text: /* 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 <Python.h>
  
  #include <algorithm>
  #include <cctype>
  #include <cstdlib>
  #include <iterator>
  #include <map>
  #include <memory>
  #include <mutex>  // NOLINT // for call_once
  #include <string>
  #include <tuple>
  #include <type_traits>
  #include <unordered_map>
  #include <unordered_set>
  #include <utility>
  #include <vector>
  
  #include "paddle/fluid/framework/convert_utils.h"
  #include "paddle/fluid/framework/custom_operator.h"
  #include "paddle/fluid/framework/data_layout.h"
  #include "paddle/fluid/framework/data_type_transform.h"
  #include "paddle/fluid/framework/executor.h"
  #include "paddle/fluid/framework/executor_cache.h"
  #include "paddle/fluid/framework/executor_gc_helper.h"
  #include "paddle/fluid/framework/feed_fetch_method.h"
  #include "paddle/fluid/framework/feed_fetch_type.h"
  #include "paddle/fluid/framework/garbage_collector.h"
  #include "paddle/fluid/framework/io/fs.h"
  #include "paddle/fluid/framework/ir/coalesce_grad_tensor_pass.h"
  #include "paddle/fluid/framework/ir/cost_model.h"
  #include "paddle/fluid/framework/ir/generate_pass.h"
  #include "paddle/fluid/framework/ir/pass_builder.h"
  #include "paddle/fluid/framework/lod_rank_table.h"
  #include "paddle/fluid/framework/lod_tensor_array.h"
  #include "paddle/fluid/framework/new_executor/standalone_executor.h"
  #include "paddle/fluid/framework/op_info.h"
  #include "paddle/fluid/framework/op_registry.h"
  #include "paddle/fluid/framework/op_version_registry.h"
  #include "paddle/fluid/framework/parallel_executor.h"
  #include "paddle/fluid/framework/phi_utils.h"
  #include "paddle/fluid/framework/prune.h"
  #include "paddle/fluid/framework/reader.h"
  #include "paddle/fluid/framework/save_load_util.h"
  #include "paddle/fluid/framework/scope_pool.h"
  #include "paddle/fluid/framework/selected_rows_utils.h"
  #include "paddle/fluid/framework/tensor_util.h"
  #include "paddle/fluid/framework/trainer.h"
  #include "paddle/fluid/framework/type_defs.h"
  #include "paddle/fluid/framework/version.h"
  #include "paddle/fluid/imperative/amp_auto_cast.h"
  #include "paddle/fluid/imperative/layer.h"
  #include "paddle/fluid/memory/allocation/allocator_strategy.h"
  #include "paddle/fluid/memory/allocation/mmap_allocator.h"
  #include "paddle/fluid/operators/activation_op.h"
  #include "paddle/fluid/operators/common_infer_shape_functions.h"
  #include "paddle/fluid/operators/py_func_op.h"
  #include "paddle/fluid/platform/cpu_helper.h"
  #include "paddle/fluid/platform/cpu_info.h"
  #include "paddle/fluid/platform/device/device_wrapper.h"
  #include "paddle/fluid/platform/device_context.h"
  #include "paddle/fluid/platform/dynload/dynamic_loader.h"
  #include "paddle/fluid/platform/enforce.h"
  #include "paddle/fluid/platform/init.h"
  #include "paddle/fluid/platform/monitor.h"
  #include "paddle/fluid/platform/place.h"
  #include "paddle/fluid/platform/profiler.h"
  #include "paddle/fluid/pybind/cuda_streams_py.h"
  #include "paddle/fluid/pybind/distributed_py.h"
  #include "paddle/fluid/pybind/eager.h"
  #include "paddle/fluid/pybind/io.h"
  #include "paddle/phi/core/compat/convert_utils.h"
  #include "paddle/phi/core/lod_utils.h"
  #include "paddle/utils/none.h"
  #ifdef PADDLE_WITH_ASCEND
  #include "paddle/fluid/pybind/ascend_wrapper_py.h"
  #endif
  #include "paddle/fluid/pybind/bind_cost_model.h"
  #include "paddle/fluid/pybind/bind_fleet_executor.h"
  #include "paddle/fluid/pybind/box_helper_py.h"
  #include "paddle/fluid/pybind/communication.h"
  #include "paddle/fluid/pybind/compatible.h"
  #include "paddle/fluid/pybind/const_value.h"
  #include "paddle/fluid/pybind/data_set_py.h"
  #include "paddle/fluid/pybind/exception.h"
  #include "paddle/fluid/pybind/fleet_wrapper_py.h"
  #include "paddle/fluid/pybind/generator_py.h"
  #include "paddle/fluid/pybind/global_value_getter_setter.h"
  #include "paddle/fluid/pybind/gloo_context_py.h"
  #include "paddle/fluid/pybind/gloo_wrapper_py.h"
  #include "paddle/fluid/pybind/heter_wrapper_py.h"
  #include "paddle/fluid/pybind/imperative.h"
  #include "paddle/fluid/pybind/inference_api.h"
  #include "paddle/fluid/pybind/ir.h"
  #include "paddle/fluid/pybind/metrics_py.h"
  #include "paddle/fluid/pybind/ps_gpu_wrapper_py.h"
  #include "paddle/fluid/pybind/pybind_boost_headers.h"
  
  #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
  #include "paddle/fluid/pybind/nccl_wrapper_py.h"
  #endif
  #include "paddle/fluid/framework/data_type.h"
  #include "paddle/fluid/pybind/protobuf.h"
  #include "paddle/fluid/pybind/pybind.h"  // NOLINT
  #include "paddle/fluid/pybind/reader_py.h"
  #include "paddle/fluid/pybind/tensor_py.h"
  #include "paddle/fluid/string/to_string.h"
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
  #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
  #include "paddle/fluid/operators/nccl/nccl_gpu_common.h"
  #endif
  #ifndef PADDLE_WITH_HIP
  #include "paddle/fluid/platform/device/gpu/cuda/cuda_profiler.h"
  #endif
  #include "paddle/fluid/platform/device/gpu/gpu_info.h"
  #endif
  
  #ifdef PADDLE_WITH_ASCEND_CL
  #include "paddle/fluid/platform/collective_helper.h"
  #include "paddle/fluid/platform/device/npu/npu_info.h"
  #include "paddle/fluid/platform/device/npu/npu_profiler.h"
  #endif
  
  #ifdef PADDLE_WITH_XPU
  #include "paddle/fluid/platform/device/xpu/xpu_info.h"
  #include "paddle/fluid/platform/device/xpu/xpu_op_list.h"
  #endif
  
  #include "paddle/fluid/platform/cuda_graph_with_memory_pool.h"
  
  #ifdef PADDLE_WITH_IPU
  #include "paddle/fluid/platform/device/ipu/ipu_backend.h"
  #include "paddle/fluid/platform/device/ipu/ipu_info.h"
  #endif
  
  #ifdef PADDLE_WITH_MLU
  #include "paddle/fluid/platform/device/mlu/mlu_info.h"
  #endif
  
  #ifdef PADDLE_WITH_CRYPTO
  #include "paddle/fluid/pybind/crypto.h"
  #endif
  
  #if defined PADDLE_WITH_PSCORE
  #include "paddle/fluid/pybind/fleet_py.h"
  #endif
  
  #include "pybind11/stl.h"
  
  DECLARE_bool(use_mkldnn);
  
  // disable auto conversion to list in Python
  PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
  PYBIND11_MAKE_OPAQUE(paddle::framework::FetchUnmergedList);
  PYBIND11_MAKE_OPAQUE(paddle::framework::FetchList);
  PYBIND11_MAKE_OPAQUE(paddle::framework::FetchType);
  
  namespace paddle {
  namespace pybind {
  
  PyTypeObject *g_place_pytype = nullptr;
  PyTypeObject *g_cudaplace_pytype = nullptr;
  PyTypeObject *g_cpuplace_pytype = nullptr;
  PyTypeObject *g_xpuplace_pytype = nullptr;
  PyTypeObject *g_npuplace_pytype = nullptr;
  PyTypeObject *g_cudapinnedplace_pytype = nullptr;
  PyTypeObject *g_mluplace_pytype = nullptr;
  PyTypeObject *g_framework_tensor_pytype = nullptr;
  PyTypeObject *g_framework_lodtensorarray_pytype = nullptr;
  
  bool IsCompiledWithCUDA() {
  #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP)
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithNCCL() {
  #ifdef PADDLE_WITH_NCCL
    return true;
  #else
    return false;
  #endif
  }
  
  bool IsCompiledWithROCM() {
  #ifndef PADDLE_WITH_HIP
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithAscend() {
  #ifndef PADDLE_WITH_ASCEND
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithXPU() {
  #ifndef PADDLE_WITH_XPU
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithNPU() {
  #ifndef PADDLE_WITH_ASCEND_CL
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithIPU() {
  #ifndef PADDLE_WITH_IPU
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithMKLDNN() {
  #ifndef PADDLE_WITH_MKLDNN
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithCINN() {
  #ifndef PADDLE_WITH_CINN
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithMLU() {
  #ifndef PADDLE_WITH_MLU
    return false;
  #else
    return true;
  #endif
  }
  
  bool IsCompiledWithHETERPS() {
  #ifndef PADDLE_WITH_HETERPS
    return false;
  #else
    return true;
  #endif
  }
  
  bool SupportsBfloat16() {
  #ifndef PADDLE_WITH_MKLDNN
    return false;
  #else
    if (platform::MayIUse(platform::cpu_isa_t::avx512_core))
      return true;
    else
      return false;
  #endif
  }
  
  bool SupportsBfloat16FastPerformance() {
  #ifndef PADDLE_WITH_MKLDNN
    return false;
  #else
    if (platform::MayIUse(platform::cpu_isa_t::avx512_bf16))
      return true;
    else
      return false;
  #endif
  }
  
  bool SupportsInt8() {
  #ifndef PADDLE_WITH_MKLDNN
    return false;
  #else
    return (platform::MayIUse(platform::cpu_isa_t::avx2) ||
            platform::MayIUse(platform::cpu_isa_t::avx512f));
  #endif
  }
  
  bool SupportsVNNI() {
  #ifndef PADDLE_WITH_MKLDNN
    return false;
  #else
    return platform::MayIUse(platform::cpu_isa_t::avx512_core_vnni);
  #endif
  }
  
  bool IsCompiledWithBrpc() {
  #ifndef PADDLE_WITH_DISTRIBUTE
    return false;
  #endif
    return true;
  }
  
  bool IsCompiledWithDIST() {
  #ifdef PADDLE_WITH_DISTRIBUTE
    return true;
  #else
    return false;
  #endif
  }
  
  template <typename PlaceType1, typename PlaceType2>
  static inline bool IsSamePlace(const PlaceType1 &p1, const PlaceType2 &p2) {
    return paddle::platform::Place(p1) == paddle::platform::Place(p2);
  }
  
  template <typename PlaceType>
  static inline int PlaceIndex(const PlaceType &p) {
    return static_cast<int>(paddle::platform::Place(p).GetType());
  }
  
  static PyObject *GetPythonAttribute(PyObject *obj, const char *attr_name) {
    // NOTE(zjl): PyObject_GetAttrString would return nullptr when attr_name
    // is not inside obj, but it would also set the error flag of Python.
    // If the error flag is set in C++, C++ code would not raise Exception,
    // but Python would raise Exception once C++ call ends.
    // To avoid unexpected Exception raised in Python, we check whether
    // attribute exists before calling PyObject_GetAttrString.
    //
    // Caution: PyObject_GetAttrString would increase reference count of PyObject.
    // Developer should call Py_DECREF manually after the attribute is not used.
    if (PyObject_HasAttrString(obj, attr_name)) {
      return PyObject_GetAttrString(obj, attr_name);
    } else {
      return nullptr;
    }
  }
  
  template <typename T>
  static T PyObjectCast(PyObject *obj) {
    try {
      return py::cast<T>(py::handle(obj));
    } catch (py::cast_error &) {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "Python object is not type of %s, the real type is %s",
          typeid(T).name(), obj->ob_type->tp_name));
    }
  }
  
  using PyNameVarBaseMap = std::unordered_map<std::string, py::handle>;
  
  static std::vector<std::shared_ptr<imperative::VarBase>> GetVarBaseList(
      const PyNameVarBaseMap &state_dict) {
    std::vector<std::shared_ptr<imperative::VarBase>> vec_res;
    vec_res.reserve(state_dict.size());
  
    for (auto &para : state_dict) {
      PyObject *py_obj = para.second.ptr();
      if (!py_obj || py_obj == Py_None) {
        PADDLE_THROW(platform::errors::InvalidArgument(
            "The parameter [%s] to save is None", para.first));
      }
      vec_res.emplace_back(
          PyObjectCast<std::shared_ptr<imperative::VarBase>>(py_obj));
    }
  
    return vec_res;
  }
  
  static std::vector<std::string> inline GetNameList(
      const py::handle &py_handle) {
    std::vector<std::string> vec_res;
  
