/* 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. */

#include <memory>
#include <string>

#include "paddle/fluid/operators/elementwise/elementwise_npu.h"
#include "paddle/fluid/platform/device/npu/npu_op_runner.h"

namespace paddle {
namespace operators {

template <typename DeviceContext, typename T>
class ElementwisePowNPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto& dev_ctx =
        ctx.template device_context<paddle::platform::NPUDeviceContext>();

    auto* x = ctx.Input<phi::DenseTensor>("X");
    auto* y = ctx.Input<phi::DenseTensor>("Y");
    auto* out = ctx.Output<phi::DenseTensor>("Out");

    auto place = ctx.GetPlace();
    int axis = ctx.Attr<int>("axis");

    out->mutable_data<T>(place);

    bool direct_compute = false;
    auto x_dims = x->dims();
    auto y_dims = y->dims();
    axis =
        (axis < 0 ? std::abs(x_dims.size() - y_dims.size()) + axis + 1 : axis);
    if (x_dims.size() >= y_dims.size()) {
      direct_compute = y_dims == phi::slice_ddim(x_dims, axis, x_dims.size());
    } else {
      direct_compute = x_dims == phi::slice_ddim(y_dims, axis, y_dims.size());
    }

    auto stream = dev_ctx.stream();

    if (direct_compute) {
      const auto& runner = NpuOpRunner("Pow", {*x, *y}, {*out}, {});
      runner.Run(stream);
    } else {
      phi::DenseTensor transformed_x, transformed_y;
      NpuElementWiseOpBroadcast<T>(
          dev_ctx, x, y, axis, &transformed_x, &transformed_y);
      const auto& runner =
          NpuOpRunner("Pow", {transformed_x, transformed_y}, {*out}, {});
      runner.Run(stream);
    }
  }
};

template <typename DeviceContext, typename T>
class ElementwisePowGradNPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto& dev_ctx =
        ctx.template device_context<paddle::platform::NPUDeviceContext>();
    auto* x = ctx.Input<phi::DenseTensor>("X");
    auto* y = ctx.Input<phi::DenseTensor>("Y");
    auto* dout = ctx.Input<phi::DenseTensor>(framework::GradVarName("Out"));
    auto* dx = ctx.Output<phi::DenseTensor>(framework::GradVarName("X"));
    auto* dy = ctx.Output<phi::DenseTensor>(framework::GradVarName("Y"));
    int axis = ctx.Attr<int>("axis");
    auto place = ctx.GetPlace();

    auto x_dims = x->dims();
    auto y_dims = y->dims();
    axis =
        (axis < 0 ? std::abs(x_dims.size() - y_dims.size()) + axis + 1 : axis);
    phi::DenseTensor transformed_x, transformed_y;
    NpuElementWiseOpBroadcast<T>(
        dev_ctx, x, y, axis, &transformed_x, &transformed_y);

    auto dout_dims = dout->dims();
    auto stream = dev_ctx.stream();
    // Reshape info vector.
    std::vector<int> reduce_axes;
    if (dx) {
      phi::DenseTensor zero_tensor(dout->type());
      zero_tensor.mutable_data<T>(dout_dims, place);
      FillNpuTensorWithConstant<T>(&zero_tensor, static_cast<T>(0));

      dx->mutable_data<T>(place);
      phi::DenseTensor tmp_dx;
      tmp_dx.mutable_data<T>(dout_dims, place);

      // dx = dout * y * pow(x, y - 1);
      phi::DenseTensor PowGrad_dx_temp1(dout->type());
      PowGrad_dx_temp1.mutable_data<T>(dout->dims(), place);
      const auto& runner_PowGrad_dx_temp1 =
          NpuOpRunner("Mul", {*dout, transformed_y}, {PowGrad_dx_temp1}, {});
      runner_PowGrad_dx_temp1.Run(stream);

      phi::DenseTensor one_dx(transformed_y.type());
      one_dx.mutable_data<T>(transformed_y.dims(), place);
      const auto& runner_one_dx =
          NpuOpRunner("OnesLike", {transformed_y}, {one_dx}, {});
      runner_one_dx.Run(stream);

      phi::DenseTensor sub_dx(transformed_y.type());
      sub_dx.mutable_data<T>(transformed_y.dims(), place);
      const auto& runner_sub_dx =
          NpuOpRunner("Sub", {transformed_y, one_dx}, {sub_dx}, {});
      runner_sub_dx.Run(stream);

      phi::DenseTensor PowGrad_dx_temp2(transformed_x.type());
      PowGrad_dx_temp2.mutable_data<T>(transformed_x.dims(), place);
      const auto& runner_PowGrad_dx_temp2 =
          NpuOpRunner("Pow", {transformed_x, sub_dx}, {PowGrad_dx_temp2}, {});
      runner_PowGrad_dx_temp2.Run(stream);

      const auto& runner_dx = NpuOpRunner(
          "Mul", {PowGrad_dx_temp1, PowGrad_dx_temp2}, {tmp_dx}, {});
      runner_dx.Run(stream);

