/* 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. */
// disable numpy compile error

#if defined(_MSC_VER)
#include <BaseTsd.h>
typedef SSIZE_T ssize_t;
#endif

#include <Python.h>
// Avoid a problem with copysign defined in pyconfig.h on Windows.
#ifdef copysign
#undef copysign
#endif

#include <string>
#include <vector>

#include "paddle/fluid/eager/accumulation/accumulation_node.h"
#include "paddle/fluid/eager/api/all.h"
#include "paddle/fluid/eager/autograd_meta.h"
#include "paddle/fluid/eager/backward.h"
#include "paddle/fluid/eager/custom_operator/custom_operator_node.h"
#include "paddle/fluid/eager/utils.h"
#include "paddle/fluid/framework/convert_utils.h"
#include "paddle/fluid/framework/custom_operator.h"
#include "paddle/fluid/framework/phi_utils.h"
#include "paddle/fluid/framework/python_headers.h"
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/pybind/eager.h"
#include "paddle/fluid/pybind/eager_utils.h"
#include "paddle/fluid/pybind/exception.h"
#include "paddle/fluid/pybind/tensor_py.h"
#include "paddle/phi/api/ext/op_meta_info.h"
#include "paddle/phi/api/lib/utils/allocator.h"
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/core/compat/convert_utils.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"

#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#include "paddle/fluid/pybind/cuda_streams_py.h"
#endif

#include "gflags/gflags.h"
#include "paddle/phi/api/include/operants_manager.h"
#include "paddle/phi/api/include/tensor_operants.h"

DECLARE_string(tensor_operants_mode);

namespace paddle {
namespace pybind {

namespace py = ::pybind11;

extern PyTypeObject* p_tensor_type;
extern PyTypeObject* g_multidevicefeedreader_pytype;
extern PyTypeObject* g_orderedmultidevicefeedreader_pytype;

size_t PyArray_Size_(PyObject* numpy_data) {
  size_t res = 1;
  auto dims = pybind11::detail::array_proxy(numpy_data)->dimensions;
  auto nd = pybind11::detail::array_proxy(numpy_data)->nd;
  while (nd--) {
    res *= (*dims++);
  }
  return res;
}

class EagerNumpyAllocation : public phi::Allocation {
 public:
  explicit EagerNumpyAllocation(PyObject* numpy_data, phi::DataType dtype)
      : Allocation(
            static_cast<void*>(pybind11::detail::array_proxy(numpy_data)->data),
            phi::SizeOf(dtype) * PyArray_Size_(numpy_data),
            paddle::platform::CPUPlace()),
        arr_(numpy_data) {
    PADDLE_ENFORCE_NOT_NULL(
        arr_,
        platform::errors::InvalidArgument("The underlying PyObject pointer of "
                                          "numpy array cannot be nullptr"));
    PADDLE_ENFORCE_NE(
        arr_,
        Py_None,
        platform::errors::PreconditionNotMet(
            "The underlying PyObject pointer of numpy array cannot be None"));
    Py_INCREF(arr_);
  }
  ~EagerNumpyAllocation() override {
    py::gil_scoped_acquire gil;
    Py_DECREF(arr_);
  }

 private:
  PyObject* arr_;
};

static PyObject* eager_api_scale(PyObject* self,
                                 PyObject* args,
                                 PyObject* kwargs) {
  EAGER_TRY
  // TODO(jiabin): Sync Tensor and Variable here when we support

  auto& tensor =
      reinterpret_cast<TensorObject*>(PyTuple_GET_ITEM(args, 0))->tensor;
  float scale = CastPyArg2AttrFloat(PyTuple_GET_ITEM(args, 1), 1);
  float bias = CastPyArg2AttrFloat(PyTuple_GET_ITEM(args, 2), 2);
  bool bias_after_scale = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3);
  bool trace_backward = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 4), 4);
  paddle::Tensor ret;
  {
    eager_gil_scoped_release guard;
    ret = egr::scale(tensor, scale, bias, bias_after_scale, trace_backward);
  }
  return ToPyObject(ret);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_run_backward(PyObject* self,
                                        PyObject* args,
                                        PyObject* kwargs) {
  EAGER_TRY
  auto tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
  auto grad_tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
  bool retain_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2);
  {
    eager_gil_scoped_release guard;
    egr::Backward(tensors, grad_tensors, retain_graph);
  }
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_run_partial_grad(PyObject* self,
                                            PyObject* args,
                                            PyObject* kwargs) {
  EAGER_TRY
  auto tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
  auto inputs = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
  auto grad_tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 2), 2);
  auto retain_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3);
  auto create_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 4), 4);
  auto only_inputs = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 5), 5);
  auto allow_unused = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 6), 6);
  auto no_grad_vars = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 7), 7);
  std::vector<paddle::Tensor> result;
  {
    eager_gil_scoped_release guard;
    result = egr::Grad(tensors,
                       inputs,
                       grad_tensors,
                       retain_graph,
                       create_graph,
                       only_inputs,
                       allow_unused,
                       no_grad_vars);
    VLOG(4) << " in eager_api_run_partial_grad, after runing egr::Grad";
  }
  return ToPyObject(result, true /* return_py_none_if_not_initialize */);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_tensor_copy(PyObject* self,
                                       PyObject* args,
                                       PyObject* kwargs) {
  EAGER_TRY
  paddle::Tensor& src =
      reinterpret_cast<TensorObject*>(PyTuple_GET_ITEM(args, 0))->tensor;
  paddle::Tensor& dst =
      reinterpret_cast<TensorObject*>(PyTuple_GET_ITEM(args, 1))->tensor;
  auto place = CastPyArg2Place(PyTuple_GET_ITEM(args, 2), 2);
  bool blocking = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3);

  {
    eager_gil_scoped_release guard;
    dst = src.copy_to(place, blocking);
    egr::EagerUtils::autograd_meta(&dst)->SetStopGradient(
        egr::EagerUtils::autograd_meta(&(src))->StopGradient());
    egr::EagerUtils::autograd_meta(&dst)->SetPersistable(
        egr::EagerUtils::autograd_meta(&(src))->Persistable());
  }
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