    PyObject *py_obj = py_handle.ptr();  // get underlying PyObject
    // Python None is not nullptr in C++!
    if (!py_obj || py_obj == Py_None) {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "The parameter list to save is None"));
    }
  
    if (PyList_Check(py_obj)) {
      size_t len = PyList_GET_SIZE(py_obj);
  
      vec_res.reserve(len);
  
      const char *kNameField = "name";
  
      for (size_t i = 0; i < len; ++i) {
        PyObject *py_name =
            PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kNameField);
        PADDLE_ENFORCE_NOT_NULL(py_name,
                                platform::errors::InvalidArgument(
                                    "The name of parameter to save is None"));
        vec_res.emplace_back(PyObjectCast<std::string>(py_name));
        Py_DECREF(py_name);
      }
    } else {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "The parameters to save is not a list"));
    }
    return vec_res;
  }
  
  static void inline CreateVariableIfNotExit(
      const py::handle &py_handle, const framework::Scope &scope,
      const framework::Executor *exe = nullptr) {
    std::vector<std::string> vec_res;
  
    PyObject *py_obj = py_handle.ptr();  // get underlying PyObject
    // Python None is not nullptr in C++!
    if (!py_obj || py_obj == Py_None) {
      PADDLE_THROW(
          platform::errors::InvalidArgument("The parameter list to set is None"));
    }
  
    if (PyList_Check(py_obj)) {
      size_t len = PyList_GET_SIZE(py_obj);
  
      vec_res.reserve(len);
  
      const char *kNameField = "name";
      const char *kVarDescField = "desc";
  
      for (size_t i = 0; i < len; ++i) {
        PyObject *py_name =
            PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kNameField);
        PADDLE_ENFORCE_NOT_NULL(py_name,
                                platform::errors::InvalidArgument(
                                    "The name of parameter to set is None"));
        auto para_name = PyObjectCast<std::string>(py_name);
        Py_DECREF(py_name);
  
        auto var = scope.FindVar(para_name);
        if (var == nullptr) {
          PADDLE_ENFORCE_NOT_NULL(exe,
                                  platform::errors::InvalidArgument(
                                      "Parameter not Initialized, "
                                      "Please set argument [executor] not None "
                                      "or run startup program first"));
          PyObject *py_var_desc =
              PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kVarDescField);
          PADDLE_ENFORCE_NOT_NULL(
              py_var_desc, platform::errors::InvalidArgument(
                               "The var_desc of parameter to set is None"));
          auto var_desc = PyObjectCast<framework::VarDesc>(py_var_desc);
          Py_DECREF(py_var_desc);
          var = const_cast<framework::Scope *>(&scope)->Var(para_name);
          auto *tensor_temp = var->GetMutable<framework::LoDTensor>();
          tensor_temp->Resize(phi::make_ddim(var_desc.GetShape()));
          tensor_temp->mutable_data(
              exe->GetPlace(),
              framework::TransToPhiDataType(var_desc.GetDataType()));
        }
      }
    } else {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "The parameters to set is not a list"));
    }
  
    return;
  }
  
  static void AssertStaticGraphAndDygraphGradMakerNoDiff() {
    std::set<std::string> ops;
    for (auto &pair : framework::OpInfoMap::Instance().map()) {
      bool has_static_grad_maker = (pair.second.grad_op_maker_ != nullptr);
      bool has_dygraph_grad_maker =
          (pair.second.dygraph_grad_op_maker_ != nullptr);
      if (has_static_grad_maker ^ has_dygraph_grad_maker) {
        bool has_kernel =
            (framework::OperatorWithKernel::AllOpKernels().count(pair.first) > 0);
        if (has_kernel) {
          ops.insert(pair.first);
        } else {
          VLOG(5) << pair.first << " has no kernels, skip";
        }
      }
    }
    PADDLE_ENFORCE_EQ(ops.empty(), true,
                      platform::errors::Unimplemented(
                          "OperatorWithKernel [%s] have only static graph grad "
                          "maker or have only dygraph grad maker, which is not "
                          "allowed",
                          string::join_strings(ops, ',')));
  }
  
  #ifdef PADDLE_WITH_NCCL
  static int GetNCCLVersion() {
  #if NCCL_VERSION_CODE >= 2304
    int ver;
    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGetVersion(&ver));
    return ver;
  #else
    PADDLE_THROW(platform::errors::External(
        "Cannot get NCCL version successfully when nccl version < 2.3.4"));
  #endif
  }
  #endif
  
  template <typename PlaceType>
  static void TensorCopyFrom(framework::Tensor *dst, const framework::Tensor &src,
                             const PlaceType &place, int64_t batch_size) {
    if (batch_size < 0) {
      framework::TensorCopy(src, place, dst);
    } else {
      auto sliced = src.Slice(0, batch_size);
      framework::TensorCopy(sliced, place, dst);
    }
  }
  
  #ifdef PADDLE_WITH_AVX
  PYBIND11_MODULE(core_avx, m) {
  #else
  PYBIND11_MODULE(core_noavx, m) {
  #endif
  
    BindEager(&m);
    BindCudaStream(&m);
  
    // Not used, just make sure cpu_info.cc is linked.
    paddle::platform::CpuTotalPhysicalMemory();
  
    paddle::memory::allocation::UseAllocatorStrategyGFlag();
  
    AssertStaticGraphAndDygraphGradMakerNoDiff();
  
    m.doc() = "C++ core of PaddlePaddle";
  
    // using framework in this function. Since it is inside a function, it will
    // not cause namespace pollution.
    using namespace paddle::framework;  // NOLINT
  
    BindException(&m);
  
    m.def("set_num_threads", &platform::SetNumThreads);
  
    m.def("disable_signal_handler", &DisableSignalHandler);
  
    m.def("clear_gradients",
          [](std::vector<std::shared_ptr<imperative::VarBase>> param_list,
             bool set_to_zero) {
            for (auto param : param_list) {
              param->ClearGradient(set_to_zero);
            }
          });
  
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    m.def("cudnn_version", &platform::DnnVersion);
    m.def("gpu_memory_available", []() {
      size_t available = 0;
      size_t total = 0;
      paddle::platform::GpuMemoryUsage(&available, &total);
      return available;
    });
  #endif
  
  #ifdef PADDLE_WITH_NCCL
    m.def("nccl_version", &GetNCCLVersion);
  #endif
  
    m.def("is_cuda_graph_capturing", &platform::IsCUDAGraphCapturing);
  #ifdef PADDLE_WITH_CUDA
    py::class_<platform::CUDAGraph>(m, "CUDAGraph")
        .def_static("begin_capture",
                    [](platform::CUDAPlace place, int mode) {
                      platform::BeginCUDAGraphCapture(
                          place, static_cast<cudaStreamCaptureMode>(mode));
                    })
        .def_static("end_capture", &platform::EndCUDAGraphCapture)
        .def("replay", &platform::CUDAGraph::Replay)
        .def("reset", &platform::CUDAGraph::Reset)
        .def("print_to_dot_files", &platform::CUDAGraph::PrintToDotFiles);
  #endif
  
    m.def("wait_device", [](const platform::Place &place) {
      platform::DeviceContextPool::Instance().Get(place)->Wait();
    });
  
    m.def("from_dlpack", [](py::capsule *dltensor) {
      DLManagedTensor *dmt = reinterpret_cast<DLManagedTensor *>(
          PyCapsule_GetPointer(dltensor->ptr(), "dltensor"));
  
      PADDLE_ENFORCE_NOT_NULL(
          dmt, platform::errors::InvalidArgument(
                   "from_dlpack received an invalid capsule. "
                   "Note that a DLPack tensor can be consumed only once."));
  
      PyCapsule_SetName(dltensor->ptr(), "used_dltensor");
      DLTensor dl = dmt->dl_tensor;
      framework::Tensor tensor;
  
      if (dl.device.device_type == kDLCPU) {
        paddle::framework::TensorFromDLPack(dl, &tensor);
      }
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
      if (dl.device.device_type == kDLGPU) {
        paddle::framework::TensorFromDLPack(dl, &tensor);
      }
  #endif
      return tensor;
    });
  
    m.def("_create_loaded_parameter",
          [](const py::handle &vec_var_list, const Scope &scope,
             const Executor *executor) {
            CreateVariableIfNotExit(vec_var_list, scope, executor);
          });
  
    m.def("save_op_version_info", [](framework::ProgramDesc &desc) {
      framework::compatible::pb::OpVersionMap pb_vmap{desc.OpVersionMap()};
      framework::compatible::SaveOpVersions(
          framework::compatible::OpVersionRegistrar::GetInstance()
              .GetVersionMap(),
          &pb_vmap);
    });
  
    m.def("set_printoptions", [](const py::kwargs &kwargs) {
      auto &print_opt = framework::PrintOptions::Instance();
      if (kwargs.contains("precision")) {
        print_opt.precision = kwargs["precision"].cast<int>();
      }
      if (kwargs.contains("threshold")) {
        print_opt.threshold = kwargs["threshold"].cast<int>();
      }
      if (kwargs.contains("edgeitems")) {
        print_opt.edgeitems = kwargs["edgeitems"].cast<int>();
      }
      if (kwargs.contains("linewidth")) {
        print_opt.linewidth = kwargs["linewidth"].cast<int>();
      }
      if (kwargs.contains("sci_mode")) {
        print_opt.sci_mode = kwargs["sci_mode"].cast<bool>();
      }
  
