/* Copyright (c) 2021 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. */

#ifdef PADDLE_WITH_ASCEND_CL
#include <memory>
#include <string>

#include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename DeviceContext, typename T>
class ElementwiseDivNPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* x = ctx.Input<Tensor>("X");
    auto* y = ctx.Input<Tensor>("Y");

    auto* out = ctx.Output<Tensor>("Out");

    auto place = ctx.GetPlace();

    out->mutable_data<T>(place);

    auto stream =
        ctx.template device_context<paddle::platform::NPUDeviceContext>()
            .stream();

    auto runner = NpuOpRunner("Div", {*x, *y}, {*out}, {});
    runner.Run(stream);
  }
};

template <typename DeviceContext, typename T>
class ElementwiseDivGradNPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* out = ctx.Input<Tensor>("Out");
    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto* x = ctx.Input<Tensor>("X");
    auto* y = ctx.Input<Tensor>("Y");

    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));

    auto place = ctx.GetPlace();

    auto stream =
       ctx.template device_context<paddle::platform::NPUDeviceContext>()
           .stream();

    Tensor y_power(y->type());
    y_power.mutable_data<T>(y->dims(), place);
    auto y_power_runner = NpuOpRunner("Power", {*y},
             {y_power}, {{"power", static_cast<float>(-1)}});
    y_power_runner.Run(stream);

    if (dx) {
      dx->mutable_data<T>(place);

      Tensor tensor_zeros(x->type());
      tensor_zeros.mutable_data<T>(x->dims(), place);
      auto tensor_zeros_runner = NpuOpRunner("ZerosLike", {*x},
               {tensor_zeros}, {});
      tensor_zeros_runner.Run(stream);

      Tensor x_zero(paddle::framework::proto::VarType::BOOL);
      x_zero.mutable_data<bool>(x->dims(), place);
      auto x_zero_runner = NpuOpRunner("Equal", {*x, tensor_zeros},
              {x_zero}, {});
      x_zero_runner.Run(stream);

      Tensor x_nozero(paddle::framework::proto::VarType::BOOL);
      x_nozero.mutable_data<bool>(x->dims(), place);
      auto x_nozero_runner = NpuOpRunner("LogicalNot", {x_zero},
              {x_nozero}, {});
      x_nozero_runner.Run(stream);

      Tensor x_nozero_f(x->type());
      x_nozero_f.mutable_data<T>(x->dims(), place);
      auto x_nozero_f_runner = NpuOpRunner("Cast", {x_nozero},
               {x_nozero_f}, {{"dst_type", static_cast<int32_t>(0)}});
      x_nozero_f_runner.Run(stream);

      Tensor x_grad_w(x->type());
      x_grad_w.mutable_data<T>(x->dims(), place);
      auto x_grad_w_runner = NpuOpRunner("Mul", {x_nozero_f, y_power},
               {x_grad_w}, {});
      x_grad_w_runner.Run(stream);

      auto x_grad_runner = NpuOpRunner("Mul", {x_grad_w, *dout}, {*dx}, {});
      x_grad_runner.Run(stream);
    }

    if (dy) {
      dy->mutable_data<T>(place);

      Tensor neg_out(y->type());
      neg_out.mutable_data<T>(y->dims(), place);
      auto neg_out_runner = NpuOpRunner("Neg", {*out},
              {neg_out}, {});
      neg_out_runner.Run(stream);

      Tensor y_grad_w(y->type());
      y_grad_w.mutable_data<T>(y->dims(), place);
      auto y_grad_w_runner = NpuOpRunner("Div", {neg_out, *y},
              {y_grad_w}, {});
      y_grad_w_runner.Run(stream);

      auto y_grad_runner = NpuOpRunner("Mul", {y_grad_w, *dout}, {*dy}, {});
      y_grad_runner.Run(stream);
    }
  }
};

}  // namespace operators
}  // namespace paddle
namespace ops = paddle::operators;

REGISTER_OP_NPU_KERNEL(
    elementwise_div,
    ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext, float>,
    ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext,
                                 paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
    elementwise_div_grad,
    ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
    ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext,
                                     paddle::platform::float16>);
#endif