PyObject* eager_api_get_all_grads(PyObject* self,
                                  PyObject* args,
                                  PyObject* kwargs) {
  EAGER_TRY
  auto tensor_list = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);

  std::vector<paddle::Tensor> ret;
  for (auto& tensor : tensor_list) {
    VLOG(6) << "Get grad for tensor: " << tensor.name();
    auto meta = egr::EagerUtils::nullable_autograd_meta(tensor);
    if (!meta || meta->StopGradient()) {
      ret.emplace_back(paddle::Tensor());
      continue;
    }
    if (meta && meta->Grad().initialized()) {
      ret.emplace_back(meta->Grad());
    } else {
      ret.emplace_back(paddle::Tensor());
    }
  }
  return ToPyObject(ret, true);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

PyObject* eager_api_get_grads_lists(PyObject* self,
                                    PyObject* args,
                                    PyObject* kwargs) {
  EAGER_TRY
  auto tensor_list = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
  // The order of the 3 vectors is: FP16_grads, BF16_grads, FP32_grads
  std::vector<std::vector<paddle::Tensor>> ret(3);

  for (auto& tensor : tensor_list) {
    VLOG(6) << "Get grad for tensor: " << tensor.name();
    auto meta = egr::EagerUtils::nullable_autograd_meta(tensor);
    if (meta && meta->Grad().initialized()) {
      auto& grad = meta->Grad();
      switch (grad.dtype()) {
        case phi::DataType::FLOAT16:
          ret[0].emplace_back(grad);
          break;
        case phi::DataType::BFLOAT16:
          ret[1].emplace_back(grad);
          break;
        case phi::DataType::FLOAT32:
          ret[2].emplace_back(grad);
          break;
        default:
          break;
      }
    }
  }

  return ToPyObject(ret);

  EAGER_CATCH_AND_THROW_RETURN_NULL
}

PyObject* eager_api_get_grads_types(PyObject* self,
                                    PyObject* args,
                                    PyObject* kwargs) {
  EAGER_TRY
  auto tensor_list = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);

  std::vector<int> ret;

  for (auto& tensor : tensor_list) {
    VLOG(6) << "Get grad for tensor: " << tensor.name();
    auto meta = egr::EagerUtils::nullable_autograd_meta(tensor);
    if (!meta || meta->StopGradient()) {
      ret.emplace_back(-1);
      continue;
    }

    auto& grad = meta->Grad();
    if (meta && grad.initialized()) {
      if (grad.is_dense_tensor() &&
          (tensor.dtype() == phi::DataType::FLOAT32 ||
           tensor.dtype() == phi::DataType::FLOAT16 ||
           tensor.dtype() == phi::DataType::BFLOAT16)) {
        ret.emplace_back(
            paddle::framework::TransToProtoVarType(tensor.dtype()));
      }
    } else {
      ret.emplace_back(-1);
    }
  }

  return ToPyObject(ret);

  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_read_next_tensor_list(PyObject* self,
                                                 PyObject* args,
                                                 PyObject* kwargs) {
  EAGER_TRY
  auto tensor_base_list =
      CastPyArg2VectorOfTensorBase(PyTuple_GET_ITEM(args, 0), 0);
  std::vector<paddle::Tensor> tensor_list;
  {
    eager_gil_scoped_release guard;
    tensor_list.reserve(tensor_base_list.size());
    auto func = [](phi::DenseTensor& tensor_base) {
      paddle::Tensor tensor(egr::Controller::Instance().GenerateUniqueName());
      auto autograd_meta = egr::EagerUtils::autograd_meta(&tensor);
      autograd_meta->SetPersistable(false);
      autograd_meta->SetStopGradient(true);
      tensor.set_impl(std::make_shared<phi::DenseTensor>(tensor_base));
      return tensor;
    };
    for (auto& tensor_base : tensor_base_list) {
      tensor_list.emplace_back(func(tensor_base));
    }
  }
  return ToPyObject(tensor_list);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static void ConstructFwdAndBwdMap(
    const std::vector<paddle::OpMetaInfo>& vec_map,
    const std::string& op_type) {
  auto& in_out_map = egr::Controller::Instance().GetCustomEdgesSlotMap();
  if (in_out_map.find(op_type) != in_out_map.end()) {
    VLOG(7) << "Find Exist CustomEdgesSlotMap Skip >>>> ";
    return;
  } else {
    VLOG(7) << "Construct CustomEdgesSlotMap ";
    auto inputs_names = paddle::OpMetaInfoHelper::GetInputs(vec_map[0]);
    auto outputs_names = paddle::OpMetaInfoHelper::GetOutputs(vec_map[0]);
    auto attrs_names = paddle::OpMetaInfoHelper::GetAttrs(vec_map[0]);
    auto grad_outputs_names = paddle::OpMetaInfoHelper::GetOutputs(vec_map[1]);
    auto grad_inputs_names = paddle::OpMetaInfoHelper::GetInputs(vec_map[1]);
    auto grad_attrs_names = paddle::OpMetaInfoHelper::GetAttrs(vec_map[1]);
    std::vector<std::unordered_map<int, int>> res(5);