      VLOG(4) << "Set printoptions: precision=" << print_opt.precision
              << ", threshold=" << print_opt.threshold
              << ", edgeitems=" << print_opt.edgeitems
              << ", linewidth=" << print_opt.linewidth
              << ", sci_mode=" << print_opt.sci_mode;
    });
  
    m.def("broadcast_shape", [](const std::vector<int64_t> &x_dim,
                                const std::vector<int64_t> &y_dim) {
      return phi::vectorize(operators::details::BroadcastTwoDims(
          phi::make_ddim(x_dim), phi::make_ddim(y_dim), -1));
    });
  
    m.def(
        "_append_python_callable_object_and_return_id",
        [](py::object py_obj) -> size_t {
          return paddle::operators::AppendPythonCallableObjectAndReturnId(py_obj);
        });
  
    m.def("_get_use_default_grad_op_desc_maker_ops",
          [] { return OpInfoMap::Instance().GetUseDefaultGradOpDescMakerOps(); });
  
    m.def("_get_all_register_op_kernels",
          [](const std::string &lib) {
            std::unordered_map<std::string, std::vector<std::string>>
                all_kernels_info;
            if (lib == "fluid" || lib == "all") {
              auto &all_kernels =
                  paddle::framework::OperatorWithKernel::AllOpKernels();
  
              for (auto &kernel_pair : all_kernels) {
                auto op_type = kernel_pair.first;
                std::vector<std::string> kernel_types;
                for (auto &info_pair : kernel_pair.second) {
                  paddle::framework::OpKernelType kernel_type = info_pair.first;
                  kernel_types.emplace_back(
                      paddle::framework::KernelTypeToString(kernel_type));
                }
                all_kernels_info.emplace(op_type, kernel_types);
              }
            }
            if (lib == "phi" || lib == "all") {
              auto phi_kernels = phi::KernelFactory::Instance().kernels();
              for (auto &kernel_pair : phi_kernels) {
                auto op_type = phi::TransToFluidOpName(kernel_pair.first);
                std::vector<std::string> kernel_types;
                for (auto &info_pair : kernel_pair.second) {
                  framework::OpKernelType kernel_type =
                      framework::TransPhiKernelKeyToOpKernelType(info_pair.first);
                  auto kernel_type_str =
                      framework::KernelTypeToString(kernel_type);
                  if (all_kernels_info.count(op_type)) {
                    if (std::find(all_kernels_info[op_type].begin(),
                                  all_kernels_info[op_type].end(),
                                  kernel_type_str) ==
                        all_kernels_info[op_type].end()) {
                      all_kernels_info[op_type].emplace_back(kernel_type_str);
                    }
                  } else {
                    kernel_types.emplace_back(kernel_type_str);
                  }
                }
                if (!kernel_types.empty()) {
                  all_kernels_info.emplace(op_type, kernel_types);
                }
              }
            }
  
            return all_kernels_info;
          },
          py::arg("lib") = "all",
          R"DOC(
             Return the registered kernels in paddle.
  
             Args:
                 lib[string]: the libarary, could be 'phi', 'fluid' and 'all'.
             )DOC");
  
    // NOTE(zjl): ctest would load environment variables at the beginning even
    // though we have not `import paddle.fluid as fluid`. So we add this API
    // to enable eager deletion mode in unittest.
    m.def("_set_eager_deletion_mode", &paddle::framework::SetEagerDeletionMode);
  
    m.def("_set_fuse_parameter_group_size",
          &paddle::framework::ir::SetFuseParameterGroupsSize);
    m.def("_set_fuse_parameter_memory_size",
          &paddle::framework::ir::SetFuseParameterMemorySize);
  
    m.add_object("_cleanup",
                 py::capsule([]() { ScopePool::Instance().Clear(); }));
  
    m.def("_set_paddle_lib_path", &paddle::platform::dynload::SetPaddleLibPath);
  
    m.def("_promote_types_if_complex_exists",
          &paddle::framework::PromoteTypesIfComplexExists);
  
    BindImperative(&m);
  
    py::class_<framework::Tensor> framework_tensor(m, "Tensor",
                                                   py::buffer_protocol());
    g_framework_tensor_pytype =
        reinterpret_cast<PyTypeObject *>(framework_tensor.ptr());
    framework_tensor
        .def("__array__",
             [](framework::Tensor &self) { return TensorToPyArray(self); })
        .def("_ptr",
             [](const framework::Tensor &self) {
               return reinterpret_cast<uintptr_t>(self.data());
             })
        .def("_is_initialized",
             [](const framework::Tensor &self) { return self.IsInitialized(); })
        .def("_get_dims",
             [](const framework::Tensor &self) { return vectorize(self.dims()); })
        .def("_set_dims",
             [](framework::Tensor &self, const std::vector<int64_t> &dim) {
               self.Resize(phi::make_ddim(dim));
             })
        .def("_set_layout",
             [](framework::Tensor &self, const std::string &layout) {
               self.set_layout(StringToDataLayout(layout));
             })
        .def("_alloc_float",
             [](framework::Tensor &self, paddle::platform::CUDAPlace &place) {
               self.mutable_data<float>(place);
             })
        .def("_alloc_float",
             [](framework::Tensor &self, paddle::platform::XPUPlace &place) {
               self.mutable_data<float>(place);
             })
        .def("_alloc_float",
             [](framework::Tensor &self, paddle::platform::CPUPlace &place) {
               self.mutable_data<float>(place);
             })
        .def("_alloc_float",
             [](framework::Tensor &self, paddle::platform::NPUPlace &place) {
               self.mutable_data<float>(place);
             })
        .def("_alloc_float",
             [](framework::Tensor &self, paddle::platform::MLUPlace &place) {
               self.mutable_data<float>(place);
             })
        .def("_alloc_double",
             [](framework::Tensor &self, paddle::platform::CPUPlace &place) {
               self.mutable_data<double>(place);
             })
        .def("_alloc_int",
             [](framework::Tensor &self, paddle::platform::CPUPlace &place) {
               self.mutable_data<int>(place);
             })
        .def("_alloc_int",
             [](framework::Tensor &self, paddle::platform::XPUPlace &place) {
               self.mutable_data<int>(place);
             })
        .def("_alloc_int",
             [](framework::Tensor &self, paddle::platform::CUDAPlace &place) {
               self.mutable_data<int>(place);
             })
        .def("_alloc_int",
             [](framework::Tensor &self, paddle::platform::MLUPlace &place) {
               self.mutable_data<int>(place);
             })
        .def("_alloc_int",
             [](framework::Tensor &self,
                paddle::platform::CUDAPinnedPlace &place) {
               self.mutable_data<int>(place);
             })
        .def("_alloc_float",
             [](framework::Tensor &self,
                paddle::platform::CUDAPinnedPlace &place) {
               self.mutable_data<float>(place);
             })
        .def("_mutable_data",
             [](framework::Tensor &self, paddle::platform::CPUPlace &place,
                paddle::framework::proto::VarType::Type type) {
               return reinterpret_cast<uintptr_t>(
                   self.mutable_data(place, framework::TransToPhiDataType(type)));
             })
        .def("_mutable_data",
             [](framework::Tensor &self, paddle::platform::XPUPlace &place,
                paddle::framework::proto::VarType::Type type) {
               return reinterpret_cast<uintptr_t>(
                   self.mutable_data(place, framework::TransToPhiDataType(type)));
             })
        .def("_mutable_data",
             [](framework::Tensor &self, paddle::platform::CUDAPlace &place,
                paddle::framework::proto::VarType::Type type) {
               return reinterpret_cast<uintptr_t>(
                   self.mutable_data(place, framework::TransToPhiDataType(type)));
             })
        .def("_mutable_data",
             [](framework::Tensor &self, paddle::platform::CUDAPinnedPlace &place,
                paddle::framework::proto::VarType::Type type) {
               return reinterpret_cast<uintptr_t>(
                   self.mutable_data(place, framework::TransToPhiDataType(type)));
             })
        .def("_mutable_data",
             [](framework::Tensor &self, paddle::platform::MLUPlace &place,
                paddle::framework::proto::VarType::Type type) {
               return reinterpret_cast<uintptr_t>(
                   self.mutable_data(place, framework::TransToPhiDataType(type)));
             })
        .def("_clear", &framework::Tensor::clear)
        .def("_mutable_data",
             [](framework::Tensor &self, paddle::platform::NPUPlace &place,
                paddle::framework::proto::VarType::Type type) {
               return reinterpret_cast<uintptr_t>(
                   self.mutable_data(place, framework::TransToPhiDataType(type)));
             })
        .def("_copy_from", &TensorCopyFrom<paddle::platform::CPUPlace>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("_copy_from", &TensorCopyFrom<paddle::platform::XPUPlace>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPlace>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("_copy_from", &TensorCopyFrom<paddle::platform::NPUPlace>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPinnedPlace>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("_copy_from", &TensorCopyFrom<paddle::platform::MLUPlace>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("_copy_from", &TensorCopyFrom<paddle::platform::Place>,
             py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
        .def("set", SetTensorFromPyArray<paddle::platform::CPUPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
        .def("set", SetTensorFromPyArray<paddle::platform::XPUPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
        .def("set", SetTensorFromPyArray<paddle::platform::CUDAPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
        .def("set", SetTensorFromPyArray<paddle::platform::NPUPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
        .def("set", SetTensorFromPyArray<paddle::platform::IPUPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
        .def("set", SetTensorFromPyArray<paddle::platform::MLUPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
        .def("set", SetTensorFromPyArray<paddle::platform::CUDAPinnedPlace>,
             py::arg("array"), py::arg("place"), py::arg("zero_copy") = false,
             R"DOC(
          Set the data of Tensor on place with given numpy array.
          
          Args:
            lod (numpy.ndarray): The data to set.
            place (CPUPlace|CUDAPlace|XPUPlace|IPUPlace|CUDAPinnedPlace|NPUPlace|MLUPlace): The place where the
            Tensor is to be set.
            zero_copy (bool, optional): Whether to share memory with the input numpy array.
            This parameter only works with CPUPlace. Default: False.
  
          Returns:
              None.
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import numpy as np
  
                  t = fluid.Tensor()
                  t.set(np.ndarray([5, 30]), fluid.CPUPlace())
            )DOC")
  
        .def("shape",
             [](framework::Tensor &self) { return vectorize(self.dims()); },
             R"DOC(
             Return the shape of Tensor.
  
             Returns:
                 list[int]: The shape of Tensor.
  
  
             Examples:
                 .. code-block:: python
  
                    import paddle.fluid as fluid
                    import numpy as np
  
                    t = fluid.Tensor()
                    t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                    print(t.shape())  # [5, 30]
             )DOC")
        .def("_to_dlpack",
             [](framework::Tensor &self) {
               DLPackTensor dlpack_tensor(self, 1);
               DLManagedTensor *dmt = dlpack_tensor.ToDLManagedTensor();
               auto capsule = py::capsule(
                   static_cast<void *>(dmt), "dltensor", [](PyObject *ptr) {
                     if (ptr) {
                       auto dltensor = new DLManagedTensor;
                       try {
                         dltensor = reinterpret_cast<DLManagedTensor *>(
                             PyCapsule_GetPointer(ptr, "used_dltensor"));
                         return;
                       } catch (...) {
                         dltensor = reinterpret_cast<DLManagedTensor *>(
                             PyCapsule_GetPointer(ptr, "dltensor"));
                       }
                       dltensor->deleter(dltensor);
                     }
                   });
               return capsule;
             })
        .def("_set_float_element", TensorSetElement<float>)
        .def("_get_float_element", TensorGetElement<float>)
        .def("_set_double_element", TensorSetElement<double>)
        .def("_get_double_element", TensorGetElement<double>)
        .def("_place", [](framework::Tensor &self) { return self.place(); })
        .def("_dtype",
             [](framework::Tensor &self) {
               return framework::TransToProtoVarType(self.type());
             })
        .def("_layout",
             [](framework::Tensor &self) {
               return DataLayoutToString(self.layout());
             })
        .def("_share_data_with", &framework::Tensor::ShareDataWith)
        .def("__getitem__", PySliceTensor, py::return_value_policy::reference)
        .def("__str__",
             [](const framework::Tensor &self) {
               std::stringstream ostr;
               ostr << self;
               return ostr.str();
             }) /* ------ End of original Tensor ------ */
        .def(
            "__init__",
            [](framework::Tensor &instance, const std::vector<std::vector<size_t>>
                                                &recursive_sequence_lengths) {
              LoD new_lod;
              new_lod.reserve(recursive_sequence_lengths.size());
              std::copy(recursive_sequence_lengths.begin(),
                        recursive_sequence_lengths.end(),
                        std::back_inserter(new_lod));
              LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
              PADDLE_ENFORCE_EQ(
                  CheckLoD(new_offset_lod, -1), true,
                  platform::errors::InvalidArgument(
                      "The provided recursive_sequence_lengths info is "
                      "invalid, "
                      "the LoD converted by recursive_sequence_lengths is %s",
                      new_lod));
              new (&instance) framework::Tensor(new_offset_lod);
            })
        .def("__init__",
             [](framework::Tensor &instance) {
               new (&instance) framework::Tensor();
             })
        // We implement offset based LOD in C++ while we use length based with
        // Python API. So we changed set_lod to set_recursive_sequence_lengths
        // to
        // avoid misuse.
        // The discussion is here:
        // https://github.com/PaddlePaddle/Paddle/issues/10855
        .def("set_lod",
             [](framework::Tensor &self,
                const std::vector<std::vector<size_t>> &lod) {
               // the input lod is offset-based level-of-detail info
               LoD new_lod;
               new_lod.reserve(lod.size());
               std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
               PADDLE_ENFORCE_EQ(
                   CheckLoD(new_lod, vectorize(self.dims()).front()), true,
                   platform::errors::InvalidArgument(
                       "The provided LoD is invalid, the LoD is %s", new_lod));
               self.set_lod(new_lod);
             },
             py::arg("lod"), R"DOC(
             Set LoD of the Tensor.
  
