clip_op.cc

// Copyright (c) 2022 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 "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/unary.h"

namespace paddle {
namespace operators {

class ClipOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  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;
DECLARE_INFER_SHAPE_FUNCTOR(clip, ClipInferShapeFunctor,
                            PD_INFER_META(phi::UnchangedInferMeta));
REGISTER_OPERATOR(clip, ops::ClipOp, ops::ClipOpMaker<float>,
                  ops::ClipGradOpMaker<paddle::framework::OpDesc>,
                  ops::ClipGradOpMaker<paddle::imperative::OpBase>,
                  ops::ClipInplaceInferer, ClipInferShapeFunctor);
REGISTER_OPERATOR(clip_grad, ops::ClipOpGrad, ops::ClipGradInplaceInferer,
                  ops::ClipDoubleGradOpMaker<paddle::framework::OpDesc>,
                  ops::ClipDoubleGradOpMaker<paddle::imperative::OpBase>);

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."));