    in_out_map.insert({op_type, {res}});
    // Prepare pos map for grad_outputs
    VLOG(7) << "Prepare pos map for grad_outputs";
    PADDLE_ENFORCE_LE(
        grad_outputs_names.size(),
        inputs_names.size(),
        paddle::platform::errors::InvalidArgument(
            "Grad outputs num should be less equal than forward inputs num."));
    for (size_t i = 0; i < grad_outputs_names.size(); i++) {
      size_t end = grad_outputs_names[i].find("@GRAD");
      PADDLE_ENFORCE_NE(
          end,
          std::string::npos,
          paddle::platform::errors::NotFound(
              "All Grad outputs should be grad and we got %s is not grad var, "
              "please check your op and change to fit the rule.",
              grad_outputs_names[i]));
      for (size_t j = 0; j < inputs_names.size(); j++) {
        if (grad_outputs_names[i].substr(0, end) == inputs_names[j]) {
          VLOG(7) << " ==== Custom Operator: " << op_type << "'s No." << j
                  << " inputs: " << inputs_names[j] << " related to No." << i
                  << " grad_outputs: " << grad_outputs_names[i];
          in_out_map[op_type][0][0][j] = i;
        }
      }
    }
    // Prepare pos map for grad_inputs
    for (size_t i = 0; i < grad_inputs_names.size(); i++) {
      size_t end = grad_inputs_names[i].find("@GRAD");
      if (end != std::string::npos) {
        for (size_t j = 0; j < outputs_names.size(); j++) {
          if (grad_inputs_names[i].substr(0, end) == outputs_names[j]) {
            VLOG(7) << " ==== Custom Operator: " << op_type << "'s No." << j
                    << " outputs: " << outputs_names[j] << " related to No."
                    << i << " grad_inputs's grad: " << grad_inputs_names[i];
            in_out_map[op_type][0][1][j] = i;
          }
        }
      } else {
        if (std::find(outputs_names.begin(),
                      outputs_names.end(),
                      grad_inputs_names[i]) != outputs_names.end()) {
          for (size_t j = 0; j < outputs_names.size(); j++) {
            if (grad_inputs_names[i] == outputs_names[j]) {
              VLOG(7) << " ==== Custom Operator: " << op_type << "'s No." << j
                      << " outputs: " << outputs_names[j] << " related to No."
                      << i
                      << " grad_inputs fwd outputs: " << grad_inputs_names[i];
              in_out_map[op_type][0][2][j] = i;
            }
          }
        } else {
          for (size_t j = 0; j < inputs_names.size(); j++) {
            if (grad_inputs_names[i] == inputs_names[j]) {
              VLOG(7) << " ==== Custom Operator: " << op_type << "'s No." << j
                      << " inputs: " << inputs_names[j] << " related to No."
                      << i
                      << " grad_inputs fwd inputs: " << grad_inputs_names[i];
              in_out_map[op_type][0][3][j] = i;
            }
          }
        }
      }
    }

    // Prepare pos map for grad attrs_
    for (size_t i = 0; i < grad_attrs_names.size(); i++) {
      auto end = std::find(
          attrs_names.begin(), attrs_names.end(), grad_attrs_names[i]);
      PADDLE_ENFORCE_NE(end,
                        attrs_names.end(),
                        paddle::platform::errors::NotFound(
                            "All Grad attrs should be one of forward attrs and "
                            "we got %s is not one of them, please check your "
                            "op and change to fit the rule.",
                            grad_attrs_names[i]));
      for (size_t j = 0; j < attrs_names.size(); j++) {
        if (grad_attrs_names[i] == attrs_names[j]) {
          VLOG(7) << " ==== Custom Operator: " << op_type << "'s No." << j
                  << " attrs: " << attrs_names[j] << " related to No." << i
                  << " grad_attrs: " << grad_attrs_names[i];
          in_out_map[op_type][0][4][j] = i;
        }
      }
    }
  }
}

static std::vector<paddle::any> CastAttrsToTargetType(
    const std::vector<paddle::any>& src,
    const std::vector<std::string>& attrs_names) {
  std::vector<paddle::any> res;
  PADDLE_ENFORCE_EQ(src.size(),
                    attrs_names.size(),
                    paddle::platform::errors::InvalidArgument(
                        "We Expected same size of attrs and attrs_name list, "
                        "if u got this error indicate your custom op setting "
                        "%s attrs, but you just give %s",
                        attrs_names.size(),
                        src.size()));
  for (size_t i = 0; i < src.size(); i++) {
    size_t end = attrs_names[i].find(": ");
    std::string type_name = attrs_names[i].substr(end + 2);
    if (type_name == "int") {
      if (src[i].type() == typeid(bool)) {
        res.emplace_back(static_cast<int>(paddle::any_cast<bool>(src[i])));
      } else if (src[i].type() == typeid(int)) {
        res.emplace_back(src[i]);
      } else {
        PADDLE_THROW(platform::errors::InvalidArgument(
            "Your No. %s attrs should only can be bool or int32, other type is "
            "forbidden for now but we got %s. Check your code first please",
            i,
            src[i].type().name()));
      }
    } else if (type_name == "int64_t") {
      if (src[i].type() == typeid(bool)) {
        res.emplace_back(static_cast<int64_t>(paddle::any_cast<bool>(src[i])));
      } else if (src[i].type() == typeid(int)) {
        res.emplace_back(static_cast<int64_t>(paddle::any_cast<int>(src[i])));
      } else if (src[i].type() == typeid(int64_t)) {
        res.emplace_back(src[i]);
      } else {
        PADDLE_THROW(platform::errors::InvalidArgument(
            "Your No. %s attrs should only can be bool or int32 or int64_t, "
            "other type is forbidden for now but we got %s. Check your code "
            "first please",
            i,
            src[i].type().name()));
      }
    } else {
      res.emplace_back(src[i]);
    }
  }
  return res;
}

static PyObject* eager_api_jit_function_call(PyObject* self,
                                             PyObject* args,
                                             PyObject* kwargs) {
  EAGER_TRY

  std::shared_ptr<jit::Function> function =
      CastPyArg2JitFunction(PyTuple_GET_ITEM(args, 0), 0);
  std::vector<paddle::Tensor> ins =
      CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
  std::vector<paddle::Tensor> outs;
  {
    eager_gil_scoped_release guard;
    outs = (*function)(ins);
  }
  return ToPyObject(outs);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_run_custom_op(PyObject* self,
                                         PyObject* args,
                                         PyObject* kwargs) {
  EAGER_TRY
  FLAGS_tensor_operants_mode = "phi";
  if (paddle::OperantsManager::Instance().phi_operants.get() == nullptr) {
    paddle::OperantsManager::Instance().phi_operants.reset(
        new paddle::operants::PhiTensorOperants());
    VLOG(4) << "Initialize phi tensor operants successfully";
  }