             Args:
                 lod (list[list[int]]): The lod to set.
  
             Returns:
                  None.
  
             Examples:
                 .. code-block:: python
  
                   import paddle.fluid as fluid
                   import numpy as np
  
                   t = fluid.Tensor()
                   t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                   t.set_lod([[0, 2, 5]])
                   print(t.lod()) # [[0, 2, 5]]
             )DOC")
        .def("set_recursive_sequence_lengths",
             [](framework::Tensor &self, const std::vector<std::vector<size_t>>
                                             &recursive_sequence_lengths) {
               // the input recursive_sequence_lengths is length-based
               // level-of-detail info
               LoD new_lod;
               new_lod.reserve(recursive_sequence_lengths.size());
               std::copy(recursive_sequence_lengths.begin(),
                         recursive_sequence_lengths.end(),
                         std::back_inserter(new_lod));
               LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
               PADDLE_ENFORCE_EQ(
                   CheckLoD(new_offset_lod, vectorize(self.dims()).front()), true,
                   platform::errors::InvalidArgument(
                       "The provided recursive_sequence_lengths info is "
                       "invalid, "
                       "the LoD converted by recursive_sequence_lengths is "
                       "%s",
                       new_lod));
               self.set_lod(new_offset_lod);
             },
             py::arg("recursive_sequence_lengths"), R"DOC(
             Set LoD of the Tensor according to recursive sequence lengths.
  
             For example, if recursive_sequence_lengths=[[2, 3]], which means
             there are two sequences with length 2 and 3 respectively, the
             corresponding lod would be [[0, 2, 2+3]], i.e., [[0, 2, 5]].
  
             Args:
                  recursive_sequence_lengths (list[list[int]]): The recursive sequence lengths.
             
             Returns:
                  None.
  
             Examples:
                 .. code-block:: python
  
                   import paddle.fluid as fluid
                   import numpy as np
  
                   t = fluid.Tensor()
                   t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                   t.set_recursive_sequence_lengths([[2, 3]])
                   print(t.recursive_sequence_lengths())  # [[2, 3]]
                   print(t.lod())  # [[0, 2, 5]]
             )DOC")
        .def("lod",
             [](framework::Tensor &self) -> std::vector<std::vector<size_t>> {
               // output the offset-based lod info
               LoD lod = self.lod();
               std::vector<std::vector<size_t>> new_lod;
               new_lod.reserve(lod.size());
               std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
               return new_lod;
             },
             R"DOC(
             Return the LoD of the Tensor.
  
             Returns:
                 list[list[int]]: The lod of the Tensor.
             
             Examples:
                 .. code-block:: python
  
                   import paddle.fluid as fluid
                   import numpy as np
  
                   t = fluid.Tensor()
                   t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                   t.set_lod([[0, 2, 5]])
                   print(t.lod()) # [[0, 2, 5]]
             )DOC")
        // Set above comments of set_lod.
        .def("recursive_sequence_lengths",
             [](framework::Tensor &self) -> std::vector<std::vector<size_t>> {
               // output the length-based lod info
               LoD lod = phi::ConvertToLengthBasedLoD(self.lod());
               std::vector<std::vector<size_t>> new_lod;
               new_lod.reserve(lod.size());
               std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
               return new_lod;
             },
             R"DOC(
             Return the recursive sequence lengths corresponding to of the LodD 
             of the Tensor.
  
             Returns:
                  list[list[int]]: The recursive sequence lengths.
  
             Examples:
                 .. code-block:: python
  
                   import paddle.fluid as fluid
                   import numpy as np
  
                   t = fluid.Tensor()
                   t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                   t.set_recursive_sequence_lengths([[2, 3]])
                   print(t.recursive_sequence_lengths()) # [[2, 3]]
             )DOC")
        .def("has_valid_recursive_sequence_lengths",
             [](framework::Tensor &self) -> bool {
               // Check that the lod info is valid and match the outermost
               // dimension of the Tensor data
               return CheckLoD(self.lod(), vectorize(self.dims()).front());
             },
             R"DOC(
             Check whether the LoD of the Tensor is valid.
  
             Returns:
                 bool: Whether the LoD is valid.
  
             Examples:
                 .. code-block:: python
  
                   import paddle.fluid as fluid
                   import numpy as np
  
                   t = fluid.Tensor()
                   t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                   t.set_recursive_sequence_lengths([[2, 3]])
                   print(t.has_valid_recursive_sequence_lengths()) # True
             )DOC")
        .def("_as_type",
             [](const framework::Tensor &self,
                paddle::framework::proto::VarType::Type type) {
               framework::Tensor dst;
               if (self.IsInitialized() && self.numel() > 0) {
                 TransDataType(self, type, &dst);
               }
               return dst;
             })
        .def("_copy",
             [](const framework::Tensor &self, const platform::Place &place) {
               // follow fetch_op's inplementation
               framework::Tensor dst;
               if (self.IsInitialized() && self.numel() > 0) {
                 TensorCopySync(self, place, &dst);
               } else {
                 // Not copy, if the src tensor is empty.
                 dst.clear();
                 dst.Resize({0});
               }
               dst.set_lod(self.lod());
               return dst;
  #ifdef _WIN32
             });
  #else
             })
        .def(py::pickle(
            [](const framework::Tensor &t) {  // __getstate__
              auto holder = t.Holder();
              PADDLE_ENFORCE_EQ(platform::is_cpu_place(holder->place()), true,
                                platform::errors::PreconditionNotMet(
                                    "Tensor is not on CPU."
                                    "Now only Tensor on CPU can be serialized."));
              auto *mmap_writer_allocation =
                  dynamic_cast<memory::allocation::MemoryMapWriterAllocation *>(
                      holder.get());
              PADDLE_ENFORCE_NOT_NULL(
                  mmap_writer_allocation,
                  platform::errors::PreconditionNotMet(
                      "Tensor is not in shared memory."
                      "Now only Tensor on shared memory can be serialized."));
              int type_idx = static_cast<int>(t.type());
  
              return py::make_tuple(mmap_writer_allocation->ipc_name(),
                                    mmap_writer_allocation->size(), type_idx,
                                    vectorize(t.dims()), t.lod());
            },
            [](py::tuple t) {  // __setstate__
              if (t.size() != 5)
                throw std::runtime_error("Invalid Tensor state!");
  
              // 1. Create a new C++ instance
              framework::Tensor tensor;
  
              // 2. Rebuild Allocation
              const std::string &ipc_name = t[0].cast<std::string>();
              size_t size = t[1].cast<size_t>();
              auto shared_reader_holder =
                  memory::allocation::RebuildMemoryMapReaderAllocation(ipc_name,
                                                                       size);
  
              // 3. Maintain global fd set
              VLOG(3) << "Tensor ipc name: " << ipc_name;
              memory::allocation::MemoryMapFdSet::Instance().Insert(ipc_name);
  
              // 4. Rebuild Tensor
              tensor.ResetHolderWithType(
                  shared_reader_holder,
                  static_cast<paddle::experimental::DataType>(t[2].cast<int>()));
              tensor.Resize(phi::make_ddim(t[3].cast<std::vector<int>>()));
              tensor.set_lod(t[4].cast<framework::LoD>());
  
              return tensor;
            }));
  #endif
  
    py::class_<phi::SelectedRows>(m, "SelectedRows")
        .def("__init__",
             [](phi::SelectedRows &instance) {
               new (&instance) phi::SelectedRows();
             })
        .def("__init__",
             [](phi::SelectedRows &instance, const std::vector<int64_t> rows,
                const int64_t &height) {
               new (&instance) phi::SelectedRows(rows, height);
             })
        .def("get_tensor",
             [](phi::SelectedRows &self) { return self.mutable_value(); },
             py::return_value_policy::reference)
        .def("numel",
             [](phi::SelectedRows &self) -> int64_t {
               return self.value().numel();
             })
        .def("set_height", &phi::SelectedRows::set_height)
        .def("height", &phi::SelectedRows::height)
        .def("set_rows",
             [](phi::SelectedRows &self, std::vector<int64_t> rows) {
  #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP)
               self.set_rows(rows);
  #else
          Vector<int64_t> new_rows(rows);
          self.set_rows(new_rows);
  #endif
             })
        .def("sync_index",
             [](phi::SelectedRows &instance) { instance.SyncIndex(); })
        .def("rows", [](phi::SelectedRows &self) {
          auto rows = self.rows();
          std::vector<int64_t> new_rows;
          new_rows.reserve(rows.size());
          std::copy(rows.begin(), rows.end(), std::back_inserter(new_rows));
          return new_rows;
        });
  
    py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
  
  All parameter, weight, gradient are variables in Paddle.
  )DOC")
        .def(py::init<>())
        .def("is_int", [](const Variable &var) { return var.IsType<int>(); })
        .def("set_int",
             [](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
        .def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
        .def("is_float", [](const Variable &var) { return var.IsType<float>(); })
        .def("set_float",
             [](Variable &var, float val) -> void {
               *var.GetMutable<float>() = val;
             })
        .def("get_float",
             [](const Variable &var) -> float { return var.Get<float>(); })
        .def("get_tensor",
             [](Variable &self) -> LoDTensor * {
               return self.GetMutable<LoDTensor>();
             },
             py::return_value_policy::reference)
        .def("get_bytes",
             [](Variable &self) {
               return py::bytes(*self.GetMutable<std::string>());
             })
        .def("set_string_list",
             [](Variable &self, Strings str_list) {
               *self.GetMutable<Strings>() = str_list;
             })
        .def("set_vocab", [](Variable &self,
                             Vocab vocab) { *self.GetMutable<Vocab>() = vocab; })
        .def("get_string_tensor",
             [](Variable &self) { return self.GetMutable<Strings>(); },
             py::return_value_policy::reference)
        .def("get_map_tensor",
             [](Variable &self) { return self.GetMutable<Vocab>(); },
             py::return_value_policy::reference)
        .def("get_lod_rank_table",
             [](Variable &self) { return self.GetMutable<LoDRankTable>(); },
             py::return_value_policy::reference)
        .def("get_selected_rows",
             [](Variable &self) -> phi::SelectedRows * {
               return self.GetMutable<phi::SelectedRows>();
             },
             py::return_value_policy::reference)
        .def("get_lod_tensor_array",
             [](Variable &self) { return self.GetMutable<LoDTensorArray>(); },
             py::return_value_policy::reference)
        .def("get_fetch_list",
             [](Variable &self) { return self.GetMutable<FetchList>(); },
             py::return_value_policy::reference)
  #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
        .def("get_communicator",
             [](Variable &self) -> platform::Communicator * {
               return self.GetMutable<platform::Communicator>();
             },
             py::return_value_policy::reference)
  #endif
        .def("get_reader",
             [](Variable &self) -> framework::ReaderHolder * {
               PADDLE_ENFORCE_EQ(
                   self.IsType<framework::ReaderHolder>(), true,
                   platform::errors::InvalidArgument(
                       "The variable is not type of ReaderHolder."));
               return self.GetMutable<framework::ReaderHolder>();
             },
             py::return_value_policy::reference)
        .def("get_scope",
             [](Variable &self) -> Scope * {
               auto scope_vec =
                   self.GetMutable<std::vector<framework::Scope *>>();
               PADDLE_ENFORCE_GT(
                   scope_vec->size(), 0,
                   platform::errors::InvalidArgument(
                       "The size of scope_vec should be greater than 0"));
               return scope_vec->front();
             },
             py::return_value_policy::reference)
        .def("set_scope", [](Variable &self, Scope &scope) {
          auto scope_vec = self.GetMutable<std::vector<framework::Scope *>>();
          scope_vec->emplace_back(&scope);
        });
  
    BindReader(&m);
  
    py::class_<Scope>(m, "_Scope", R"DOC(
      Scope is an association of a name to Variable. All variables belong to Scope.
  
