocalDoc[] = R"DOC(
Reciprocal Activation Operator.

$$out = \\frac{1}{x}$$

)DOC";

UNUSED constexpr char LogDoc[] = R"DOC(
Log Activation Operator.

$out = \ln(x)$

Natural logarithm of x.

)DOC";

UNUSED constexpr char SquareDoc[] = R"DOC(
The OP square each elements of the inputs.

$out = x^2$

)DOC";

UNUSED constexpr char SoftplusDoc[] = R"DOC(
Softplus Activation Operator.

$out = \ln(1 + e^{x})$

)DOC";

UNUSED constexpr char SoftsignDoc[] = R"DOC(
Softsign Activation Operator.

$$out = \\frac{x}{1 + \|x\|}$$

)DOC";

class AcosOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of acos operator");
    AddOutput("Out", "Output of acos operator");
    AddComment(R"DOC(
Arccosine Activation Operator.

$$out = \cos^{-1}(x)$$

)DOC");
  }
};

class AsinOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of asin operator");
    AddOutput("Out", "Output of asin operator");
    AddComment(R"DOC(
Arcsine Activation Operator.

$$out = \sin^{-1}(x)$$

)DOC");
  }
};

class AtanOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of atan operator");
    AddOutput("Out", "Output of atan operator");
    AddComment(R"DOC(
Arctanh Activation Operator.

$$out = \tanh^{-1}(x)$$

)DOC");
  }
};

class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "A LoDTensor or Tensor representing preactivation values. Must be "
             "one of the following types: float32, float64.");
    AddOutput(
        "Out",
        "A LoDTensor or Tensor with the same type and size as that of x.");
    AddAttr<float>("alpha", "Slope of the activation function at x < 0.")
        .SetDefault(0.02f);
    AddAttr<bool>("use_mkldnn",
                  "(bool, default false) Only used in mkldnn kernel")
        .SetDefault(false);
    AddAttr<bool>("is_test",
                  "(bool, default false) Set to true for inference only, false "
                  "for training. Some layers may run faster when this is true.")
        .SetDefault(false);
    AddComment(R"DOC(
LeakyRelu Activation Operator.

$$out = \max(x, \alpha * x)$$

)DOC");
  }
};

class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of Softshrink operator");
    AddOutput("Out", "Output of Softshrink operator");
    AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
    AddComment(R"DOC(
:strong:`Softshrink Activation Operator`

..  math::
    out = \begin{cases}
         x - \lambda, \text{if } x > \lambda \\
         x + \lambda, \text{if } x < -\lambda \\
         0,  \text{otherwise}
         \end{cases}

)DOC");
  }
};

class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of HardShrink operator");
    AddOutput("Out", "Output of HardShrink operator");
    AddAttr<float>("threshold",
                   "The value of threshold for HardShrink. [default: 0.5]")
        .SetDefault(0.5f);
    AddComment(R"DOC(
:strong:`HardShrink activation operator`

..  math::
    out = \begin{cases}
            x, \text{if } x > \lambda \\
            x, \text{if } x < -\lambda \\
            0,  \text{otherwise}
          \end{cases}

)DOC");
  }
};

class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "The input is a multi-dimensional Tensor. The data type is "
             "float32, float64.");
    AddOutput("Out",
              "The output is a multi-dimensional Tensor which has same "
              "dimension and data type as the ``X``.");
    AddAttr<float>("t_min", "The min marginal value of BRelu")
        .SetDefault(static_cast<float>(0));
    AddAttr<float>("t_max", "The max marginal value of BRelu")
        .SetDefault(static_cast<float>(24));
    AddComment(R"DOC(
BRelu Activation Operator.

$out = \min(\max(x, t_{min}), t_{max})$

)DOC");
  }
};

class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of SoftRelu operator");
    AddOutput("Out", "Output of SoftRelu operator");
    AddAttr<float>("threshold", "The threshold value of SoftRelu")
        .SetDefault(40.0f);
    AddComment(R"DOC(
SoftRelu Activation Operator.

