/* Copyright (c) 2020 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/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/segment_pooling.h"
#include "paddle/fluid/platform/macros.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename DeviceContext, typename T, typename IndexT>
void SegmentKernelLaunchHelper(const framework::ExecutionContext& context) {
  auto* input = context.Input<Tensor>("X");
  auto* segment = context.Input<Tensor>("SegmentIds");
  auto* output = context.Output<Tensor>("Out");
  std::string pooltype = context.Attr<std::string>("pooltype");
  Tensor* summed_ids = nullptr;

  int64_t num_indices = segment->numel();
  PADDLE_ENFORCE_EQ(
      num_indices, input->dims()[0],
      platform::errors::InvalidArgument(
          "Segment_ids should be the same size as dimension 0 of input X."));
  PADDLE_ENFORCE_EQ(num_indices, segment->dims()[0],
                    platform::errors::InvalidArgument(
                        "Segment_ids should be 1-D tensor, or it's other "
                        "dimension size is 1. Segment_ids's shape is: [%s].",
                        segment->dims()));

  if (input->numel() == 0 || segment->numel() == 0) {
    return;
  }

  bool cpu_place = context.GetPlace().type() == typeid(platform::CPUPlace);
  if (cpu_place) {
    auto dims = input->dims();
    auto* segment_ids = segment->data<IndexT>();
    dims[0] = static_cast<int64_t>(segment_ids[segment->numel() - 1] + 1);
    PADDLE_ENFORCE_GT(
        dims[0], 0,
        platform::errors::InvalidArgument(
            "Segment ids must be >= 0, but got last id %d", dims[0]));
    output->Resize({dims});
    output->mutable_data<T>(context.GetPlace());
    math::SetConstant<DeviceContext, T> set_zero;
    auto& dev_ctx = context.template device_context<DeviceContext>();
    set_zero(dev_ctx, output, static_cast<T>(0));
  }
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
  if (!cpu_place) {
    Tensor length;
    length.mutable_data<IndexT>(framework::make_ddim({1}),
                                platform::CPUPlace());
    IndexT* length_data = length.data<IndexT>();
    const IndexT* segment_ids = segment->data<IndexT>();

#ifdef PADDLE_WITH_HIP
    PADDLE_ENFORCE_GPU_SUCCESS(
        hipMemcpy(length_data, segment_ids + num_indices - 1, sizeof(IndexT),
                  hipMemcpyDeviceToHost));
#else
    PADDLE_ENFORCE_GPU_SUCCESS(
        cudaMemcpy(length_data, segment_ids + num_indices - 1, sizeof(IndexT),
                   cudaMemcpyDeviceToHost));
#endif

    IndexT length_host = length_data[0];
    length_host++;
    PADDLE_ENFORCE_GT(
        length_host, 0,
        platform::errors::InvalidArgument(
            "Segment ids must be >= 0, but got last id %d", length_data[0]));
    auto dims = input->dims();
    dims[0] = static_cast<int64_t>(length_host);
    output->Resize({dims});
    output->mutable_data<T>(context.GetPlace());
    T init_value = 0;
    if (pooltype == "MAX") {
      init_value = static_cast<T>(-FLT_MAX);
    } else if (pooltype == "MIN") {
      init_value = static_cast<T>(FLT_MAX);
    }
    math::SetConstant<DeviceContext, T> setconst;
    auto& dev_ctx = context.template device_context<DeviceContext>();
    setconst(dev_ctx, output, static_cast<T>(init_value));
    // the gpu kernel of mean pool record the counts of segment_ids
    if (pooltype == "MEAN") {
      summed_ids = context.Output<Tensor>("SummedIds");
      summed_ids->Resize({dims[0], 1});
      summed_ids->mutable_data<T>(context.GetPlace());
      setconst(dev_ctx, summed_ids, static_cast<T>(1e-12));
    }
  }
#endif

  SegmentPoolFunctor<DeviceContext, T, IndexT> pool;

  pool(context.template device_context<DeviceContext>(), *input, *segment,
       output, summed_ids, pooltype);
}

template <typename DeviceContext, typename T>
class SegmentPoolKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* segment = context.Input<Tensor>("SegmentIds");
    auto index_type = segment->type();
    if (index_type == framework::proto::VarType::INT32) {
      SegmentKernelLaunchHelper<DeviceContext, T, int>(context);
    } else if (index_type == framework::proto::VarType::INT64) {
      SegmentKernelLaunchHelper<DeviceContext, T, int64_t>(context);
    } else {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "Unsupported index type, Expected int, int64, but got %s.",
          index_type));
    }
  }
};

template <typename DeviceContext, typename T>
class SegmentPoolGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* input = context.Input<Tensor>("X");
    auto* output = context.Input<Tensor>("Out");
    auto* segment = context.Input<Tensor>("SegmentIds");
    auto* out_g = context.Input<Tensor>(framework::GradVarName("Out"));
    auto* in_g = context.Output<Tensor>(framework::GradVarName("X"));
    std::string pooltype = context.Attr<std::string>("pooltype");

    const Tensor* summed_ids = nullptr;
    if (pooltype == "MEAN") {
      summed_ids = context.Input<Tensor>("SummedIds");
    }

    in_g->mutable_data<T>(context.GetPlace());
    math::SetConstant<DeviceContext, T> set_zero;
    auto& dev_ctx = context.template device_context<DeviceContext>();
    set_zero(dev_ctx, in_g, static_cast<T>(0));

    auto index_type = segment->type();
    if (index_type == framework::proto::VarType::INT32) {
      SegmentPoolGradFunctor<DeviceContext, T, int> pool;
      pool(context.template device_context<DeviceContext>(), *input, *output,
           *out_g, *segment, in_g, summed_ids, pooltype);
    } else if (index_type == framework::proto::VarType::INT64) {
      SegmentPoolGradFunctor<DeviceContext, T, int64_t> pool;
      pool(context.template device_context<DeviceContext>(), *input, *output,
           *out_g, *segment, in_g, summed_ids, pooltype);
    } else {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "Unsupported index type, Expected int, int64, but got %s.",
          index_type));
    }
  }
};

}  // namespace operators
}  // namespace paddle