      Variables in a parent scope can be retrieved from local scope.
  
      You need to specify a scope to run a Net, i.e., `exe.Run(&scope)`.
      One net can run in different scopes and update different variable in the
      scope.
  
      You can create var in a scope and get it from the scope.
  
      Examples:
          .. code-block:: python
  
            import paddle.fluid as fluid
            # create tensor from a scope and set value to it.
            param = scope.var('Param').get_tensor()
            param_array = np.full((height, row_numel), 5.0).astype("float32")
            param.set(param_array, place)
  
          )DOC")
        .def("_remove_from_pool",
             [](Scope &self) { ScopePool::Instance().Remove(&self); })
        .def("var",
             [](Scope &self, const std::string &name) -> Variable * {
               return self.Var(name);
             },
             py::arg("name"),
             R"DOC(
             Find or create variable named :code:`name` in the current scope.
  
             If the variable named :code:`name` does not exist in the
             current scope, the variable would be created. Otherwise,
             return the existing variable.
  
             Args:
                 name (str): the variable name.
  
             Returns:
                 out (core.Variable): the found or created variable.
             )DOC",
             py::return_value_policy::reference)
        .def("find_var", &Scope::FindVar, py::arg("name"),
             R"DOC(
             Find variable named :code:`name` in the current scope or
             its parent scope. Return None if not found. 
  
             Args:
                 name (str): the variable name.
  
             Returns:
                 out (core.Variable|None): the found variable or None.
             )DOC",
             py::return_value_policy::reference)
        .def("erase", &Scope::EraseVars, py::arg("names"),
             R"DOC(
             Find variable named :code:`name` in the current scope or
             its parent scope. Return None if not found. 
  
             Args:
                 name (str): the variable names to be erase.
  
             Returns:
                 None
             )DOC",
             py::return_value_policy::reference)
        .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
             R"DOC(
             Create a new sub-scope of the current scope.
  
             Returns:
                 out (core._Scope): the created sub-scope.
             )DOC",
             py::return_value_policy::reference)
        .def("drop_kids", &Scope::DropKids,
             R"DOC(
             Delete all sub-scopes of the current scope.
             )DOC")
        .def("_kids", &Scope::kids);
  
    m.def("Scope",
          []() -> Scope * {
            auto *s = new Scope();
            ScopePool::Instance().Insert(std::unique_ptr<Scope>(s));
            return s;
          },
          R"DOC(
          Create a new scope.
  
          Returns:
              out (core._Scope): the created scope.
          )DOC",
          py::return_value_policy::reference);
  
    //! @note: Be careful! PyBind will return std::string as an unicode, not
    //! Python str. If you want a str object, you should cast them in Python.
    m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
      std::vector<py::bytes> ret_values;
      for (auto &iter : OpInfoMap::Instance().map()) {
        auto &info = iter.second;
        if (info.HasOpProtoAndChecker()) {
          std::string str;
          PADDLE_ENFORCE_EQ(
              info.Proto().SerializeToString(&str), true,
              platform::errors::Fatal(
                  "Serialize OpProto Error. This could be a bug of Paddle."));
          ret_values.emplace_back(str);
        }
      }
      return ret_values;
    });
    m.def("get_op_attrs_default_value",
          [](py::bytes byte_name) -> paddle::framework::AttributeMap {
            std::string op_type = byte_name;
            paddle::framework::AttributeMap res;
            auto info = OpInfoMap::Instance().GetNullable(op_type);
            if (info != nullptr) {
              if (info->HasOpProtoAndChecker()) {
                auto op_checker = info->Checker();
                res = op_checker->GetDefaultAttrsMap();
              }
            }
            return res;
          });
    m.def(
        "get_grad_op_desc", [](const OpDesc &op_desc,
                               const std::unordered_set<std::string> &no_grad_set,
                               const std::vector<BlockDesc *> &grad_sub_block) {
          std::unordered_map<std::string, std::string> grad_to_var;
          std::vector<std::unique_ptr<OpDesc>> grad_op_descs =
              framework::OpInfoMap::Instance()
                  .Get(op_desc.Type())
                  .GradOpMaker()(op_desc, no_grad_set, &grad_to_var,
                                 grad_sub_block);
          std::vector<OpDesc *> grad_op_desc_ptrs(grad_op_descs.size());
          std::transform(grad_op_descs.begin(), grad_op_descs.end(),
                         grad_op_desc_ptrs.begin(),
                         [](std::unique_ptr<OpDesc> &p) { return p.release(); });
          return std::make_pair(grad_op_desc_ptrs, grad_to_var);
        });
    m.def("has_grad_op_maker", [](const std::string op_type) {
      return framework::OpInfoMap::Instance().Get(op_type).HasGradOpMaker();
    });
    m.def("has_non_empty_grad_op_maker", [](const std::string op_type) {
      return framework::OpInfoMap::Instance()
          .Get(op_type)
          .HasNonEmptyGradOpMaker();
    });
    m.def("has_infer_inplace", [](const std::string op_type) {
      return framework::OpInfoMap::Instance().Get(op_type).HasInferInplace();
    });
    m.def("infer_no_need_buffer_slots",
          [](const std::string op_type, const framework::VariableNameMap &inputs,
             const framework::VariableNameMap &outputs,
             const framework::AttributeMap &attrs) {
            auto infer_func = framework::OpInfoMap::Instance()
                                  .Get(op_type)
                                  .NoNeedBufferVarsInferer();
            if (infer_func) {
              return infer_func(inputs, outputs, attrs);
            } else {
              std::unordered_set<std::string> empty = {};
              return empty;
            }
          });
    m.def("prune", [](const ProgramDesc &origin,
                      const std::set<std::string> &feeded_var_names,
                      const std::vector<std::array<size_t, 2>> &targets) {
      ProgramDesc prog_with_targets(origin);
  
      for (const auto &t : targets) {
        prog_with_targets.MutableBlock(t[0])->Op(t[1])->SetIsTarget(true);
      }
      proto::ProgramDesc pruned_desc;
      auto pruned_origin_block_id_map =
          Prune(*prog_with_targets.Proto(), feeded_var_names, &pruned_desc);
      return std::make_tuple(ProgramDesc(pruned_desc),
                             pruned_origin_block_id_map);
    });
    m.def("prune_backward",
          [](const framework::ProgramDesc &program) {
            return PruneBackward(program);
          },
          R"DOC(
               Prune the backward part of a program, mostly called in
               program.clone(for_test=True).
                
               Args:
                     program (ProgramDesc): The original program.
  
               Returns:
                     tuple(ProgramDesc, map<int, int>): The first part is 
                     the pruned program desc, and the second part is a map
                     which contains the id pair of pruned block and corresponding
                     origin block.
             )DOC");
    m.def("empty_var_name",
          []() { return std::string(framework::kEmptyVarName); });
    m.def("grad_var_suffix",
          []() { return std::string(framework::kGradVarSuffix); });
    m.def_submodule(
         "var_names",
         "The module will return special predefined variable name in Paddle")
        .def("empty", []() { return kEmptyVarName; })
        .def("temp", []() { return kTempVarName; });
  