  paddle::CustomOpKernelContext ctx =
      CastPyArg2CustomOpKernelContext(PyTuple_GET_ITEM(args, 0), 0);
  std::string op_type = CastPyArg2AttrString(PyTuple_GET_ITEM(args, 1), 1);
  bool trace_backward = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2);
  {
    eager_gil_scoped_release guard;
    VLOG(7) << "Get things for python for Custom Op: " << op_type
            << ", trace_backward is: " << trace_backward;
    auto meta_info_map = egr::Controller::Instance().GetOpMetaInfoMap();
    PADDLE_ENFORCE_NE(
        meta_info_map.find(op_type),
        meta_info_map.end(),
        paddle::platform::errors::NotFound(
            "Can't find %s in Eager OpMetaInfoMap which should be "
            "created by LoadOpMetaInfoAndRegisterOp, please make "
            "sure you registered your op first and try again. ",
            op_type));
    VLOG(7) << "Run Kernel of Custom Op: " << op_type;
    // TODO(HongyuJia): Optimize Attrs Cast naming and implementation
    std::vector<paddle::any> res_attrs = CastAttrsToTargetType(
        ctx.Attrs(),
        paddle::OpMetaInfoHelper::GetAttrs(meta_info_map.at(op_type)[0]));
    ctx.EmplaceBackAttrs(res_attrs);
    const auto& vec_map = meta_info_map.at(op_type);

    const auto& inputs =
        paddle::OpMetaInfoHelper::GetInputs(meta_info_map.at(op_type)[0]);
    const auto& outputs =
        paddle::OpMetaInfoHelper::GetOutputs(meta_info_map.at(op_type)[0]);
    const auto& inplace_map =
        paddle::OpMetaInfoHelper::GetInplaceMap(meta_info_map.at(op_type)[0]);
    // handle inplace map
    ctx.MapPlainOutputs(inputs, outputs, inplace_map);
    (*paddle::OpMetaInfoHelper::GetKernelFn(vec_map[0]))(&ctx);
    ctx.AssignInplaceOutputs();

    VLOG(7) << "Get AutogradMeta for inputs and outputs for Custom Op";
    std::vector<std::vector<egr::AutogradMeta*>> ins_auto_grad_metas;
    std::vector<std::vector<egr::AutogradMeta*>> outs_auto_grad_metas;
    VLOG(7) << "We got slot num of ins is: " << ctx.InputRange().size();
    ins_auto_grad_metas.resize(ctx.InputRange().size());
    VLOG(7) << "We got slot num of outs is: " << ctx.OutputRange().size();
    outs_auto_grad_metas.resize(ctx.OutputRange().size());

    for (size_t i = 0; i < ctx.InputRange().size(); i++) {
      ins_auto_grad_metas[i] =
          egr::EagerUtils::nullable_autograd_meta(ctx.InputsBetween(
              ctx.InputRangeAt(i).first, ctx.InputRangeAt(i).second));
    }
    for (size_t i = 0; i < ctx.OutputRange().size(); i++) {
      outs_auto_grad_metas[i] =
          egr::EagerUtils::unsafe_autograd_meta(ctx.OutputsBetweeen(
              ctx.OutputRangeAt(i).first, ctx.OutputRangeAt(i).second));
    }
    bool require_any_grad = false;
    for (size_t i = 0; i < ins_auto_grad_metas.size(); i++) {
      require_any_grad =
          require_any_grad || egr::EagerUtils::ComputeRequireGrad(
                                  trace_backward, &(ins_auto_grad_metas[i]));
    }

    // handle inplace map
    for (size_t i = 0; i < ctx.InputRange().size(); i++) {
      if (inplace_map.find(inputs[i]) != inplace_map.end()) {
        size_t input_size =
            ctx.InputRangeAt(i).second - ctx.InputRangeAt(i).first;
        size_t start_idx = ctx.InputRangeAt(i).first;
        for (size_t j = 0; j < input_size; j++) {
          egr::EagerUtils::CheckInplace(ctx.InputAt(start_idx + j),
                                        ins_auto_grad_metas[i][j],
                                        require_any_grad);
          // Bump Inplace Version
          ctx.MutableInputAt(start_idx + j).bump_inplace_version();
          VLOG(3) << "Custom operator: Tensor("
                  << ctx.InputAt(start_idx + j).name()
                  << ") uses Inplace Strategy.";
        }
      }
    }

    if (require_any_grad && (vec_map.size() > 1)) {
      VLOG(6) << " Construct Grad for Custom Op: " << op_type;
      ConstructFwdAndBwdMap(vec_map, op_type);
      for (size_t i = 0; i < outs_auto_grad_metas.size(); i++) {
        egr::EagerUtils::PassStopGradient(false, &(outs_auto_grad_metas[i]));
      }
      // Note(HongyuJia): In dygraph eager mode, CheckInplace makes sure leaf
      // nodes set stop_gradient=True. However, dygraph mode can also outputs
      // lead nodes' gradients (For example, we can get x.grad after x.add_(y)).
      // To be consistent with dygraph mode, we have to PassStopGradient for all
      // inplaced ins_auto_grad_metas.
      std::unordered_map<size_t, size_t> inplace_tensor_map =
          ctx.GetInplaceTensorMap();
      for (auto pair : inplace_tensor_map) {
        egr::EagerUtils::PassStopGradient(false,
                                          &(ins_auto_grad_metas[pair.first]));
      }
      auto grad_node = std::make_shared<egr::RunCustomOpNode>(
          outs_auto_grad_metas.size(), ins_auto_grad_metas.size(), op_type);
      auto slot_map =
          egr::Controller::Instance().GetCustomEdgesSlotMap().at(op_type);
      // Prepare Grad outputs
      size_t no_grad_cnt = 0;
      for (size_t i = 0; i < ins_auto_grad_metas.size(); i++) {
        const std::vector<paddle::Tensor>& in_tensors = ctx.InputsBetween(
            ctx.InputRangeAt(i).first, ctx.InputRangeAt(i).second);

        if (slot_map[0][0].find(i) != slot_map[0][0].end()) {
          grad_node->SetGradOutMeta(in_tensors, slot_map[0][0][i]);
        } else {
          grad_node->SetGradOutMeta(
              in_tensors, ins_auto_grad_metas.size() - 1 - no_grad_cnt);
          no_grad_cnt++;
        }
      }
      // Prepare Grad inputs with grad of fwd outputs
      for (size_t i = 0; i < outs_auto_grad_metas.size(); i++) {
        const std::vector<paddle::Tensor>& out_tensors = ctx.OutputsBetweeen(
            ctx.OutputRangeAt(i).first, ctx.OutputRangeAt(i).second);

        egr::EagerUtils::SetOutRankWithSlot(&(outs_auto_grad_metas[i]), i);
        egr::EagerUtils::SetHistory(&(outs_auto_grad_metas[i]), grad_node);
        grad_node->SetGradInMeta(out_tensors, i);
      }

