paddlepaddle--paddle/paddle/fluid/operators/dropout_op.cc

/* Copyright (c) 2016 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 <memory>
#include <string>

#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/prim/api/composite_backward/composite_backward_api.h"
#include "paddle/fluid/prim/utils/static/composite_grad_desc_maker.h"
#include "paddle/phi/infermeta/binary.h"

namespace paddle {
namespace operators {

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

 protected:
  phi::KernelKey GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "X"),
                          ctx.GetPlace());
  }

  phi::KernelKey GetKernelTypeForVar(
      const std::string& var_name,
      const phi::DenseTensor& tensor,
      const phi::KernelKey& expected_kernel_type) const override {
    if (var_name == "Seed") {
      VLOG(10) << "var_name:" << var_name
               << " does not need to transform in dropout op";
      return phi::KernelKey(phi::Backend::ALL_BACKEND,
                            expected_kernel_type.layout(),
                            expected_kernel_type.dtype());
    }

    return phi::KernelKey(
        tensor.place(), tensor.layout(), expected_kernel_type.dtype());
  }
};

class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "The input of dropout op.");
    AddInput("Seed",
             "The seed of dropout op, it has higher priority than the attr "
             "fix_seed and seed")
        .AsDispensable()
        .AsExtra();
    AddOutput("Out", "The output of dropout op.");
    AddOutput("Mask", "The random sampled dropout mask.")
        .AsIntermediate()
        .AsExtra();

    AddAttr<float>("dropout_prob", "Probability of setting units to zero.")
        .SetDefault(.5f)
        .AddCustomChecker([](const float& drop_p) {
          PADDLE_ENFORCE_EQ(drop_p >= 0.0f && drop_p <= 1.0f,
                            true,
                            common::errors::InvalidArgument(
                                "'dropout_prob' must be between 0.0 and 1.0."));
        })
        .SupportTensor();
    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);
    AddAttr<std::string>(
        "dropout_implementation",
        "[\"downgrade_in_infer\"|\"upscale_in_train\"]"
        "There are two kinds of ways to implement dropout"
        "(the mask below is a tensor have the same shape with input"
        "the value of mask is 0 or 1, the ratio of 0 is dropout_prob)"
        "1. downgrade_in_infer(default), downgrade the outcome at inference "
        "time"
        "   train: out = input * mask"
        "   inference: out = input * (1.0 - dropout_prob)"
        "2. upscale_in_train, upscale the outcome at training time, do nothing "
        "in inference"
        "   train: out = input * mask / ( 1.0 - dropout_prob )"
        "   inference: out = input"
        "   dropout op can be removed from the program. the program will be "
        "efficient")
        .SetDefault("downgrade_in_infer")
        .AddCustomChecker([](const std::string& type) {
          PADDLE_ENFORCE_EQ(
              type == "downgrade_in_infer" || type == "upscale_in_train",
              true,
              common::errors::InvalidArgument(
                  "dropout_implementation can only be downgrade_in_infer or "
                  "upscale_in_train"));
        });

    AddComment(R"DOC(
Dropout Operator.

Dropout refers to randomly dropping out units in a neural network. It is a
regularization technique for reducing overfitting by preventing neuron
co-adaption during training. The dropout operator randomly set (according to
the given dropout probability) the outputs of some units to zero, while others
are set equal to their corresponding inputs.

)DOC");
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("Mask"), "Input", "Mask", "DropoutGrad");
    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")),
                   "Input",
                   framework::GradVarName("Out"),
                   "DropoutGrad");

    auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));

    ctx->SetOutputDim(framework::GradVarName("X"), out_dims);
    ctx->ShareLoD(framework::GradVarName("Out"),
                  /*->*/ framework::GradVarName("X"));
  }

 protected:
  phi::KernelKey GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(
                              ctx, framework::GradVarName("Out")),
                          ctx.GetPlace());
  }
};

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

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("dropout_grad");
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetInput("Mask", this->Output("Mask"));
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetAttrMap(this->Attrs());
  }
};

class DropoutCompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
  using prim::CompositeGradOpMakerBase::CompositeGradOpMakerBase;

 public:
  void Apply() override {
    auto mask = this->GetSingleForwardOutput("Mask");
    auto out_grad = this->GetSingleOutputGrad("Out");
    auto x_grad = this->GetSingleInputGrad("X");
    auto x_grad_p = this->GetOutputPtr(&x_grad);
    auto x_grad_name = this->GetOutputName(x_grad);
    auto p = this->Attr<float>("dropout_prob");
    auto is_test = this->Attr<bool>("is_test");
    auto mode = this->Attr<std::string>("dropout_implementation");
    prim::dropout_grad<prim::DescTensor>(
        mask, out_grad, p, is_test, mode, x_grad_p);
    VLOG(3) << "Running dropout_grad composite func";
    this->RecoverOutputName(x_grad, x_grad_name);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

DECLARE_INFER_SHAPE_FUNCTOR(dropout,
                            DropoutInferShapeFunctor,
                            PD_INFER_META(phi::DropoutInferMeta));
REGISTER_OPERATOR(dropout,
                  ops::DropoutOp,
                  ops::DropoutOpMaker,
                  ops::DropoutCompositeGradOpMaker,
                  ops::DropoutGradOpMaker<paddle::framework::OpDesc>,
                  ops::DropoutGradOpMaker<paddle::imperative::OpBase>,
                  DropoutInferShapeFunctor);
REGISTER_OPERATOR(dropout_grad, ops::DropoutOpGrad);