    // clang-format off
    py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
        .def_static("create",
                    [](paddle::platform::CPUPlace& place)
                        -> paddle::platform::DeviceContext* {
      auto* context = new paddle::platform::CPUDeviceContext();
      context->SetAllocator(
        paddle::memory::allocation::AllocatorFacade::Instance()
          .GetAllocator(place)
          .get());
      context->SetHostAllocator(
        paddle::memory::allocation::AllocatorFacade::Instance()
          .GetAllocator(paddle::platform::CPUPlace())
          .get());
      context->SetZeroAllocator(
        paddle::memory::allocation::AllocatorFacade::Instance()
          .GetZeroAllocator(place)
          .get());
      return context;
                    })
        .def_static("create",
                    [](paddle::platform::XPUPlace& place)
                        -> paddle::platform::DeviceContext* {
  #ifndef PADDLE_WITH_XPU
               PADDLE_THROW(
                   platform::errors::PermissionDenied(
                   "Cannot use XPUPlace in CPU/GPU version, "
                   "Please recompile or reinstall Paddle with XPU support."));
  #else
        auto* context = new paddle::platform::XPUDeviceContext(place);
        context->SetAllocator(
          paddle::memory::allocation::AllocatorFacade::Instance()
            .GetAllocator(place)
            .get());
        context->SetHostAllocator(
          paddle::memory::allocation::AllocatorFacade::Instance()
            .GetAllocator(paddle::platform::CPUPlace())
            .get());
        context->SetZeroAllocator(
          paddle::memory::allocation::AllocatorFacade::Instance()
            .GetZeroAllocator(place)
            .get());
        return context;
  #endif
                    })
          .def_static("create",
                    [](paddle::platform::MLUPlace& place)
                        -> paddle::platform::DeviceContext* {
  #ifndef PADDLE_WITH_MLU
               PADDLE_THROW(
                   platform::errors::PermissionDenied(
                   "Cannot use MLUPlace in CPU/GPU version, "
                   "Please recompile or reinstall Paddle with MLU support."));
  #else
                      return new paddle::platform::MLUDeviceContext(place);
  #endif
                    })
          .def_static("create",
                      [](paddle::platform::NPUPlace& place)
                          -> paddle::platform::DeviceContext* {
  #ifndef PADDLE_WITH_ASCEND_CL
               PADDLE_THROW(
                   platform::errors::PermissionDenied(
                   "Cannot use NPUPlace in CPU/GPU/XPU version, "
                   "Please recompile or reinstall Paddle with NPU support."));
  #else
                  return new paddle::platform::NPUDeviceContext(place);
  #endif
          })
        .def_static("create",
                    [](paddle::platform::CUDAPlace& place)
                        -> paddle::platform::DeviceContext* {
  #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP)
               PADDLE_THROW(
                   platform::errors::PermissionDenied(
                   "Cannot use CUDAPlace in CPU only version, "
                   "Please recompile or reinstall Paddle with CUDA support."));
  #else
        auto* context = new paddle::platform::CUDADeviceContext(place);
        context->SetAllocator(
          paddle::memory::allocation::AllocatorFacade::Instance()
            .GetAllocator(place, context->stream())
            .get());
        context->SetHostAllocator(
          paddle::memory::allocation::AllocatorFacade::Instance()
            .GetAllocator(paddle::platform::CPUPlace())
            .get());
        context->SetZeroAllocator(
          paddle::memory::allocation::AllocatorFacade::Instance()
          .GetZeroAllocator(place)
          .get());
        context->PartialInitWithAllocator();
        return context;
  #endif
                    })
            .def_static("create",
                  [](paddle::platform::CUDAPinnedPlace& place)
                          -> paddle::platform::DeviceContext* {
  #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP)
               PADDLE_THROW(
                   platform::errors::PermissionDenied(
                   "Cannot use CUDAPinnedPlace in CPU only version, "
                   "Please recompile or reinstall Paddle with CUDA support."));
  #else
                    return new paddle::platform::CUDAPinnedDeviceContext(place);
  #endif
                  });;
  // clang-format on
  #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
    py::class_<platform::Communicator>(m, "Communicator").def(py::init<>());
  #endif
    m.def("get_all_device_type", []() {
      std::vector<std::string> device_types;
  #ifdef PADDLE_WITH_CUSTOM_DEVICE
      device_types = platform::DeviceManager::GetAllDeviceTypes();
  #else
            LOG(WARNING) << string::Sprintf(
                "Cannot use get_all_device_type because you have installed"
                "CPU/GPU version PaddlePaddle.\n"
                "If you want to use get_all_device_type, please try to install"
                "CustomDevice version "
                "PaddlePaddle by: pip install paddlepaddle-core\n");
  #endif
      return device_types;
    });
    m.def("get_all_custom_device_type", []() {
      std::vector<std::string> device_types;
  #ifdef PADDLE_WITH_CUSTOM_DEVICE
      device_types = platform::DeviceManager::GetAllCustomDeviceTypes();
  #else
            LOG(WARNING) << string::Sprintf(
                "Cannot use get_all_custom_device_type because you have installed"
                "CPU/GPU version PaddlePaddle.\n"
                "If you want to use get_all_custom_device_type, please try to "
                "install CustomDevice version "
                "PaddlePaddle by: pip install paddlepaddle-core\n");
  #endif
      return device_types;
    });
    m.def("get_available_device", [] {
      std::vector<std::string> devices;
  #ifdef PADDLE_WITH_CUSTOM_DEVICE
      devices = platform::DeviceManager::GetAllDeviceList();
  #else
            LOG(WARNING) << string::Sprintf(
                "Cannot use get_available_device because you have installed"
                "CPU/GPU version PaddlePaddle.\n"
                "If you want to use get_available_device, please try to install"
                "CustomDevice version "
                "PaddlePaddle by: pip install paddlepaddle-core\n");
  #endif
      return devices;
    });
    m.def("get_available_custom_device", [] {
      std::vector<std::string> devices;
  #ifdef PADDLE_WITH_CUSTOM_DEVICE
      devices = platform::DeviceManager::GetAllCustomDeviceList();
  #else
            LOG(WARNING) << string::Sprintf(
                "Cannot use get_available_custom_device because you have "
                "installed"
                "CPU/GPU version PaddlePaddle.\n"
                "If you want to use get_available_custom_device, please try to "
                "install"
                "CustomDevice version "
                "PaddlePaddle by: pip install paddlepaddle-core\n");
  #endif
      return devices;
    });
    py::class_<platform::CustomPlace>(m, "CustomPlace",
                                      R"DOC(
      CustomPlace is a descriptor of a device.
      It represents a custom device on which a tensor will be allocated and a model will run.
  
      Examples:
          .. code-block:: python
  
            import paddle
            fake_cpu_place = paddle.CustomPlace("FakeCPU", 0)
                                               )DOC")
        .def("__init__",
             [](platform::CustomPlace &self, const std::string &device_type,
                int dev_id) {
  #ifdef PADDLE_WITH_CUSTOM_DEVICE
               if (UNLIKELY(dev_id < 0)) {
                 LOG(ERROR) << string::Sprintf(
                     "Invalid CustomPlace(%s, %d), device id must be 0 "
                     "or "
                     "positive integer",
                     device_type, dev_id);
                 std::exit(-1);
               }
  
               if (LIKELY(platform::DeviceManager::HasDeviceType(device_type) &&
                          platform::DeviceManager::IsCustom(device_type))) {
                 int dev_count = static_cast<int>(
                     platform::DeviceManager::GetDeviceCount(device_type));
                 if (UNLIKELY(dev_id >= dev_count)) {
                   if (dev_count == 0) {
                     LOG(ERROR) << "Cannot use " << device_type
                                << " because there is no " << device_type
                                << " detected on your "
                                   "machine.";
                     std::exit(-1);
                   } else {
                     LOG(ERROR) << string::Sprintf(
                         "Invalid CustomPlace(%s, %d), dev_id must "
                         "inside "
                         "[0, %d), because %s "
                         "number on your machine is %d",
                         device_type, dev_id, dev_count, device_type, dev_count);
                     std::exit(-1);
                   }
                 }
                 new (&self) platform::CustomPlace(device_type, dev_id);
               } else {
                 LOG(ERROR) << string::Sprintf(
                     "Invalid CustomPlace(%s, %d), the device type is "
                     "not registered "
                     "as a custom device.",
                     device_type, dev_id);
                 std::exit(-1);
               }
  #else
               LOG(ERROR) << string::Sprintf(
                   "Cannot use CustomDevice because you have installed CPU/GPU"
                   "version PaddlePaddle.\n"
                   "If you want to use CustomDevice, please try to install"
                   "CustomDevice version "
                   "PaddlePaddle by: pip install paddlepaddle-core\n"
                   "If you only have CPU, please change "
                   "CustomPlace(%s, %d) to be CPUPlace().\n",
                   device_type, dev_id);
               std::exit(-1);
  #endif
             })
        .def("get_device_id",
             [](const platform::CustomPlace &self) { return self.GetDeviceId(); })
        .def("get_device_type",
             [](const platform::CustomPlace &self) {
               return self.GetDeviceType();
             })
        .def("__repr__", string::to_string<const platform::CustomPlace &>)
        .def("__str__", string::to_string<const platform::CustomPlace &>);
    py::class_<platform::CUDAPlace> cudaplace(m, "CUDAPlace", R"DOC(
  
      CUDAPlace is a descriptor of a device.
      It represents a GPU device allocated or to be allocated with Tensor or LoDTensor.
      Each CUDAPlace has a dev_id to indicate the graphics card ID represented by the current CUDAPlace,
      staring from 0.
      The memory of CUDAPlace with different dev_id is not accessible.
      Numbering here refers to the logical ID of the visible graphics card, not the actual ID of the graphics card.
      You can set visible GPU devices by setting the `CUDA_VISIBLE_DEVICES` environment variable.
      When the program starts, visible GPU devices will be numbered from 0.
      If `CUDA_VISIBLE_DEVICES` is not set, all devices are visible by default,
      and the logical ID is the same as the actual ID.
  
      Parameters:
          id (int): GPU device ID.
  
      Examples:
          .. code-block:: python
  
            import paddle
  
            place = paddle.CUDAPlace(0)
  
          )DOC");
    g_cudaplace_pytype = reinterpret_cast<PyTypeObject *>(cudaplace.ptr());
    cudaplace
        .def("__init__",
             [](platform::CUDAPlace &self, int dev_id) {
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
               if (UNLIKELY(dev_id < 0)) {
                 LOG(ERROR) << string::Sprintf(
                     "Invalid CUDAPlace(%d), device id must be 0 or "
                     "positive integer",
                     dev_id);
                 std::exit(-1);
               }
  
               if (UNLIKELY(dev_id >= platform::GetGPUDeviceCount())) {
                 if (platform::GetGPUDeviceCount() == 0) {
                   LOG(ERROR) << "Cannot use GPU because there is no GPU "
                                 "detected on your "
                                 "machine.";
                   std::exit(-1);
                 } else {
                   LOG(ERROR) << string::Sprintf(
                       "Invalid CUDAPlace(%d), must inside [0, %d), because GPU "
                       "number on your machine is %d",
                       dev_id, platform::GetGPUDeviceCount(),
                       platform::GetGPUDeviceCount());
                   std::exit(-1);
                 }
               }
  
               new (&self) platform::CUDAPlace(dev_id);
  #else
               LOG(ERROR) << string::Sprintf(
                   "Cannot use GPU because you have installed CPU version "
                   "PaddlePaddle.\n"
                   "If you want to use GPU, please try to install GPU version "
                   "PaddlePaddle by: pip install paddlepaddle-gpu\n"
                   "If you only have CPU, please change CUDAPlace(%d) to be "
                   "CPUPlace().\n",
                   dev_id);
               std::exit(-1);
  #endif
             })
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
        .def("get_device_id",
             [](const platform::CUDAPlace &self) { return self.GetDeviceId(); })
        .def("_type", &PlaceIndex<platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::CUDAPlace, platform::Place>)
        .def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::CUDAPlace, platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::CUDAPlace, platform::NPUPlace>)
        .def("_equals", &IsSamePlace<platform::CUDAPlace, platform::MLUPlace>)
        .def("_equals",
             &IsSamePlace<platform::CUDAPlace, platform::CUDAPinnedPlace>)
        .def("_get_device_id",
             [](platform::CUDAPlace &self) -> int { return self.GetDeviceId(); })
  #endif
        .def("__repr__", string::to_string<const platform::CUDAPlace &>)
        .def("__str__", string::to_string<const platform::CUDAPlace &>);
  
    py::class_<platform::XPUPlace> xpuplace(m, "XPUPlace", R"DOC(
      **Note**:
      Examples:
          .. code-block:: python
            import paddle.fluid as fluid
            xpu_place = fluid.XPUPlace(0)
          )DOC");
    g_xpuplace_pytype = reinterpret_cast<PyTypeObject *>(xpuplace.ptr());
    xpuplace
        .def("__init__",
             [](platform::XPUPlace &self, int dev_id) {
  #ifdef PADDLE_WITH_XPU
               if (UNLIKELY(dev_id < 0)) {
                 LOG(ERROR) << string::Sprintf(
                     "Invalid XPUPlace(%d), device id must be 0 or "
                     "positive integer",
                     dev_id);
                 std::exit(-1);
               }
               if (UNLIKELY(dev_id >= platform::GetXPUDeviceCount())) {
                 if (platform::GetXPUDeviceCount() == 0) {
                   LOG(ERROR) << "Cannot use XPU because there is no XPU "
                                 "detected on your "
                                 "machine.";
                   std::exit(-1);
                 } else {
                   LOG(ERROR) << string::Sprintf(
                       "Invalid XPUPlace(%d), must inside [0, %d), because XPU "
                       "number on your machine is %d",
                       dev_id, platform::GetXPUDeviceCount(),
                       platform::GetXPUDeviceCount());
                   std::exit(-1);
                 }
               }
               new (&self) platform::XPUPlace(dev_id);
  #else
               LOG(ERROR) << string::Sprintf(
                   "Cannot use XPU because you have installed CPU/GPU version "
                   "PaddlePaddle.\n"
                   "If you want to use XPU, please try to install XPU version "
                   "PaddlePaddle by: pip install paddlepaddle-xpu\n"
                   "If you only have CPU, please change XPUPlace(%d) to be "
                   "CPUPlace().\n",
                   dev_id);
               std::exit(-1);
  #endif
             })
  #ifdef PADDLE_WITH_XPU
        .def("_type", &PlaceIndex<platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::XPUPlace, platform::Place>)
        .def("_equals", &IsSamePlace<platform::XPUPlace, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::XPUPlace, platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::XPUPlace, platform::XPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::XPUPlace, platform::CUDAPinnedPlace>)
        .def("get_device_id",
             [](const platform::XPUPlace &self) { return self.GetDeviceId(); })
  #endif
        .def("__repr__", string::to_string<const platform::XPUPlace &>)
        .def("__str__", string::to_string<const platform::XPUPlace &>);
  #ifdef PADDLE_WITH_XPU
    py::enum_<phi::backends::xpu::XPUVersion>(m, "XPUVersion", py::arithmetic())
        .value("XPU1", phi::backends::xpu::XPUVersion::XPU1)
        .value("XPU2", phi::backends::xpu::XPUVersion::XPU2)
        .export_values();
    m.def("get_xpu_device_count", platform::GetXPUDeviceCount);
    m.def("get_xpu_device_version",
          [](int device_id) { return platform::get_xpu_version(device_id); });
    m.def("get_xpu_device_op_support_types",
          [](const std::string &op_name, phi::backends::xpu::XPUVersion version) {
            return platform::get_xpu_op_support_type(op_name, version);
          });
    m.def("get_xpu_device_op_list", [](phi::backends::xpu::XPUVersion version) {
      return platform::get_xpu_op_list(version);
    });
    m.def("is_float16_supported", [](const platform::XPUPlace &place) -> bool {
      // XPUs with Compute Capability > xpu2 support float16 and bfloat16
      return platform::get_xpu_version(place.device) >
             phi::backends::xpu::XPUVersion::XPU1;
    });
    m.def("is_bfloat16_supported", [](const platform::XPUPlace &place) -> bool {
      // XPUs with Compute Capability > xpu2 support float16 and bfloat16
      return platform::get_xpu_version(place.device) >
             phi::backends::xpu::XPUVersion::XPU1;
    });
  #endif
  