$out = \ln(1 + \exp(\max(\min(x, threshold), -threshold)))$

)DOC");
  }
};

class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "The input is a multi-dimensional Tensor. The data type is "
             "float32 or float64.");
    AddOutput("Out",
              "The output is a multi-dimensional Tensor which has same "
              "dimension and data type as the ``x``.");
    AddAttr<float>("alpha", "The alpha value of ELU").SetDefault(1.0f);
    AddComment(R"DOC(
ELU Activation Operator.

Applies the following element-wise computation on the input according to
https://arxiv.org/abs/1511.07289.

$out = \max(0, x) + \min(0, \alpha * (e^x - 1))$

)DOC");
  }
};

class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "Input of relu6 operator, an N-D Tensor, "
             "with data type float32, float64.");
    AddOutput(
        "Out",
        "Output of relu6 operator, a Tensor with the same shape as input.");
    AddAttr<float>("threshold",
                   "The threshold value of Relu6. Default is 6.0. ")
        .SetDefault(6.0f);
    AddComment(R"DOC(
Relu6 Activation Operator.

$out = \min(\max(0, x), threshold)$

)DOC");
  }
};

class PowOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of Pow operator");
    AddInput("FactorTensor",
             "(Tensor<float>, optional). If provided, pow will use this"
             "The shape of FactorTensor MUST BE [1]."
             "it has higher priority than attr(factor).")
        .AsDispensable();
    AddOutput("Out", "Output of Pow operator");
    AddAttr<float>("factor", "The exponential factor of Pow").SetDefault(1.0f);
    AddComment(R"DOC(
Pow Activation Operator.

$out = x^{factor}$

)DOC");
  }
};

class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "Input of STanh operator."
             " A LoDTensor or Tensor with type float32, float64.");
    AddOutput("Out", "Output of STanh operator. A Tensor with type float32.");
    AddAttr<float>("scale_a", "The scale parameter of a for the input. ")
        .SetDefault(0.67f);
    AddAttr<float>("scale_b", "The scale parameter of b for the input")
        .SetDefault(1.7159f);
    AddComment(R"DOC(
STanh Activation Operator.

$$out = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$

)DOC");
  }
};

class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of ThresholdedRelu operator");
    AddOutput("Out", "Output of ThresholdedRelu operator");
    AddAttr<float>("threshold",
                   "The threshold location of activation. [default 1.0].")
        .SetDefault(1.0f);
    AddComment(R"DOC(
:strong:`ThresholdedRelu activation operator`

..  math::

    out = \begin{cases}
             x,  \text{if } x > threshold \\
             0,  \text{otherwise}
          \end{cases}
)DOC");
  }
};

class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "An N-D Tensor with data type float32, float64. ");
    AddOutput("Out", "A Tensor with the same shape as input. ");
    AddAttr<float>("slope",
                   "The slope of the linear approximation of sigmoid. Its "
                   "value MUST BE positive. Default is 0.2. ")
        .SetDefault(0.2f);
    AddAttr<float>(
        "offset",
        "The offset of the linear approximation of sigmoid. Default is 0.5. ")
        .SetDefault(0.5f);
    AddComment(R"DOC(
HardSigmoid Activation Operator.

A 3-part piecewise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
which is much faster than sigmoid.

$out = \max(0, \min(1, slope * x + offset))$

)DOC");
  }
};

class SwishOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of Swish operator");
    AddOutput("Out", "Output of Swish operator");
    AddAttr<float>("beta", "Constant beta of swish operator").SetDefault(1.0f);
    AddComment(R"DOC(
Swish Activation Operator.

$$out = \\frac{x}{1 + e^{- \beta \ x}}$$

)DOC");
  }
};

class HardSwishOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "Input of HardSwish operator");
    AddOutput("Out", "Output of HardSwish operator");
    AddAttr<float>("threshold", "The threshold parameter of HardSwish operator")
        .SetDefault(6.0f);
    AddAttr<float>("scale", "The scale parameter of HardSwish operator")
        .SetDefault(6.0f);
    AddAttr<float>("offset", "The offset parameter of HardSwish operator")
        .SetDefault(3.0f);
    AddComment(R"DOC(
HardSwish Activation Operator.

The hard version of swish(https://arxiv.org/pdf/1905.02244.pdf).

$out = \frac{x * (min(max(0, x+offset), threshold))}{scale}$

The threshold and scale should be positive. The offset can be either positive or negative.
The default parameters are set according to the above reference.
It is recommended to use the defaults for this activation.