      // Prepare Grad inputs with fwd outputs
      for (auto it = slot_map[0][2].begin(); it != slot_map[0][2].end(); it++) {
        VLOG(7) << "Prepare fwd_outs: " << it->first
                << " to grad_inputs: " << it->second;
        grad_node->fwd_outs[it->second] =
            egr::RunCustomOpNode::ConstructTensorWrapper(
                ctx.OutputsBetweeen(ctx.OutputRangeAt(it->first).first,
                                    ctx.OutputRangeAt(it->first).second));
      }

      // Prepare Grad inputs with fwd inputs
      for (auto it = slot_map[0][3].begin(); it != slot_map[0][3].end(); it++) {
        VLOG(7) << "Prepare fwd_ins: " << it->first
                << " to grad_inputs: " << it->second;
        grad_node->fwd_ins[it->second] =
            egr::RunCustomOpNode::ConstructTensorWrapper(
                ctx.InputsBetween(ctx.InputRangeAt(it->first).first,
                                  ctx.InputRangeAt(it->first).second));
      }

      auto attrs_names =
          paddle::OpMetaInfoHelper::GetAttrs(meta_info_map.at(op_type)[1]);
      std::vector<paddle::any> attrs(attrs_names.size());
      // Prepare attrs for Grad node
      for (auto it = slot_map[0][4].begin(); it != slot_map[0][4].end(); it++) {
        VLOG(7) << "Prepare fwd attrs: " << it->first
                << " to grad_attrs: " << it->second;
        attrs[it->second] = res_attrs[it->first];
      }
      grad_node->SetAttrs(attrs);
    }
  }
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_sparse_coo_tensor(PyObject* self,
                                             PyObject* args,
                                             PyObject* kwargs) {
  EAGER_TRY
  auto non_zero_indices = CastPyArg2Tensor(PyTuple_GET_ITEM(args, 0), 0);
  auto non_zero_elements = CastPyArg2Tensor(PyTuple_GET_ITEM(args, 1), 1);
  auto dense_shape = CastPyArg2VectorOfInt(PyTuple_GET_ITEM(args, 2), 2);
  auto stop_gradient = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3);
  paddle::Tensor tensor;
  {
    eager_gil_scoped_release guard;
    PADDLE_ENFORCE(non_zero_indices.is_dense_tensor(),
                   paddle::platform::errors::Fatal(
                       "the non-zero indices must be a DenseTensor."));
    PADDLE_ENFORCE(non_zero_elements.is_dense_tensor(),
                   paddle::platform::errors::Fatal(
                       "the non-zero elements must be a DenseTensor."));
    auto dense_indices =
        std::dynamic_pointer_cast<phi::DenseTensor>(non_zero_indices.impl());
    auto dense_elements =
        std::dynamic_pointer_cast<phi::DenseTensor>(non_zero_elements.impl());
    // TODO(zhangkaihuo): After creating SparseCooTensor, call coalesced() to
    // sort and merge duplicate indices
    std::shared_ptr<phi::SparseCooTensor> coo_tensor =
        std::make_shared<phi::SparseCooTensor>(
            *dense_indices, *dense_elements, phi::make_ddim(dense_shape));
    tensor.set_impl(coo_tensor);
    auto name =
        egr::Controller::Instance().GenerateUniqueName("generated_tensor");
    tensor.set_name(name);
    auto autograd_meta = egr::EagerUtils::autograd_meta(&tensor);
    autograd_meta->SetStopGradient(static_cast<bool>(stop_gradient));
    if (!autograd_meta->GetMutableGradNode()) {
      VLOG(3) << "Tensor(" << name
              << ") doesn't have GradNode, add GradNodeAccumulation to it.";
      autograd_meta->SetGradNode(
          std::make_shared<egr::GradNodeAccumulation>(autograd_meta));
    }
  }
  return ToPyObject(tensor);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_sparse_csr_tensor(PyObject* self,
                                             PyObject* args,
                                             PyObject* kwargs) {
  EAGER_TRY
  auto non_zero_crows = CastPyArg2Tensor(PyTuple_GET_ITEM(args, 0), 0);
  auto non_zero_cols = CastPyArg2Tensor(PyTuple_GET_ITEM(args, 1), 1);
  auto non_zero_elements = CastPyArg2Tensor(PyTuple_GET_ITEM(args, 2), 2);
  auto dense_shape = CastPyArg2VectorOfInt(PyTuple_GET_ITEM(args, 3), 3);
  auto stop_gradient = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 4), 4);
  paddle::Tensor tensor;
  {
    eager_gil_scoped_release guard;
    PADDLE_ENFORCE(non_zero_crows.is_dense_tensor(),
                   paddle::platform::errors::Fatal(
                       "the compressed non-zero rows must be a DenseTensor."));
    PADDLE_ENFORCE(non_zero_cols.is_dense_tensor(),
                   paddle::platform::errors::Fatal(
                       "the non-zero cols must be a DenseTensor."));
    PADDLE_ENFORCE(non_zero_elements.is_dense_tensor(),
                   paddle::platform::errors::Fatal(
                       "the non-zero elements must be a DenseTensor."));

