/* 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. */
#include <algorithm>
#include <map>
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"

namespace paddle {
namespace operators {

struct CopyRange {
  size_t begin;
  size_t end;
};

struct LoDTensorToArrayFunctor;

template <typename DeviceContext>
struct LoDTensorToArrayFunctorImpl {
  const LoDTensorToArrayFunctor *prev_functor_;
  DeviceContext *dev_ctx_;
  template <typename T>
  void apply();
};

struct LoDTensorToArrayFunctor : public boost::static_visitor<void> {
  std::vector<const framework::Tensor *> ref_inputs_;
  mutable std::vector<framework::Tensor *> outputs_;
  const framework::Tensor &input_;

  explicit LoDTensorToArrayFunctor(const framework::Tensor &input)
      : input_(input) {}

  void AddOutput(framework::Tensor *t) {
    outputs_.emplace_back(t);
    ref_inputs_.emplace_back(t);
  }

  template <typename Place>
  void operator()(Place place) const {
    auto &pool = platform::DeviceContextPool::Instance();
    auto *dev_ctx = pool.Get(place);
    if (std::is_same<Place, platform::CPUPlace>::value) {
      Apply(static_cast<platform::CPUDeviceContext *>(dev_ctx));
    } else {
#ifdef PADDLE_WITH_CUDA
      Apply(static_cast<platform::CUDADeviceContext *>(dev_ctx));
#else
      PADDLE_THROW("Not compiled with cuda");
#endif
    }
  }

  template <typename DeviceContext>
  void Apply(DeviceContext *dev_ctx) const {
    LoDTensorToArrayFunctorImpl<DeviceContext> func;
    func.prev_functor_ = this;
    func.dev_ctx_ = dev_ctx;
    framework::VisitDataType(input_.type(), func);
  }
};

template <typename DeviceContext>
template <typename T>
void LoDTensorToArrayFunctorImpl<DeviceContext>::apply() {
  math::SplitFunctor<DeviceContext, T> func;
  func(*dev_ctx_, prev_functor_->input_, prev_functor_->ref_inputs_, 0,
       &prev_functor_->outputs_);
}

class LoDTensorToArrayOp : public framework::OperatorBase {
 public:
  LoDTensorToArrayOp(const std::string &type,
                     const framework::VariableNameMap &inputs,
                     const framework::VariableNameMap &outputs,
                     const framework::AttributeMap &attrs)
      : OperatorBase(type, inputs, outputs, attrs) {}

 private:
  void RunImpl(const framework::Scope &scope,
               const platform::Place &place) const override {
    auto &x = detail::Ref(scope.FindVar(Input("X")), "Cannot find input %s",
                          Input("X"))
                  .Get<framework::LoDTensor>();
    auto &rank_table = detail::Ref(scope.FindVar(Input("RankTable")))
                           .Get<framework::LoDRankTable>();
    auto &out = *detail::Ref(scope.FindVar(Output("Out")))
                     .GetMutable<framework::LoDTensorArray>();
    auto &items = rank_table.items();
    auto max_seq_len = items[0].length;
    auto rank_level = rank_table.level();

    PADDLE_ENFORCE_LT(rank_level, x.lod().size(),
                      "Input should be a LOD tensor, and size is at least %d",
                      rank_level + 1);
    out.resize(max_seq_len);
    std::vector<std::vector<CopyRange>> copy_ranges(max_seq_len);

    // set out[i] lod
    for (size_t t = 0; t < max_seq_len; t++) {
      auto &lod = *out[t].mutable_lod();
      lod.clear();
      for (auto &item : items) {
        if (t >= item.length) {
          break;
        }
        size_t start_idx = x.lod()[rank_level][item.index] + t;
        auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
            x.lod(), start_idx, start_idx + 1, rank_level + 1);
        auto &lod_length = lod_and_offset.first;
        framework::AppendLoD(&lod, lod_length);
        size_t start_offset = lod_and_offset.second.first;
        size_t end_offset = lod_and_offset.second.second;
        copy_ranges[t].emplace_back(CopyRange{start_offset, end_offset});
      }
    }

    auto &outputs = *const_cast<framework::Scope &>(scope)
                         .Var()
                         ->GetMutable<std::map<size_t, framework::Tensor>>();

    for (size_t i = 0; i < max_seq_len; ++i) {
      auto &ranges = copy_ranges[i];
      size_t height = std::accumulate(
          ranges.begin(), ranges.end(), 0UL,
          [](size_t a, const CopyRange &b) { return a + b.end - b.begin; });
      auto x_dim = x.dims();
      x_dim[0] = static_cast<int64_t>(height);
      out[i].Resize(x_dim);
      out[i].mutable_data(x.place(), x.type());
      size_t offset = 0;
      for (auto &each_range : ranges) {
        size_t len = each_range.end - each_range.begin;
        if (len == 0) {
          continue;
        }
        // out[i][offset: offset+len] = x[each_range.begin: each_range.end]
        auto slice = out[i].Slice(static_cast<int>(offset),
                                  static_cast<int>(offset + len));
        outputs.insert({each_range.begin, slice});
        offset += len;
      }
    }

    LoDTensorToArrayFunctor functor(x);
    for (auto &out_pair : outputs) {
      functor.AddOutput(&out_pair.second);
    }
    platform::VisitPlace(place, functor);
  }
};

class LoDTensorToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X", "");
    AddInput("RankTable", "");
    AddOutput("Out", "");
    AddComment("");
  }
};

class LoDTensorToArrayInferShape : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext *context) const override {
    PADDLE_ENFORCE(context->HasInput("X"),
                   "Input(X) of LoDTensorToArrayOp should not be null.");
    PADDLE_ENFORCE(
        context->HasInput("RankTable"),
        "Input(RankTable) of LoDTensorToArrayOp should not be null.");

    PADDLE_ENFORCE(context->HasOutput("Out"),
                   "Output(Out) of LoDTensorToArrayOp should not be null.");

    auto x_dim = context->GetInputDim("X");
    // The first dim of each LoDTensor in Output can only be set at run-time.;
    // We still have to Resize each LoDTensor in Output.
    context->SetOutputDim("Out", x_dim);
    // The lod level should be passed to out in compile time.
    if (!context->IsRuntime()) {
      context->DecreaseLoDLevel("X", /*->*/ "Out");
    }
  }
};

class LoDTensorToArrayInferVarType : public framework::VarTypeInference {
 public:
  void operator()(framework::InferVarTypeContext *ctx) const override {
    for (auto &out_var : ctx->Output("Out")) {
      ctx->SetType(out_var, framework::proto::VarType::LOD_TENSOR_ARRAY);
    }
  }
};

class LoDTensorToArrayGradMaker : public framework::SingleGradOpDescMaker {
 public:
  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;

 protected:
  std::unique_ptr<framework::OpDesc> Apply() const override {
    auto *grad_op = new framework::OpDesc();
    grad_op->SetType("array_to_lod_tensor");
    grad_op->SetInput("X", OutputGrad("Out"));
    grad_op->SetInput("RankTable", Input("RankTable"));
    grad_op->SetOutput("Out", InputGrad("X"));
    grad_op->SetAttrMap(Attrs());
    return std::unique_ptr<framework::OpDesc>(grad_op);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(lod_tensor_to_array, ops::LoDTensorToArrayOp,
                  ops::LoDTensorToArrayOpProtoMaker,
                  ops::LoDTensorToArrayInferShape,
                  ops::LoDTensorToArrayInferVarType,
                  ops::LoDTensorToArrayGradMaker);