)DOC");
  }
};

REGISTER_ACTIVATION_OP_MAKER(Sigmoid, SigmoidDoc);
REGISTER_ACTIVATION_OP_MAKER(LogSigmoid, LogSigmoidDoc);
REGISTER_ACTIVATION_OP_MAKER(Exp, ExpDoc);
REGISTER_ACTIVATION_OP_MAKER(Relu, ReluDoc);
REGISTER_ACTIVATION_OP_MAKER(Gelu, GeluDoc);
REGISTER_ACTIVATION_OP_MAKER(Tanh, TanhDoc);
REGISTER_ACTIVATION_OP_MAKER(TanhShrink, TanhShrinkDoc);
REGISTER_ACTIVATION_OP_MAKER(Sqrt, SqrtDoc);
REGISTER_ACTIVATION_OP_MAKER(Rsqrt, RsqrtDoc);
REGISTER_ACTIVATION_OP_MAKER(Abs, AbsDoc);
REGISTER_ACTIVATION_OP_MAKER(Ceil, CeilDoc);
REGISTER_ACTIVATION_OP_MAKER(Floor, FloorDoc);
REGISTER_ACTIVATION_OP_MAKER(Cos, CosDoc);
REGISTER_ACTIVATION_OP_MAKER(Sin, SinDoc);
REGISTER_ACTIVATION_OP_MAKER(Round, RoundDoc);
REGISTER_ACTIVATION_OP_MAKER(Reciprocal, ReciprocalDoc);
REGISTER_ACTIVATION_OP_MAKER(Log, LogDoc);
REGISTER_ACTIVATION_OP_MAKER(Square, SquareDoc);
REGISTER_ACTIVATION_OP_MAKER(Softplus, SoftplusDoc);
REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc);

template <ActBwdOpFwdDeps kDepValue>
class ActivationOpDoubleGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    if (static_cast<int>(kDepValue) & static_cast<int>(kDepX)) {
      if (ctx->HasOutput("DX")) {
        ctx->ShareDim("X", "DX");
        ctx->ShareLoD("X", "DX");
      }
      if (ctx->HasOutput("DDOut")) {
        ctx->ShareDim("X", "DDOut");
        ctx->ShareLoD("X", "DDOut");
      }
    }
    if (static_cast<int>(kDepValue) & static_cast<int>(kDepOut)) {
      if (ctx->HasOutput("DOut")) {
        ctx->ShareDim("Out", "DOut");
        ctx->ShareLoD("Out", "DOut");
      }
      if (ctx->HasOutput("DDOut")) {
        ctx->ShareDim("Out", "DDOut");
        ctx->ShareLoD("Out", "DDOut");
      }
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return GetKernelType(ctx, *this, "DDX");
  }
};

template <ActBwdOpFwdDeps kDepValue>
class ActivationOpDoubleGrad2 : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    if (static_cast<int>(kDepValue) & static_cast<int>(kDepX)) {
      if (ctx->HasOutput("DDOut")) {
        ctx->ShareDim("X", "DDOut");
        ctx->ShareLoD("X", "DDOut");
      }
    }
    if (static_cast<int>(kDepValue) & static_cast<int>(kDepOut)) {
      if (ctx->HasOutput("DDOut")) {
        ctx->ShareDim("Out", "DDOut");
        ctx->ShareLoD("Out", "DDOut");
      }
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return GetKernelType(ctx, *this, "DDX");
  }
};

//
// ReluGrad: dx = dy if y >= 0 else 0
// ReluGradGrad: ddy = ddx if y >= 0 else 0
//
template <typename T>
class ReluDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
 public:
  using ::paddle::framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  std::unique_ptr<T> Apply() const override {
    auto* op = new T();
    op->SetType("relu_grad_grad");
    // input1: Out
    op->SetInput("Out", this->Input("Out"));
    // input2: ddx
    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
    op->SetAttrMap(this->Attrs());
    // output: ddy
    op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
    return std::unique_ptr<T>(op);
  }
};

