/* 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 <string>

#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"

namespace paddle {
namespace framework {
class OpDesc;
class InferShapeContext;
template <typename T>
class EmptyGradOpMaker;
}  // namespace framework
namespace imperative {
class OpBase;
}  // namespace imperative
}  // namespace paddle

namespace paddle {
namespace operators {

static void DeepCopy(const framework::LoDTensor &src_item,
                     const std::string &fetch_var_name,
                     framework::LoDTensor *dst_item) {
  if (src_item.IsInitialized() && src_item.numel() > 0) {
#ifdef PADDLE_WITH_MKLDNN
    // Conversion from MKL-DNN to Paddle
    if (src_item.layout() == framework::DataLayout::kMKLDNN) {
      framework::Tensor out;
      // Convert to desired Paddle layout, apart from grads of filter
      // as params are not a subject to paddle's data_format
      framework::innerTransDataLayoutFromMKLDNN(
          src_item.layout(), fetch_var_name == framework::GradVarName("Filter")
                                 ? framework::DataLayout::kNCHW
                                 : paddle::platform::MKLDNNDeviceContext::tls()
                                       .get_cur_paddle_data_layout(),
          src_item, &out, platform::CPUPlace());
      paddle::framework::TensorCopySync(out, platform::CPUPlace(), dst_item);
    } else {
      paddle::framework::TensorCopySync(src_item, platform::CPUPlace(),
                                        dst_item);
    }
#else
    paddle::framework::TensorCopySync(src_item, platform::CPUPlace(), dst_item);
#endif
  } else {
    // Not copy, if the src tensor is empty.
    dst_item->clear();
    dst_item->Resize({0});
  }
  dst_item->set_lod(src_item.lod());
}

class FetchV2Op : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {}

 protected:
  framework::OpKernelType GetKernelTypeForVar(
      const std::string &var_name, const framework::Tensor &tensor,
      const framework::OpKernelType &expected_kernel_type) const override {
    if (!tensor.IsInitialized()) {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }

  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    auto *fetch_var = ctx.InputVar("X");
    if (fetch_var == nullptr) {
      return framework::OpKernelType(framework::proto::VarType::FP32,
                                     platform::CPUPlace());
    }

    if (fetch_var->IsType<framework::LoDTensor>()) {
      auto &src_item = fetch_var->Get<framework::LoDTensor>();
      if (!src_item.IsInitialized()) {
        return framework::OpKernelType(framework::proto::VarType::FP32,
                                       platform::CPUPlace());
      }
    } else {
      auto &src_item = fetch_var->Get<framework::LoDTensorArray>();
      if (src_item.empty() || !src_item[0].IsInitialized()) {
        return framework::OpKernelType(framework::proto::VarType::FP32,
                                       platform::CPUPlace());
      }
    }

    return framework::OpKernelType(
        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
        platform::CPUPlace());
  }
};

class FetchV2Kernel {
 public:
  void operator()(const framework::ExecutionContext &ctx) const {
    auto fetch_var_name = ctx.InputName("X");
    auto *fetch_var = ctx.InputVar("X");
    if (fetch_var == nullptr) {
      return;
    }
    PADDLE_ENFORCE_EQ(
        ctx.HasOutput("Out"), true,
        platform::errors::NotFound("Output(Out) of fetch_v2_op is not found."));
    auto *out_var = ctx.OutputVar("Out");

    int col = ctx.Attr<int>("col");
    PADDLE_ENFORCE_GE(
        col, 0, platform::errors::InvalidArgument(
                    "Expected the column index (the attribute 'col' of "
                    "operator 'Fetch') of current fetching variable to be "
                    "no less than 0. But received column index = %d.",
                    col));

    auto *fetch_list = out_var->GetMutable<framework::FetchList>();

    if (static_cast<size_t>(col) >= fetch_list->size()) {
      fetch_list->resize(col + 1);
    }

    bool deepcopy = ctx.Attr<bool>("deepcopy");

    if (fetch_var->IsType<framework::LoDTensor>()) {
      auto &src_item = fetch_var->Get<framework::LoDTensor>();
      if (!src_item.IsInitialized()) {
        return;
      }
      auto *dst_item = &(BOOST_GET(framework::LoDTensor, fetch_list->at(col)));
      bool check_place = platform::is_cpu_place(src_item.place()) ||
                         platform::is_cuda_pinned_place(src_item.place());
      PADDLE_ENFORCE_EQ(
          check_place, true,
          platform::errors::InvalidArgument("Tensor's place of input(X) must "
                                            "be CPUPlace or CUDAPinnedPlace."));
      if (deepcopy) {
        DeepCopy(src_item, fetch_var_name, dst_item);
      } else {
        dst_item->ShareDataWith(src_item);
        dst_item->set_lod(src_item.lod());
      }
    } else {
      auto &src_item = fetch_var->Get<framework::LoDTensorArray>();
      framework::LoDTensorArray tmp(src_item.size());
      fetch_list->at(col) = tmp;
      auto &dst_item =
          BOOST_GET(framework::LoDTensorArray, fetch_list->at(col));
      for (size_t i = 0; i < src_item.size(); ++i) {
        PADDLE_ENFORCE_EQ(platform::is_cpu_place(src_item[i].place()), true,
                          platform::errors::InvalidArgument(
                              "Tensor's place of input(X) must be CPUPlace."));
        if (deepcopy) {
          DeepCopy(src_item[i], fetch_var_name, &dst_item[i]);
        } else {
          dst_item[i].ShareDataWith(src_item[i]);
          dst_item[i].set_lod(src_item[i].lod());
        }
      }
    }
  }
};

class FetchV2OpProtoMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "(LoDTensor) The resulted LoDTensor which is expected to return "
             "to users.");
    AddOutput("Out",
              "(vector<LoDTensor>) A fetching list of LoDTensor which may have "
              "different dimension, shape and data type.");
    AddAttr<int>("col", "(int) The column index of fetching object.");
    AddAttr<bool>("deepcopy", "(bool) Whether deep copy is required.")
        .SetDefault(true);
    AddComment(R"DOC(
FetchV2 Operator.
It should not be configured by users directly.
)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OPERATOR(
    fetch_v2, ops::FetchV2Op, ops::FetchV2OpProtoMaker,
    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);

REGISTER_OP_CPU_KERNEL_FUNCTOR(
    fetch_v2, float, ops::FetchV2Kernel, double, ops::FetchV2Kernel, int8_t,
    ops::FetchV2Kernel, uint8_t, ops::FetchV2Kernel, int, ops::FetchV2Kernel,
    int64_t, ops::FetchV2Kernel, bool, ops::FetchV2Kernel,
    paddle::platform::bfloat16, ops::FetchV2Kernel,
    paddle::platform::complex<float>, ops::FetchV2Kernel,
    paddle::platform::complex<double>, ops::FetchV2Kernel, plat::float16,
    ops::FetchV2Kernel, int16_t, ops::FetchV2Kernel);