    auto dense_crows =
        std::dynamic_pointer_cast<phi::DenseTensor>(non_zero_crows.impl());
    auto dense_cols =
        std::dynamic_pointer_cast<phi::DenseTensor>(non_zero_cols.impl());
    auto dense_elements =
        std::dynamic_pointer_cast<phi::DenseTensor>(non_zero_elements.impl());
    std::shared_ptr<phi::SparseCsrTensor> csr_tensor =
        std::make_shared<phi::SparseCsrTensor>(*dense_crows,
                                               *dense_cols,
                                               *dense_elements,
                                               phi::make_ddim(dense_shape));
    tensor.set_impl(csr_tensor);
    auto name =
        egr::Controller::Instance().GenerateUniqueName("generated_tensor");
    tensor.set_name(name);
    auto autograd_meta = egr::EagerUtils::autograd_meta(&tensor);
    autograd_meta->SetStopGradient(static_cast<bool>(stop_gradient));
    if (!autograd_meta->GetMutableGradNode()) {
      VLOG(3) << "Tensor(" << name
              << ") have not GradNode, add GradNodeAccumulation for it.";
      autograd_meta->SetGradNode(
          std::make_shared<egr::GradNodeAccumulation>(autograd_meta));
    }
  }
  return ToPyObject(tensor);
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_register_saved_tensors_hooks(PyObject* self,
                                                        PyObject* args,
                                                        PyObject* kwargs) {
  EAGER_TRY
  if (egr::Controller::Instance().HasGrad()) {
    auto pack_hook = PyTuple_GET_ITEM(args, 0);
    auto unpack_hook = PyTuple_GET_ITEM(args, 1);
    egr::SavedTensorsHooks::GetInstance().SetHooks(
        std::make_shared<PackHook>(pack_hook),
        std::make_shared<UnPackHook>(unpack_hook));
  }
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_reset_saved_tensors_hooks(PyObject* self,
                                                     PyObject* args,
                                                     PyObject* kwargs) {
  EAGER_TRY
  egr::SavedTensorsHooks::GetInstance().ResetHooks();
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

#if defined(PADDLE_WITH_CUDA)
static PyObject* eager_api_async_read(PyObject* self,
                                      PyObject* args,
                                      PyObject* kwargs) {
  EAGER_TRY
  auto& src = GetTensorFromArgs("async_read", "src", args, 0, false);
  auto& dst = GetTensorFromArgs("async_read", "dst", args, 1, false);
  auto& index = GetTensorFromArgs("async_read", "index", args, 2, false);
  auto& buffer = GetTensorFromArgs("async_read", "buffer", args, 3, false);
  auto& offset = GetTensorFromArgs("async_read", "offset", args, 4, false);
  auto& count = GetTensorFromArgs("async_read", "count", args, 5, false);
  {
    eager_gil_scoped_release guard;
    PADDLE_ENFORCE_EQ(
        src.is_gpu_pinned(),
        true,
        platform::errors::InvalidArgument("Required `src` device should be "
                                          "CUDAPinnedPlace, but received %d.",
                                          src.place()));
    PADDLE_ENFORCE_EQ(
        dst.is_gpu(),
        true,
        platform::errors::InvalidArgument(
            "Required `dst` device should be CUDAPlace, but received %d.",
            dst.place()));
    PADDLE_ENFORCE_EQ(
        index.is_cpu(),
        true,
        platform::errors::InvalidArgument(
            "Required `index` device should be CPUPlace, but received %d.",
            index.place()));
    PADDLE_ENFORCE_EQ(buffer.is_gpu_pinned(),
                      true,
                      platform::errors::InvalidArgument(
                          "Required `buffer` device should be CUDAPinnedPlace, "
                          "but received %d.",
                          buffer.place()));
    PADDLE_ENFORCE_EQ(
        offset.is_cpu(),
        true,
        platform::errors::InvalidArgument(
            "Required `offset` device should be CPUPlace, but received %d.",
            offset.place()));
    PADDLE_ENFORCE_EQ(
        count.is_cpu(),
        true,
        platform::errors::InvalidArgument(
            "Required `count` device should be CPUPlace, but received %d.",
            count.place()));

    auto& src_tensor = src;
    auto* dst_tensor = &dst;
    auto& index_tensor = index;
    auto* buffer_tensor = &buffer;
    auto& offset_tensor = offset;
    auto& count_tensor = count;
    auto* dst_data = dst_tensor->mutable_data<float>(dst.place());
    const auto& deviceId = paddle::platform::GetCurrentDeviceId();

    PADDLE_ENFORCE_EQ(src_tensor.dims().size(),
                      dst_tensor->dims().size(),
                      platform::errors::InvalidArgument(
                          "`src` and `dst` should have same tensor shape, "
                          "except for the first dimension."));
    PADDLE_ENFORCE_EQ(src_tensor.dims().size(),
                      buffer_tensor->dims().size(),
                      platform::errors::InvalidArgument(
                          "`src` and `buffer` should have same tensor shape, "
                          "except for the first dimension."));
    for (int i = 1; i < src_tensor.dims().size(); i++) {
      PADDLE_ENFORCE_EQ(
          src_tensor.dims()[i],
          dst_tensor->dims()[i],
          platform::errors::InvalidArgument(
              "`src` and `dst` should have the same tensor shape, "
              "except for the first dimension."));
      PADDLE_ENFORCE_EQ(
          src_tensor.dims()[i],
          buffer_tensor->dims()[i],
          platform::errors::InvalidArgument(
              "`src` and `buffer` should have the same tensor shape, "
              "except for the first dimension."));
    }
    PADDLE_ENFORCE_EQ(index_tensor.dims().size(),
                      1,
                      platform::errors::InvalidArgument(
                          "`index` tensor should be one-dimensional."));

    auto stream = paddle::platform::get_current_stream(deviceId)->raw_stream();

    int64_t numel = 0;  // total copy length
    int64_t copy_flag = offset_tensor.dims()[0];
    int64_t size = src_tensor.numel() / src_tensor.dims()[0];