// leaky_relu Grad: dx=dy if y>=0 else alpha * dy
// leaky_relu GradGrad: ddy=ddx if y>=0 else alpha * ddx
template <typename T>
class LeakyReluDoubleGradMaker
    : public ::paddle::framework::SingleGradOpMaker<T> {
 public:
  using ::paddle::framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  std::unique_ptr<T> Apply() const override {
    auto* op = new T();
    op->SetType("leaky_relu_grad_grad");
    // input1: Out
    op->SetInput("Out", this->Input("Out"));
    // X@GRAD@GRAD: ddx
    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
    op->SetAttrMap(this->Attrs());
    // Out@GRAD@GRAD: ddy
    op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
    return std::unique_ptr<T>(op);
  }
};

// sqrt Grad: dx = 0.5 * dy / y
// sqrt GradGrad: ddy = 0.5 * ddx / y, dy = -1 * dx * ddx
template <typename T>
class SqrtDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
 public:
  using ::paddle::framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  std::unique_ptr<T> Apply() const override {
    auto* op = new T();
    op->SetType("sqrt_grad_grad");
    op->SetInput("Out", this->Input("Out"));
    op->SetInput("DX", this->Output(framework::GradVarName("X")));
    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
    op->SetAttrMap(this->Attrs());
    op->SetOutput("DOut", this->InputGrad("Out"));
    op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
    return std::unique_ptr<T>(op);
  }
};

// square Grad: dx=2x*dy
// square GradGrad: ddy=2x*ddx, dx=2dy*ddx
template <typename T>
class SquareDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
 public:
  using ::paddle::framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  std::unique_ptr<T> Apply() const override {
    auto* op = new T();
    op->SetType("square_grad_grad");
    op->SetInput("X", this->Input("X"));
    // Out@GRAD: dy
    op->SetInput("DOut", this->Input(framework::GradVarName("Out")));
    // X@GRAD@GRAD: ddx
    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));

    op->SetAttrMap(this->Attrs());

    // X@GRAD: dx
    op->SetOutput("DX", this->InputGrad("X"));
    // Out@GRAD@GRAD: ddy
    op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
    return std::unique_ptr<T>(op);
  }
};

DECLARE_INPLACE_OP_INFERER(ActivationGradOpInplaceInference,
                           {framework::GradVarName("Out"),
                            framework::GradVarName("X")});
DECLARE_INPLACE_OP_INFERER(ActivationDoubleGradOpInplaceInference,
                           {"DDX", "DDOut"});

template <typename T>
class PowGradOpMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  std::unique_ptr<T> Apply() const override {
    std::unique_ptr<T> op(new T());
    op->SetType("pow_grad");
    op->SetInput("X", this->Input("X"));
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetInput("FactorTensor", this->Input("FactorTensor"));
    op->SetAttrMap(this->Attrs());

    return op;
  }
};
class PowOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    ctx->ShareDim("X", /*->*/ "Out");
    ctx->ShareLoD("X", /*->*/ "Out");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return GetKernelType(ctx, *this, "X");
  }

  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const override {
    if (var_name == "FactorTensor") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};

class PowOpGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    auto out_grad_name = framework::GradVarName("Out");
    ctx->ShareDim(out_grad_name, framework::GradVarName("X"));
    ctx->ShareLoD(out_grad_name, framework::GradVarName("X"));
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return GetKernelType(ctx, *this, framework::GradVarName("Out"));
  }

  framework::OpKernelType GetKernelTypeForVar(
      const std::string& var_name, const Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const override {
    if (var_name == "FactorTensor") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};
DECLARE_INPLACE_OP_INFERER(ActFwdInplaceInferer, {"X", "Out"});
}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
namespace plat = paddle::platform;

#define REGISTER_ACTIVATION_OP(KERNEL_TYPE, OP_NAME, functor, grad_functor) \
  REGISTER_OPERATOR(                                                        \
      KERNEL_TYPE, ops::ActivationOp, ops::OP_NAME##OpMaker,                \
      ops::ActivationOpInferVarType,                                        \
      ops::ActivationGradOpMaker<ops::grad_functor<float>::FwdDeps(),       \
                                 paddle::framework::OpDesc>,                \
      ops::ActivationGradOpMaker<ops::grad_functor<float>::FwdDeps(),       \
                                 paddle::imperative::OpBase>,               \
      std::conditional<ops::CanInplaceAct<ops::grad_functor<float>>(),      \
                       ops::ActFwdInplaceInferer, void>::type);             \
  REGISTER_OPERATOR(KERNEL_TYPE##_grad, ops::ActivationOpGrad,              \
                    ops::ActivationGradOpInplaceInference);