      if (x_dims != dout_dims) {
        reduce_axes.clear();

        int src_axis = (x_dims.size() < dout_dims.size() ? axis : 0);
        for (int ax = 0; ax < dout_dims.size(); ++ax) {
          if ((ax < src_axis || ax >= src_axis + x_dims.size()) ||
              (dout_dims[ax] > 1 && x_dims[ax - src_axis] == 1)) {
            reduce_axes.push_back(ax);
          }
        }
        if (!reduce_axes.empty()) {
          const auto& runner =
              NpuOpRunner("ReduceSumD",
                          {tmp_dx},
                          {*dx},
                          {{"axes", reduce_axes}, {"keep_dims", false}});
          runner.Run(stream);
        }
      } else {
        framework::TensorCopy(tmp_dx, place, dev_ctx, dx);
      }
    }
    if (dy) {
      phi::DenseTensor zero_tensor(dout->type());
      zero_tensor.mutable_data<T>(dout_dims, place);
      FillNpuTensorWithConstant<T>(&zero_tensor, static_cast<T>(0));

      dy->mutable_data<T>(place);
      phi::DenseTensor tmp_dy;
      tmp_dy.mutable_data<T>(dout_dims, place);

      // dy = dout * log(x) * pow(x, y)
      phi::DenseTensor PowGrad_dy_temp1(transformed_x.type());
      PowGrad_dy_temp1.mutable_data<T>(transformed_x.dims(), place);
      const auto& runner_PowGrad_dy_temp1 = NpuOpRunner(
          "Pow", {transformed_x, transformed_y}, {PowGrad_dy_temp1}, {});
      runner_PowGrad_dy_temp1.Run(stream);

      phi::DenseTensor one_dy(transformed_x.type());
      one_dy.mutable_data<T>(transformed_x.dims(), place);
      const auto& runner_one_dy =
          NpuOpRunner("OnesLike", {transformed_x}, {one_dy}, {});
      runner_one_dy.Run(stream);

      phi::DenseTensor sub_dy(transformed_x.type());
      sub_dy.mutable_data<T>(transformed_x.dims(), place);
      const auto& runner_sub_dy =
          NpuOpRunner("Sub", {transformed_x, one_dy}, {sub_dy}, {});
      runner_sub_dy.Run(stream);

      phi::DenseTensor log_dy(transformed_x.type());
      log_dy.mutable_data<T>(transformed_x.dims(), place);
      const auto& runner_log_dy = NpuOpRunner("Log1p", {sub_dy}, {log_dy}, {});
      runner_log_dy.Run(stream);

      phi::DenseTensor PowGrad_dy_temp2(transformed_x.type());
      PowGrad_dy_temp2.mutable_data<T>(transformed_x.dims(), place);
      const auto& runner_PowGrad_dy_temp2 = NpuOpRunner(
          "Mul", {log_dy, PowGrad_dy_temp1}, {PowGrad_dy_temp2}, {});
      runner_PowGrad_dy_temp2.Run(stream);

      const auto& runner_dy =
          NpuOpRunner("Mul", {*dout, PowGrad_dy_temp2}, {tmp_dy}, {});
      runner_dy.Run(stream);

      if (y_dims != dout_dims) {
        reduce_axes.clear();

        int src_axis = (y_dims.size() < dout_dims.size() ? axis : 0);
        for (int ax = 0; ax < dout_dims.size(); ++ax) {
          if ((ax < src_axis || ax >= src_axis + y_dims.size()) ||
              (dout_dims[ax] > 1 && y_dims[ax - src_axis] == 1)) {
            reduce_axes.push_back(ax);
          }
        }
        if (!reduce_axes.empty()) {
          const auto& runner =
              NpuOpRunner("ReduceSumD",
                          {tmp_dy},
                          {*dy},
                          {{"axes", reduce_axes}, {"keep_dims", false}});
          runner.Run(stream);
        }
      } else {
        framework::TensorCopy(tmp_dy, place, dev_ctx, dy);
      }
    }
    if (!dx && !dy) {
      PADDLE_THROW(platform::errors::Unavailable(
          "Not support all outputs to be empty."));
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
namespace plat = paddle::platform;

REGISTER_OP_NPU_KERNEL(
    elementwise_pow,
    ops::ElementwisePowNPUKernel<plat::NPUDeviceContext, plat::float16>,
    ops::ElementwisePowNPUKernel<plat::NPUDeviceContext, float>,
    ops::ElementwisePowNPUKernel<plat::NPUDeviceContext, double>,
    ops::ElementwisePowNPUKernel<plat::NPUDeviceContext, int>);

REGISTER_OP_NPU_KERNEL(
    elementwise_pow_grad,
    ops::ElementwisePowGradNPUKernel<plat::NPUDeviceContext, plat::float16>,
    ops::ElementwisePowGradNPUKernel<plat::NPUDeviceContext, float>,
    ops::ElementwisePowGradNPUKernel<plat::NPUDeviceContext, double>,
    ops::ElementwisePowGradNPUKernel<plat::NPUDeviceContext, int>);
