/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   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/operators/modified_huber_loss_op.h"

namespace paddle {
namespace operators {

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

 protected:
  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");

    auto x_dims = ctx->GetInputDim("X");
    auto y_dims = ctx->GetInputDim("Y");

    PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "The tensor rank of X must be 2.");
    PADDLE_ENFORCE_EQ(x_dims[1], 1, "The 2nd dimension of X must be 1.");

    ctx->SetOutputDim("IntermediateVal", x_dims);
    ctx->SetOutputDim("Out", {x_dims[0], 1});
  }
};

class ModifiedHuberLossOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  ModifiedHuberLossOpMaker(framework::OpProto* proto,
                           framework::OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X",
             "The input tensor of modified huber loss op."
             "X is 2-D tensor with shape [batch_size, 1].");
    AddInput("Y",
             "The target labels of modified huber loss op."
             "The shape of Y is same as X. Values of Y must be 0 or 1.");
    AddOutput("IntermediateVal",
              "Variable to save intermediate result which will be reused in "
              "backward processing.")
        .AsIntermediate();
    AddOutput("Out", "Classification loss for X.");
    AddComment(R"DOC(
Modified huber loss is used in binary classification problem. The shape of
input X and target Y are both [N, 1] and so is the shape of output loss.
Since target Y is not differentiable, cacluating gradient for Y is illegal.
The formulation of modified huber loss is:

L(y, f(x)) = max(0, 1 - yf(x))^2  for yf(x) >= -1,
             -4yf(x)              otherwise.

Make sure the values of target label Y are in {0, 1} here. The operator will
scale values of Y to {-1, +1} when computing losses and gradients.
)DOC");
  }
};

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

 protected:
  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
    PADDLE_ENFORCE(ctx->HasInput("IntermediateVal"),
                   "Intermediate value must not be null.");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                   "Input(Out@Grad) must not be null.");

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

    PADDLE_ENFORCE_EQ(
        intermediate_dims, x_dims,
        "The shape of X and intermediate value must be the same.");
    PADDLE_ENFORCE_EQ(out_grad_dims, x_dims,
                      "The shape of Input(Out@Grad) and X must be the same.");

    if (ctx->HasOutput(framework::GradVarName("X"))) {
      ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(modified_huber_loss, ops::ModifiedHuberLossOp,
            ops::ModifiedHuberLossOpMaker, modified_huber_loss_grad,
            ops::ModifiedHuberLossGradOp);

REGISTER_OP_CPU_KERNEL(
    modified_huber_loss,
    ops::ModifiedHuberLossKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(modified_huber_loss_grad,
                       ops::ModifiedHuberLossGradCPUKernel<float>);