    py::class_<paddle::platform::CPUPlace> cpuplace(m, "CPUPlace", R"DOC(
      CPUPlace is a descriptor of a device.
      It represents a CPU device on which a tensor will be allocated and a model will run.
  
      Examples:
          .. code-block:: python
  
            import paddle
            cpu_place = paddle.CPUPlace()
  
          )DOC");
    g_cpuplace_pytype = reinterpret_cast<PyTypeObject *>(cpuplace.ptr());
    cpuplace.def(py::init<>())
        .def("_type", &PlaceIndex<platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::CPUPlace, platform::Place>)
        .def("_equals", &IsSamePlace<platform::CPUPlace, platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::CPUPlace, platform::NPUPlace>)
        .def("_equals", &IsSamePlace<platform::CPUPlace, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::CPUPlace, platform::CPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::CPUPlace, platform::CUDAPinnedPlace>)
        .def("__repr__", string::to_string<const platform::CPUPlace &>)
        .def("__str__", string::to_string<const platform::CPUPlace &>);
  
    py::class_<paddle::platform::CUDAPinnedPlace> cudapinnedplace(
        m, "CUDAPinnedPlace", R"DOC(
      CUDAPinnedPlace is a descriptor of a device.
      It refers to the page locked memory allocated by the CUDA function `cudaHostAlloc()` in the host memory.
      The host operating system will not paging and exchanging the memory.
      It can be accessed through direct memory access technology to speed up the copy of data between the host and GPU.
      For more information on CUDA data transfer and `pinned memory`,
      please refer to `official document <https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#pinned-memory>`_ .
  
      Examples:
          .. code-block:: python
  
            import paddle
            place = paddle.CUDAPinnedPlace()
  
          )DOC");
    g_cudapinnedplace_pytype =
        reinterpret_cast<PyTypeObject *>(cudapinnedplace.ptr());
    cudapinnedplace
        .def("__init__",
             [](platform::CUDAPinnedPlace &self) {
  #if !defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_HIP)
               PADDLE_THROW(platform::errors::PermissionDenied(
                   "Cannot use CUDAPinnedPlace in CPU only version, "
                   "Please recompile or reinstall Paddle with CUDA support."));
  #endif
               new (&self) platform::CUDAPinnedPlace();
             })
        .def("_type", &PlaceIndex<platform::CUDAPinnedPlace>)
        .def("_equals", &IsSamePlace<platform::CUDAPinnedPlace, platform::Place>)
        .def("_equals",
             &IsSamePlace<platform::CUDAPinnedPlace, platform::CUDAPlace>)
        .def("_equals",
             &IsSamePlace<platform::CUDAPinnedPlace, platform::XPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::CUDAPinnedPlace, platform::NPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::CUDAPinnedPlace, platform::CPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::CUDAPinnedPlace, platform::CUDAPinnedPlace>)
        .def("__repr__", string::to_string<const platform::CUDAPinnedPlace &>)
        .def("__str__", string::to_string<const platform::CUDAPinnedPlace &>);
  
    // NPUPlace
    py::class_<platform::NPUPlace> npuplace(m, "NPUPlace", R"DOC(
      NPUPlace is a descriptor of a device.
      It represents a NPU device on which a tensor will be allocated and a model will run.
  
      Examples:
          .. code-block:: python
            import paddle
            npu_place = paddle.NPUPlace(0)
  
          )DOC");
    g_npuplace_pytype = reinterpret_cast<PyTypeObject *>(npuplace.ptr());
    npuplace
        .def("__init__",
             [](platform::NPUPlace &self, int dev_id) {
  #ifdef PADDLE_WITH_ASCEND_CL
               if (UNLIKELY(dev_id < 0)) {
                 LOG(ERROR) << string::Sprintf(
                     "Invalid NPUPlace(%d), device id must be 0 or "
                     "positive integer",
                     dev_id);
                 std::exit(-1);
               }
               if (UNLIKELY(dev_id >= platform::GetNPUDeviceCount())) {
                 if (platform::GetNPUDeviceCount() == 0) {
                   LOG(ERROR) << "Cannot use NPU because there is no NPU "
                                 "detected on your "
                                 "machine.";
                   std::exit(-1);
                 } else {
                   LOG(ERROR) << string::Sprintf(
                       "Invalid NPUPlace(%d), must inside [0, %d), because NPU "
                       "number on your machine is %d",
                       dev_id, platform::GetNPUDeviceCount(),
                       platform::GetNPUDeviceCount());
                   std::exit(-1);
                 }
               }
               new (&self) platform::NPUPlace(dev_id);
  #else
               LOG(ERROR) << string::Sprintf(
                   "Cannot use NPU because you have installed CPU/GPU version "
                   "PaddlePaddle.\n"
                   "If you want to use NPU, please try to install NPU version "
                   "PaddlePaddle by: pip install paddlepaddle-npu\n"
                   "If you only have CPU, please change NPUPlace(%d) to be "
                   "CPUPlace().\n",
                   dev_id);
               std::exit(-1);
  #endif
             })
        .def("_type", &PlaceIndex<platform::NPUPlace>)
        .def("_equals", &IsSamePlace<platform::NPUPlace, platform::Place>)
        .def("_equals", &IsSamePlace<platform::NPUPlace, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::NPUPlace, platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::NPUPlace, platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::NPUPlace, platform::NPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::NPUPlace, platform::CUDAPinnedPlace>)
        .def("get_device_id",
             [](const platform::NPUPlace &self) { return self.GetDeviceId(); })
        .def("__str__", string::to_string<const platform::NPUPlace &>);
  
    // IPUPlace
    py::class_<platform::IPUPlace>(m, "IPUPlace", R"DOC(
      IPUPlace is a descriptor of a device.
      It represents a IPU device on which a tensor will be allocated and a model will run.
  
      Examples:
          .. code-block:: python
            import paddle
  
            # required: ipu
  
            ipu_place = paddle.IPUPlace()
  
          )DOC")
        .def("__init__",
             [](platform::IPUPlace &self) {
  #ifdef PADDLE_WITH_IPU
               if (platform::GetIPUDeviceCount() == 0) {
                 LOG(ERROR) << "Cannot use IPU because there is no IPU "
                               "detected on your "
                               "machine.";
                 std::exit(-1);
               }
               // use ipu(0) to comile, while run with the number user configure
               // in sharding and pipline.
               new (&self) platform::IPUPlace(0);
  #else
               LOG(ERROR) << string::Sprintf(
                   "Cannot use IPU because you didn't install IPU version "
                   "PaddlePaddle.\n"
                   "If you want to use IPU, please try to install IPU version "
                   "PaddlePaddle by: pip install paddlepaddle*\n"
                   "If you only have CPU, please change IPUPlace to be "
                   "CPUPlace().\n");
               std::exit(-1);
  #endif
             })
        .def("_type", &PlaceIndex<platform::IPUPlace>)
        .def("_equals", &IsSamePlace<platform::IPUPlace, platform::Place>)
        .def("_equals", &IsSamePlace<platform::IPUPlace, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::IPUPlace, platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::IPUPlace, platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::IPUPlace, platform::NPUPlace>)
        .def("_equals", &IsSamePlace<platform::IPUPlace, platform::IPUPlace>)
        .def("_equals",
             &IsSamePlace<platform::IPUPlace, platform::CUDAPinnedPlace>)
  #ifdef PADDLE_WITH_IPU
        .def("get_device_id",
             [](const platform::IPUPlace &self) { return self.GetDeviceId(); })
  #endif
        .def("__str__", string::to_string<const platform::IPUPlace &>);
  
    // MLUPlace
    py::class_<platform::MLUPlace> mluplace(m, "MLUPlace", R"DOC(
      MLUPlace is a descriptor of a device.
      It represents a MLU device on which a tensor will be allocated and a model will run.
  
      Examples:
          .. code-block:: python
            import paddle
            # required: mlu
            mlu_place = paddle.MLUPlace(0)
  
          )DOC");
    g_mluplace_pytype = reinterpret_cast<PyTypeObject *>(mluplace.ptr());
    mluplace
        .def("__init__",
             [](platform::MLUPlace &self, int dev_id) {
  #ifdef PADDLE_WITH_MLU
               if (UNLIKELY(dev_id < 0)) {
                 LOG(ERROR) << string::Sprintf(
                     "Invalid MLUPlace(%d), device id must be 0 or "
                     "positive integer",
                     dev_id);
                 std::exit(-1);
               }
               if (UNLIKELY(dev_id >= platform::GetMLUDeviceCount())) {
                 if (platform::GetMLUDeviceCount() == 0) {
                   LOG(ERROR) << "Cannot use MLU because there is no MLU "
                                 "detected on your "
                                 "machine.";
                   std::exit(-1);
                 } else {
                   LOG(ERROR) << string::Sprintf(
                       "Invalid MLUPlace(%d), must inside [0, %d), because MLU "
                       "number on your machine is %d",
                       dev_id, platform::GetMLUDeviceCount(),
                       platform::GetMLUDeviceCount());
                   std::exit(-1);
                 }
               }
               new (&self) platform::MLUPlace(dev_id);
  #else
               LOG(ERROR) << string::Sprintf(
                   "Cannot use MLU because you have installed CPU/GPU/... "
                   "version "
                   "PaddlePaddle.\n"
                   "If you want to use MLU, please try to install MLU version "
                   "PaddlePaddle by: pip install paddlepaddle-mlu\n"
                   "If you only have CPU, please change MLUPlace(%d) to be "
                   "CPUPlace().\n",
                   dev_id);
               std::exit(-1);
  #endif
             })
        .def("_type", &PlaceIndex<platform::MLUPlace>)
  #ifdef PADDLE_WITH_MLU
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::Place>)
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::NPUPlace>)
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::IPUPlace>)
        .def("_equals", &IsSamePlace<platform::MLUPlace, platform::MLUPlace>)
        .def("_equals",
             &IsSamePlace<platform::MLUPlace, platform::CUDAPinnedPlace>)
        .def("get_device_id",
             [](const platform::MLUPlace &self) { return self.GetDeviceId(); })
  #endif
        .def("__str__", string::to_string<const platform::MLUPlace &>);
  