    if (copy_flag != 0) {
      PADDLE_ENFORCE_EQ(offset_tensor.dims().size(),
                        1,
                        platform::errors::InvalidArgument(
                            "`offset` tensor should be one-dimensional."));
      PADDLE_ENFORCE_EQ(count_tensor.dims().size(),
                        1,
                        platform::errors::InvalidArgument(
                            "`count` tensor should be one-dimensional."));
      PADDLE_ENFORCE_EQ(offset_tensor.numel(),
                        count_tensor.numel(),
                        platform::errors::InvalidArgument(
                            "`offset` and `count` tensor size dismatch."));
      auto* offset_data = offset_tensor.data<int64_t>();
      auto* count_data = count_tensor.data<int64_t>();
      for (int64_t i = 0; i < count_tensor.numel(); i++) {
        numel += count_data[i];
      }
      PADDLE_ENFORCE_LE(numel + index_tensor.numel(),
                        buffer_tensor->dims()[0],
                        platform::errors::InvalidArgument(
                            "Buffer tensor size is too small."));
      PADDLE_ENFORCE_LE(numel + index_tensor.numel(),
                        dst_tensor->dims()[0],
                        platform::errors::InvalidArgument(
                            "Target tensor size is too small."));

      int64_t src_offset, dst_offset = 0, c;
      auto* src_data = src_tensor.data<float>();
      for (int64_t i = 0; i < offset_tensor.numel(); i++) {
        src_offset = offset_data[i], c = count_data[i];
        PADDLE_ENFORCE_LE(src_offset + c,
                          src_tensor.dims()[0],
                          platform::errors::InvalidArgument(
                              "Invalid offset or count index."));
        PADDLE_ENFORCE_LE(dst_offset + c,
                          dst_tensor->dims()[0],
                          platform::errors::InvalidArgument(
                              "Invalid offset or count index."));
        cudaMemcpyAsync(dst_data + (dst_offset * size),
                        src_data + (src_offset * size),
                        c * size * sizeof(float),
                        cudaMemcpyHostToDevice,
                        stream);
        dst_offset += c;
      }
    } else {
      PADDLE_ENFORCE_LE(index_tensor.numel(),
                        buffer_tensor->dims()[0],
                        platform::errors::InvalidArgument(
                            "Buffer tensor size is too small."));
    }

    // Select the index data to the buffer
    auto index_select = [](const paddle::Tensor& src_tensor,
                           const paddle::Tensor& index_tensor,
                           paddle::Tensor* buffer_tensor) {
      auto* src_data = src_tensor.data<float>();
      auto* index_data = index_tensor.data<int64_t>();
      auto* buffer_data = buffer_tensor->data<float>();
      const int& slice_size = src_tensor.numel() / src_tensor.dims()[0];
      const int& copy_bytes = slice_size * sizeof(float);
      int64_t c = 0;
      for (int64_t i = 0; i < index_tensor.numel(); i++) {
        std::memcpy(buffer_data + c * slice_size,
                    src_data + index_data[i] * slice_size,
                    copy_bytes);
        c += 1;
      }
    };
    index_select(src_tensor, index_tensor, buffer_tensor);

    // Copy the data to device memory
    cudaMemcpyAsync(dst_data + (numel * size),
                    buffer_tensor->data<float>(),
                    index_tensor.numel() * size * sizeof(float),
                    cudaMemcpyHostToDevice,
                    stream);
  }
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_async_write(PyObject* self,
                                       PyObject* args,
                                       PyObject* kwargs) {
  EAGER_TRY
  auto& src = GetTensorFromArgs("async_write", "src", args, 0, false);
  auto& dst = GetTensorFromArgs("async_write", "dst", args, 1, false);
  auto& offset = GetTensorFromArgs("async_write", "offset", args, 2, false);
  auto& count = GetTensorFromArgs("async_write", "count", args, 3, false);
  {
    eager_gil_scoped_release guard;
    PADDLE_ENFORCE_EQ(
        src.is_gpu(),
        true,
        platform::errors::InvalidArgument(
            "Required `src` device should be CUDAPlace, but received %d. ",
            src.place()));
    PADDLE_ENFORCE_EQ(dst.is_gpu_pinned(),
                      true,
                      platform::errors::InvalidArgument(
                          "Required `dst` device should be CUDAPinnedPlace, "
                          "but received %d. ",
                          dst.place()));
    PADDLE_ENFORCE_EQ(
        offset.is_cpu(),
        true,
        platform::errors::InvalidArgument("Required `offset` device should "
                                          "be CPUPlace, but received %d. ",
                                          offset.place()));
    PADDLE_ENFORCE_EQ(
        count.is_cpu(),
        true,
        platform::errors::InvalidArgument(
            "Required `count` device should be CPUPlace, but received %d. ",
            count.place()));

    // TODO(daisiming): In future, add index as arguments following
    // async_read.
    auto& src_tensor = src;
    auto* dst_tensor = &dst;
    auto& offset_tensor = offset;
    auto& count_tensor = count;
    const auto& deviceId = paddle::platform::GetCurrentDeviceId();

    PADDLE_ENFORCE_EQ(offset_tensor.dims().size(),
                      1,
                      platform::errors::InvalidArgument(
                          "`offset` tensor should be one-dimensional."));
    PADDLE_ENFORCE_EQ(count_tensor.dims().size(),
                      1,
                      platform::errors::InvalidArgument(
                          "`count` tensor should be one-dimensional."));
    PADDLE_ENFORCE_EQ(offset_tensor.numel(),
                      count_tensor.numel(),
                      platform::errors::InvalidArgument(
                          "`offset` and `count` tensor size dismatch."));
    PADDLE_ENFORCE_EQ(src_tensor.dims().size(),
                      dst_tensor->dims().size(),
                      platform::errors::InvalidArgument(
                          "`src` and `dst` should have the same tensor shape, "
                          "except for the first dimension."));
    for (int i = 1; i < src_tensor.dims().size(); i++) {
      PADDLE_ENFORCE_EQ(
          src_tensor.dims()[i],
          dst_tensor->dims()[i],
          platform::errors::InvalidArgument(
              "`src` and `dst` should have the same tensor shape, "
              "except for the first dimension."));
    }

    auto stream = paddle::platform::get_current_stream(deviceId)->raw_stream();

