elementwise_op.h

/* 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. */

#pragma once
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"

namespace paddle {
namespace operators {

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

  using Tensor = framework::Tensor;
  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of elementwise op should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Y"),
                   "Input(Y) of elementwise op should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of elementwise op should not be null.");

    auto x_dim = ctx->GetInputDim("X");
    auto y_dim = ctx->GetInputDim("Y");
    PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
                      "Rank of first input must >= rank of second input.");
    ctx->SetOutputDim("Out", x_dim);
    ctx->ShareLoD("X", /*->*/ "Out");
  }
};

class ElementwiseOpInferVarType : public framework::VarTypeInference {
 public:
  void operator()(const framework::OpDesc& op_desc,
                  framework::BlockDesc* block) const override {
    auto x_var = op_desc.Input("X")[0];
    auto out_var = op_desc.Output("Out")[0];
    block->Var(out_var)->SetType(block->Var(x_var)->GetType());
  }
};

class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() final {
    AddInput("X", "(Tensor), The first input tensor of elementwise op.");
    AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
    AddOutput("Out", "The output of elementwise op.");
    AddAttr<int>("axis",
                 "(int, default -1). The start dimension index "
                 "for broadcasting Y onto X.")
        .SetDefault(-1)
        .EqualGreaterThan(-1);
    AddComment(string::Sprintf(R"DOC(
Limited Elementwise %s Operator.

The equation is:

$$%s$$

$X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be
smaller than or equal to the dimensions of $X$.

There are two cases for this operator:
1. The shape of $Y$ is same with $X$;
2. The shape of $Y$ is a congiguous subsequencet of $X$. The trailing dimensions
   of size 1 for $Y$ will be ignored for the consideration of subsequence.


For case 2:

$Y$ will be broadcasted to match the shape of $X$ and axis should be
set to index of the start dimension to broadcast $Y$ onto $X$.

If axis is -1, it is treated as axis=rank(X)-rank(Y).

For example
  .. code-block:: python

    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
    shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
    shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
    shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
    shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
    shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details)
information. However, the output only shares the LoD information with input $X$.

)DOC",
                               GetName(), GetEquation()));
  }

 protected:
  virtual std::string GetName() const = 0;
  virtual std::string GetEquation() const = 0;
};

class ElementwiseOpGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  using Tensor = framework::Tensor;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                   "Input(Out@GRAD) should not be null");

    auto x_dims = ctx->GetInputDim("X");
    auto y_dims = ctx->GetInputDim("Y");
    auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));

    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
                      "Rank of first input must >= rank of second input.");

    auto x_grad_name = framework::GradVarName("X");
    auto y_grad_name = framework::GradVarName("Y");
    if (ctx->HasOutput(x_grad_name)) {
      ctx->SetOutputDim(x_grad_name, x_dims);
    }
    if (ctx->HasOutput(y_grad_name)) {
      ctx->SetOutputDim(y_grad_name, y_dims);
    }
  }
};
}  // namespace operators
}  // namespace paddle

#define REGISTER_ELEMWISE_OP(op_type, op_name, equation)                \
  class __ElemwiseOp##op_type##Maker__                                  \
      : public ::paddle::operators::ElementwiseOpMaker {                \
   protected:                                                           \
    virtual std::string GetName() const { return op_name; }             \
    virtual std::string GetEquation() const { return equation; }        \
  };                                                                    \
  REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp,        \
                    __ElemwiseOp##op_type##Maker__,                     \
                    ::paddle::operators::ElementwiseOpInferVarType,     \
                    ::paddle::framework::DefaultGradOpDescMaker<true>); \
  REGISTER_OPERATOR(op_type##_grad, ::paddle::operators::ElementwiseOpGrad)