#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, op_name, functor,        \
                                       grad_functor)                      \
  REGISTER_OP_CPU_KERNEL(                                                 \
      act_type, ops::ActivationKernel<paddle::platform::CPUDeviceContext, \
                                      ops::functor<float>>,               \
      ops::ActivationKernel<paddle::platform::CPUDeviceContext,           \
                            ops::functor<double>>);                       \
  REGISTER_OP_CPU_KERNEL(                                                 \
      act_type##_grad,                                                    \
      ops::ActivationGradKernel<paddle::platform::CPUDeviceContext,       \
                                ops::grad_functor<float>>,                \
      ops::ActivationGradKernel<paddle::platform::CPUDeviceContext,       \
                                ops::grad_functor<double>>);

FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_OP);
FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_CPU_KERNEL);

/* ==========================    relu register  ============================= */
REGISTER_OPERATOR(
    relu, ops::ActivationOp, ops::ReluOpMaker, ops::ActivationOpInferVarType,
    ops::ActivationGradOpMaker<ops::ReluGradFunctor<float>::FwdDeps(),
                               paddle::framework::OpDesc>,
    ops::ActivationGradOpMaker<ops::ReluGradFunctor<float>::FwdDeps(),
                               paddle::imperative::OpBase>,
    ops::ActFwdInplaceInferer);
REGISTER_OPERATOR(relu_grad, ops::ActivationOpGrad,
                  ops::ActivationGradOpInplaceInference,
                  ops::ReluDoubleGradMaker<paddle::framework::OpDesc>,
                  ops::ReluDoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(
    relu_grad_grad,
    ops::ActivationOpDoubleGrad2<ops::ReluGradFunctor<float>::FwdDeps()>,
    ops::ActivationDoubleGradOpInplaceInference);

REGISTER_ACTIVATION_CPU_KERNEL(relu, Relu, ReluFunctor, ReluGradFunctor);

REGISTER_OP_CPU_KERNEL(
    relu_grad_grad,
    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
                                    ops::ReluGradGradFunctor<float>>,
    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
                                    ops::ReluGradGradFunctor<double>>,
    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
                                    ops::ReluGradGradFunctor<plat::float16>>);
/* ========================================================================== */

/* ======================== leaky relu register  ============================ */
REGISTER_OPERATOR(
    leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker,
    ops::ActivationOpInferVarType,
    ops::ActivationGradOpMaker<ops::LeakyReluGradFunctor<float>::FwdDeps(),
                               paddle::framework::OpDesc>,
    ops::ActivationGradOpMaker<ops::LeakyReluGradFunctor<float>::FwdDeps(),
                               paddle::imperative::OpBase>,
    ops::ActFwdInplaceInferer);
REGISTER_OPERATOR(leaky_relu_grad, ops::ActivationOpGrad,
                  ops::ActivationGradOpInplaceInference,
                  ops::LeakyReluDoubleGradMaker<paddle::framework::OpDesc>,
                  ops::LeakyReluDoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(
    leaky_relu_grad_grad,
    ops::ActivationOpDoubleGrad2<ops::LeakyReluGradFunctor<float>::FwdDeps()>,
    ops::ActivationDoubleGradOpInplaceInference);

REGISTER_ACTIVATION_CPU_KERNEL(leaky_relu, LeakyRelu, LeakyReluFunctor,
                               LeakyReluGradFunctor);
REGISTER_OP_CPU_KERNEL(
    leaky_relu_grad_grad,
    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
                                    ops::LeakyReluGradGradFunctor<float>>,
    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
                                    ops::LeakyReluGradGradFunctor<double>>,
    ops::ActivationDoubleGradKernel<
        plat::CPUDeviceContext, ops::LeakyReluGradGradFunctor<plat::float16>>);
/* ========================================================================== */

