clip_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 "paddle/fluid/operators/clip_op.h"
#include <memory>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"

namespace paddle {
namespace operators {

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "clip");
    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "clip");
    auto x_dims = ctx->GetInputDim("X");
    ctx->SetOutputDim("Out", x_dims);
    ctx->ShareLoD("X", /*->*/ "Out");
  }

  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    auto input_data_type =
        framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");

#ifdef PADDLE_WITH_MKLDNN
    if (this->CanMKLDNNBeUsed(ctx, input_data_type)) {
      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
                                     framework::DataLayout::kMKLDNN,
                                     framework::LibraryType::kMKLDNN);
    }
#endif
    return framework::OpKernelType(input_data_type, ctx.GetPlace());
  }
};

template <typename AttrType>
class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "Tensor, the input of clip op, data type should be float32 or "
             "float64.");
    AddInput("Min",
             "Tensor, the lower bound, data type should be float32 "
             "or float64.")
        .AsDispensable();
    AddInput("Max",
             "Tensor, the upper bound, data type should be float32 "
             "or float64.")
        .AsDispensable();
    AddOutput(
        "Out",
        "Tensor, the clipped tensor, with the same shape and data type as "
        "input(x)");
    AddAttr<AttrType>("min", "float number, the minimum value to clip by.");
    AddAttr<AttrType>("max", "float number, the maximum value to clip by.");
    AddAttr<bool>("use_mkldnn",
                  "(bool, default false) Only used in mkldnn kernel")
        .SetDefault(false)
        .AsExtra();
    AddAttr<std::string>(
        "mkldnn_data_type",
        "(string, default \"float32\"). Data type of mkldnn kernel")
        .SetDefault("float32")
        .InEnum({"float32", "bfloat16"})
        .AsExtra();
    AddComment(R"DOC(
Clip Operator.

The clip operator limits the value of given input within an interval [min, max],
just as the following equation,

$$
Out = \MIN(\MAX(x, min), max)
$$

)DOC");
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "clip_grad");
    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
                   "Out@GRAD", "clip_grad");
    auto x_dims = ctx->GetInputDim("X");
    if (ctx->HasOutput(framework::GradVarName("X"))) {
      ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
    }
  }

  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    auto input_data_type = OperatorWithKernel::IndicateVarDataType(
        ctx, framework::GradVarName("Out"));

#ifdef PADDLE_WITH_MKLDNN
    if (this->CanMKLDNNBeUsed(ctx, input_data_type)) {
      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
                                     framework::DataLayout::kMKLDNN,
                                     framework::LibraryType::kMKLDNN);
    }
#endif
    return framework::OpKernelType(input_data_type, ctx.GetPlace());
  }
};

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

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("clip_grad");
    op->SetInput("X", this->Input("X"));
    if (this->HasInput("Min")) {
      op->SetInput("Min", this->Input("Min"));
    }
    if (this->HasInput("Max")) {
      op->SetInput("Max", this->Input("Max"));
    }
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetAttrMap(this->Attrs());
  }
};

DECLARE_INPLACE_OP_INFERER(ClipInplaceInferer, {"X", "Out"});
DECLARE_INPLACE_OP_INFERER(ClipGradInplaceInferer,
                           {framework::GradVarName("Out"),
                            framework::GradVarName("X")});

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

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("clip_grad");
    op->SetInput("X", this->Input("X"));
    if (this->HasInput("Min")) {
      op->SetInput("Min", this->Input("Min"));
    }
    if (this->HasInput("Max")) {
      op->SetInput("Max", this->Input("Max"));
    }
    op->SetInput(framework::GradVarName("Out"),
                 this->OutputGrad(framework::GradVarName("X")));
    op->SetOutput(framework::GradVarName("X"),
                  this->InputGrad(framework::GradVarName("Out")));
    op->SetAttrMap(this->Attrs());
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(clip, ops::ClipOp, ops::ClipOpMaker<float>,
                  ops::ClipGradOpMaker<paddle::framework::OpDesc>,
                  ops::ClipGradOpMaker<paddle::imperative::OpBase>,
                  ops::ClipInplaceInferer);
REGISTER_OPERATOR(clip_grad, ops::ClipOpGrad, ops::ClipGradInplaceInferer,
                  ops::ClipDoubleGradOpMaker<paddle::framework::OpDesc>,
                  ops::ClipDoubleGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(
    clip, ops::ClipKernel<paddle::platform::CPUDeviceContext, float>,
    ops::ClipKernel<paddle::platform::CPUDeviceContext, double>,
    ops::ClipKernel<paddle::platform::CPUDeviceContext, int>,
    ops::ClipKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
    clip_grad, ops::ClipGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::ClipGradKernel<paddle::platform::CPUDeviceContext, double>,
    ops::ClipGradKernel<paddle::platform::CPUDeviceContext, int>,
    ops::ClipGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

REGISTER_OP_VERSION(clip)
    .AddCheckpoint(
        R"ROC(
              Upgrade clip add a new input [Min])ROC",
        paddle::framework::compatible::OpVersionDesc()
            .NewInput("Min",
                      "Pass the mix, min value as input, not attribute. Min is "
                      "dispensable.")
            .NewInput("Max",
                      "Pass the mix, min value as input, not attribute. Max is "
                      "dispensable."));