    int64_t size = src_tensor.numel() / src_tensor.dims()[0];
    auto* src_data = src_tensor.data<float>();
    auto* dst_data = dst_tensor->data<float>();
    const int64_t* offset_data = offset_tensor.data<int64_t>();
    const int64_t* count_data = count_tensor.data<int64_t>();
    int64_t src_offset = 0, dst_offset, c;
    for (int64_t i = 0; i < offset_tensor.numel(); i++) {
      dst_offset = offset_data[i], c = count_data[i];
      PADDLE_ENFORCE_LE(
          src_offset + c,
          src_tensor.dims()[0],
          platform::errors::InvalidArgument("Invalid offset or count index"));
      PADDLE_ENFORCE_LE(
          dst_offset + c,
          dst_tensor->dims()[0],
          platform::errors::InvalidArgument("Invalid offset or count index"));
      cudaMemcpyAsync(dst_data + (dst_offset * size),
                      src_data + (src_offset * size),
                      c * size * sizeof(float),
                      cudaMemcpyDeviceToHost,
                      stream);
      src_offset += c;
    }
  }
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

static PyObject* eager_api_to_uva_tensor(PyObject* self,
                                         PyObject* args,
                                         PyObject* kwargs) {
  EAGER_TRY
  VLOG(4) << "Running in eager_api_to_uva_tensor.";
  auto new_tensor = std::shared_ptr<paddle::Tensor>(
      new paddle::Tensor(egr::Controller::Instance().GenerateUniqueName()));
  PyObject* obj = PyTuple_GET_ITEM(args, 0);
  auto array = py::cast<py::array>(py::handle(obj));

  Py_ssize_t args_num = PyTuple_Size(args);
  int64_t device_id = 0;
  if (args_num > 1) {
    PyObject* Py_device_id = PyTuple_GET_ITEM(args, 1);
    if (Py_device_id) {
      device_id = CastPyArg2AttrLong(Py_device_id, 1);
    }
  }

  if (py::isinstance<py::array_t<int32_t>>(array)) {
    SetUVATensorFromPyArray<int32_t>(new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<int64_t>>(array)) {
    SetUVATensorFromPyArray<int64_t>(new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<float>>(array)) {
    SetUVATensorFromPyArray<float>(new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<double>>(array)) {
    SetUVATensorFromPyArray<double>(new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<int8_t>>(array)) {
    SetUVATensorFromPyArray<int8_t>(new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<int16_t>>(array)) {
    SetUVATensorFromPyArray<int16_t>(new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<paddle::platform::float16>>(array)) {
    SetUVATensorFromPyArray<paddle::platform::float16>(
        new_tensor, array, device_id);
  } else if (py::isinstance<py::array_t<bool>>(array)) {
    SetUVATensorFromPyArray<bool>(new_tensor, array, device_id);
  } else {
    // obj may be any type, obj.cast<py::array>() may be failed,
    // then the array.dtype will be string of unknown meaning.
    PADDLE_THROW(platform::errors::InvalidArgument(
        "Input object type error or incompatible array data type. "
        "tensor.set() supports array with bool, float16, float32, "
        "float64, int8, int16, int32, int64,"
        "please check your input or input array data type."));
  }
  return ToPyObject(*(new_tensor.get()));
  EAGER_CATCH_AND_THROW_RETURN_NULL
}
#endif

static PyObject* eager_api__add_backward_final_hook(PyObject* self,
                                                    PyObject* args,
                                                    PyObject* kwargs) {
  EAGER_TRY
  PyObject* hook_func = PyTuple_GET_ITEM(args, 0);
  egr::Controller::Instance().RegisterBackwardFinalHook(
      std::make_shared<PyVoidHook>(hook_func));
  RETURN_PY_NONE
  EAGER_CATCH_AND_THROW_RETURN_NULL
}

PyMethodDef variable_functions[] = {
    // TODO(jiabin): Remove scale when we have final state tests
    {"scale",
     (PyCFunction)(void (*)(void))eager_api_scale,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"_add_backward_final_hook",
     (PyCFunction)(void (*)(void))eager_api__add_backward_final_hook,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"run_backward",
     (PyCFunction)(void (*)(void))eager_api_run_backward,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"run_partial_grad",
     (PyCFunction)(void (*)(void))eager_api_run_partial_grad,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"_run_custom_op",
     (PyCFunction)(void (*)(void))eager_api_run_custom_op,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"tensor_copy",
     (PyCFunction)(void (*)(void))eager_api_tensor_copy,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"get_all_grads",
     (PyCFunction)(void (*)(void))eager_api_get_all_grads,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"get_grads_lists",
     (PyCFunction)(void (*)(void))eager_api_get_grads_lists,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"get_grads_types",
     (PyCFunction)(void (*)(void))eager_api_get_grads_types,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"read_next_tensor_list",
     (PyCFunction)(void (*)(void))eager_api_read_next_tensor_list,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"jit_function_call",
     (PyCFunction)(void (*)(void))eager_api_jit_function_call,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    /**sparse functions**/
    {"sparse_coo_tensor",
     (PyCFunction)(void (*)(void))eager_api_sparse_coo_tensor,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"sparse_csr_tensor",
     (PyCFunction)(void (*)(void))eager_api_sparse_csr_tensor,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"register_saved_tensors_hooks",
     (PyCFunction)(void (*)(void))eager_api_register_saved_tensors_hooks,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"reset_saved_tensors_hooks",
     (PyCFunction)(void (*)(void))eager_api_reset_saved_tensors_hooks,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
/**sparse functions**/
#if defined(PADDLE_WITH_CUDA)
    {"async_read",
     (PyCFunction)(void (*)(void))eager_api_async_read,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"async_write",
     (PyCFunction)(void (*)(void))eager_api_async_write,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
    {"to_uva_tensor",
     (PyCFunction)(void (*)(void))eager_api_to_uva_tensor,
     METH_VARARGS | METH_KEYWORDS,
     NULL},
#endif
    {NULL, NULL, 0, NULL}};

void BindFunctions(PyObject* module) {
  if (PyModule_AddFunctions(module, variable_functions) < 0) {
    PADDLE_THROW(platform::errors::Fatal(
        "Init Paddle erroe in BindFunctions(PyModule_AddFunctions)."));
    return;
  }
}

}  // namespace pybind
}  // namespace paddle