/* ===========================   sqrt register  ============================= */
REGISTER_OPERATOR(
    sqrt, ops::ActivationOp, ops::SqrtOpMaker, ops::ActivationOpInferVarType,
    ops::ActivationGradOpMaker<ops::SqrtGradFunctor<float>::FwdDeps(),
                               paddle::framework::OpDesc>,
    ops::ActivationGradOpMaker<ops::SqrtGradFunctor<float>::FwdDeps(),
                               paddle::imperative::OpBase>,
    ops::ActFwdInplaceInferer);
REGISTER_OPERATOR(sqrt_grad, ops::ActivationOpGrad,
                  ops::ActivationGradOpInplaceInference,
                  ops::SqrtDoubleGradMaker<paddle::framework::OpDesc>,
                  ops::SqrtDoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(
    sqrt_grad_grad,
    ops::ActivationOpDoubleGrad<ops::SqrtGradGradFunctor<float>::FwdDeps()>,
    ops::ActivationDoubleGradOpInplaceInference);

REGISTER_ACTIVATION_CPU_KERNEL(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor);
REGISTER_OP_CPU_KERNEL(
    sqrt_grad_grad, ops::SqrtDoubleGradKernel<plat::CPUDeviceContext,
                                              ops::SqrtGradGradFunctor<float>>,
    ops::SqrtDoubleGradKernel<plat::CPUDeviceContext,
                              ops::SqrtGradGradFunctor<double>>,
    ops::SqrtDoubleGradKernel<plat::CPUDeviceContext,
                              ops::SqrtGradGradFunctor<plat::float16>>);
/* ========================================================================== */

/* ==========================   square register  ============================ */
REGISTER_OPERATOR(
    square, ops::ActivationOp, ops::SquareOpMaker,
    ops::ActivationOpInferVarType,
    ops::ActivationGradOpMaker<ops::SquareGradFunctor<float>::FwdDeps(),
                               paddle::framework::OpDesc>,
    ops::ActivationGradOpMaker<ops::SquareGradFunctor<float>::FwdDeps(),
                               paddle::imperative::OpBase>,
    ops::ActFwdInplaceInferer);
REGISTER_OPERATOR(square_grad, ops::ActivationOpGrad,
                  ops::ActivationGradOpInplaceInference,
                  ops::SquareDoubleGradMaker<paddle::framework::OpDesc>,
                  ops::SquareDoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(
    square_grad_grad,
    ops::ActivationOpDoubleGrad<ops::SquareGradGradFunctor<float>::FwdDeps()>,
    ops::ActivationDoubleGradOpInplaceInference);

REGISTER_ACTIVATION_CPU_KERNEL(square, Square, SquareFunctor,
                               SquareGradFunctor);

REGISTER_OP_CPU_KERNEL(
    square_grad_grad,
    ops::SquareDoubleGradKernel<plat::CPUDeviceContext,
                                ops::SquareGradGradFunctor<float>>,
    ops::SquareDoubleGradKernel<plat::CPUDeviceContext,
                                ops::SquareGradGradFunctor<double>>,
    ops::SquareDoubleGradKernel<plat::CPUDeviceContext,
                                ops::SquareGradGradFunctor<plat::float16>>);
/* ========================================================================== */

/* ==========================   pow register  ============================ */

REGISTER_OPERATOR(
    pow, ops::PowOp, ops::PowOpMaker, ops::ActivationOpInferVarType,
    ops::PowGradOpMaker<paddle::framework::OpDesc>,
    ops::PowGradOpMaker<paddle::imperative::OpBase>,
    std::conditional<ops::CanInplaceAct<ops::PowGradFunctor<float>>(),
                     ops::ActFwdInplaceInferer, void>::type);
REGISTER_OPERATOR(pow_grad, ops::PowOpGrad,
                  ops::ActivationGradOpInplaceInference);

REGISTER_OP_CPU_KERNEL(
    pow, ops::PowKernel<plat::CPUDeviceContext, ops::PowFunctor<float>>,
    ops::PowKernel<plat::CPUDeviceContext, ops::PowFunctor<double>>);
REGISTER_OP_CPU_KERNEL(
    pow_grad,
    ops::PowGradKernel<plat::CPUDeviceContext, ops::PowGradFunctor<float>>,
    ops::PowGradKernel<plat::CPUDeviceContext, ops::PowGradFunctor<double>>);
/* ========================================================================== */