    py::class_<platform::Place> platformplace(m, "Place");
    g_place_pytype = reinterpret_cast<PyTypeObject *>(platformplace.ptr());
    platformplace.def(py::init<>())
        .def("_type", &PlaceIndex<platform::Place>)
        .def("_equals", &IsSamePlace<platform::Place, platform::Place>)
        .def("_equals", &IsSamePlace<platform::Place, platform::CUDAPlace>)
        .def("_equals", &IsSamePlace<platform::Place, platform::CPUPlace>)
        .def("_equals", &IsSamePlace<platform::Place, platform::XPUPlace>)
        .def("_equals", &IsSamePlace<platform::Place, platform::NPUPlace>)
        .def("_equals", &IsSamePlace<platform::Place, platform::IPUPlace>)
        .def("_equals", &IsSamePlace<platform::Place, platform::CUDAPinnedPlace>)
        .def("_equals", &IsSamePlace<platform::Place, platform::MLUPlace>)
        .def("is_gpu_place",
             [](platform::Place &self) { return platform::is_gpu_place(self); })
        .def("is_cpu_place",
             [](platform::Place &self) { return platform::is_cpu_place(self); })
        .def("is_xpu_place",
             [](platform::Place &self) { return platform::is_xpu_place(self); })
        .def("is_npu_place",
             [](platform::Place &self) { return platform::is_npu_place(self); })
        .def("is_ipu_place",
             [](platform::Place &self) { return platform::is_ipu_place(self); })
        .def("is_cuda_pinned_place",
             [](platform::Place &self) {
               return platform::is_cuda_pinned_place(self);
             })
        .def("is_mlu_place",
             [](platform::Place &self) { return platform::is_mlu_place(self); })
        .def(
            "is_custom_place",
            [](platform::Place &self) { return platform::is_custom_place(self); })
        .def("gpu_device_id", [](platform::Place &self) { return self.device; })
        .def("xpu_device_id", [](platform::Place &self) { return self.device; })
        .def("npu_device_id", [](platform::Place &self) { return self.device; })
        .def("ipu_device_id", [](platform::Place &self) { return self.device; })
        .def("mlu_device_id", [](platform::Place &self) { return self.device; })
        .def("custom_device_id",
             [](platform::Place &self) { return self.device; })
        .def("set_place", [](platform::Place &self,
                             const platform::Place &other) { self = other; })
        .def("set_place",
             [](platform::Place &self, const platform::CPUPlace &cpu_place) {
               self = cpu_place;
             })
        .def("set_place",
             [](platform::Place &self, const platform::XPUPlace &xpu_place) {
               self = xpu_place;
             })
        .def("set_place",
             [](platform::Place &self, const platform::CUDAPlace &gpu_place) {
               self = gpu_place;
             })
        .def("set_place",
             [](platform::Place &self,
                const platform::CUDAPinnedPlace &cuda_pinned_place) {
               self = cuda_pinned_place;
             })
        .def("set_place",
             [](platform::Place &self, const platform::NPUPlace &npu_place) {
               self = npu_place;
             })
        .def("set_place",
             [](platform::Place &self, const platform::IPUPlace &ipu_place) {
               self = ipu_place;
             })
        .def("set_place",
             [](platform::Place &self, const platform::MLUPlace &mlu_place) {
               self = mlu_place;
             })
        .def("set_place",
             [](platform::Place &self, const platform::CustomPlace &plug_place) {
               self = plug_place;
             })
        .def("__repr__", string::to_string<const platform::Place &>)
        .def("__str__", string::to_string<const platform::Place &>);
  
    py::class_<OperatorBase>(m, "Operator")
        .def_static("create",
                    [](py::bytes protobin) {
                      proto::OpDesc desc;
                      PADDLE_ENFORCE_EQ(desc.ParsePartialFromString(protobin),
                                        true,
                                        platform::errors::InvalidArgument(
                                            "Cannot parse user input to OpDesc"));
                      PADDLE_ENFORCE_EQ(desc.IsInitialized(), true,
                                        platform::errors::InvalidArgument(
                                            "The provided OpDesc is not "
                                            "initialized, the reason is: %s",
                                            desc.InitializationErrorString()));
                      return OpRegistry::CreateOp(desc);
                    })
        .def("run",
             [](OperatorBase &self, const Scope &scope,
                const platform::CPUPlace &place) {
               pybind11::gil_scoped_release release;
               self.Run(scope, place);
             })
        .def("run",
             [](OperatorBase &self, const Scope &scope,
                const platform::XPUPlace &place) {
               pybind11::gil_scoped_release release;
               self.Run(scope, place);
             })
        .def("run",
             [](OperatorBase &self, const Scope &scope,
                const platform::NPUPlace &place) {
               pybind11::gil_scoped_release release;
               self.Run(scope, place);
             })
        .def("run",
             [](OperatorBase &self, const Scope &scope,
                const platform::CUDAPlace &place) {
               pybind11::gil_scoped_release release;
               self.Run(scope, place);
             })
        .def("run",
             [](OperatorBase &self, const Scope &scope,
                const platform::CUDAPinnedPlace &place) {
               pybind11::gil_scoped_release release;
               self.Run(scope, place);
             })
        .def("run",
             [](OperatorBase &self, const Scope &scope,
                const platform::MLUPlace &place) {
               pybind11::gil_scoped_release release;
               self.Run(scope, place);
             })
        .def("type",
             [](const OperatorBase &op) -> std::string { return op.Type(); })
        .def("outputs",
             [](const OperatorBase &op)
                 -> std::map<std::string, std::vector<std::string>> {
                   return op.Outputs();
                 })
        .def("output_vars",
             [](const OperatorBase &op) { return op.OutputVars(true); })
        .def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
        .def("input_vars", [](const OperatorBase &op) { return op.InputVars(); })
        .def("__str__", &OperatorBase::DebugString)
        .def("no_intermediate_outputs",
             [](const OperatorBase &op) { return op.OutputVars(false); })
        .def("support_gpu", &OperatorBase::SupportGPU);
  
    py::class_<framework::ExecutorPrepareContext>(m, "ExecutorPrepareContext")
        .def(py::init<const ProgramDesc &, size_t>());
  
    py::class_<framework::TrainerBase, std::shared_ptr<framework::TrainerBase>>(
        m, "TrainerBase")
        .def("get_worker_scope",
             [](TrainerBase &self, int thread_id) -> Scope * {
               return self.GetWorkerScope(thread_id);
             },
             py::return_value_policy::reference)
        .def("finalize", &TrainerBase::Finalize)
        .def("ResetDataset", &TrainerBase::ResetDataset);
  
    m.def("_get_eager_deletion_vars", &framework::GetEagerDeletionCleanVars);
  
    py::class_<framework::Executor>(m, "Executor")
        .def(py::init<const platform::Place &>())
        .def("close", &Executor::Close)
        .def("run_from_dataset", &Executor::RunFromDataset,
             py::call_guard<py::gil_scoped_release>())
        .def("release_trainer", &Executor::ReleaseTrainer,
             py::call_guard<py::gil_scoped_release>())
        .def("init_for_dataset",
             [](Executor &self, const ProgramDesc &prog,
                const std::string &trainer_desc, Scope *scope,
                Dataset *dataset) -> std::shared_ptr<TrainerBase> {
               pybind11::gil_scoped_release release;
               return self.InitForDataset(prog, trainer_desc, scope, dataset);
             })
        .def("run_from_dataset",
             [](Executor &self, std::shared_ptr<TrainerBase> trainer) {
               pybind11::gil_scoped_release release;
               self.RunFromDataset(trainer);
             })
        .def("run_prepared_ctx",
             [](Executor &self, ExecutorPrepareContext *ctx, Scope *scope,
                std::map<std::string, const LoDTensor *> *feed_targets,
                std::map<std::string, FetchType *> *fetch_targets,
                bool create_local_scope = true, bool create_vars = true,
                const std::string &feed_holder_name = "feed",
                const std::string &fetch_holder_name = "fetch") {
               pybind11::gil_scoped_release release;
               self.RunPreparedContext(ctx, scope, feed_targets, fetch_targets,
                                       create_local_scope, create_vars,
                                       feed_holder_name, fetch_holder_name);
             })
        .def("run_prepared_ctx",
             [](Executor &self, ExecutorPrepareContext *ctx, Scope *scope,
                bool create_local_scope = true, bool create_vars = true,
                bool keep_kids = false) {
               pybind11::gil_scoped_release release;
               self.RunPreparedContext(ctx, scope, create_local_scope,
                                       create_vars, keep_kids);
             })
        .def("prepare",
             [](Executor &self, const ProgramDesc &program, int block_id,
                const std::vector<std::string> &skip_ref_cnt_vars =
                    std::vector<std::string>(),
                bool force_disable_gc = false) {
               pybind11::gil_scoped_release release;
               return self.Prepare(program, block_id, skip_ref_cnt_vars,
                                   force_disable_gc);
             })
        .def("create_variables", &Executor::CreateVariables)
        .def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope,
                       int block_id, bool create_local_scope, bool create_vars,
                       const std::vector<std::string> &fetch_vars) {
          pybind11::gil_scoped_release release;
          self.Run(prog, scope, block_id, create_local_scope, create_vars,
                   fetch_vars);
        });
  
    py::class_<framework::interpreter::CostInfo>(m, "CostInfo")
        .def(py::init<>())
        .def("total_time",
             [](interpreter::CostInfo &self) { return self.total_time; })
        .def("device_memory_bytes", [](interpreter::CostInfo &self) {
          return self.device_memory_bytes;
        });
  
    py::class_<framework::StandaloneExecutor>(m, "StandaloneExecutor")
        .def(py::init<const platform::Place &, const ProgramDesc &,
                      const ProgramDesc &, Scope *>())
        .def("run",
             [](StandaloneExecutor &self,
                const std::unordered_map<std::string, py::array> &input_dict,
                std::vector<std::string> fetch_names) {
               std::vector<framework::LoDTensor> feed_tensors;
               std::vector<std::string> feed_names;
  
               for (auto &item : input_dict) {
                 framework::LoDTensor t;
                 SetTensorFromPyArray<platform::CPUPlace>(
                     &t, item.second, platform::CPUPlace(), false);
                 feed_names.push_back(item.first);
                 feed_tensors.push_back(t);
               }
  
               paddle::framework::FetchList ret;
               {
                 pybind11::gil_scoped_release release;
                 ret = self.Run(feed_names, feed_tensors, fetch_names);
               }
               return py::cast(std::move(ret));
             })
        .def("run",
             [](StandaloneExecutor &self,
                const std::unordered_map<std::string, framework::LoDTensor>
                    &input_dict,
                std::vector<std::string> fetch_names) {
               std::vector<framework::LoDTensor> feed_tensors;
               std::vector<std::string> feed_names;
  
               for (auto &item : input_dict) {
                 feed_names.push_back(item.first);
                 feed_tensors.push_back(item.second);
               }
  
               paddle::framework::FetchList ret;
               {
                 pybind11::gil_scoped_release release;
                 ret = self.Run(feed_names, feed_tensors, fetch_names);
               }
               return py::cast(std::move(ret));
             })
        .def("run",
             [](StandaloneExecutor &self, std::vector<std::string> feed_names,
                std::vector<std::string> fetch_names) {
               paddle::framework::FetchList ret;
               {
                 pybind11::gil_scoped_release release;
                 ret = self.Run(feed_names, fetch_names);
               }
               return py::cast(std::move(ret));
             })
        .def("dry_run",
             [](StandaloneExecutor &self,
                const std::unordered_map<std::string, py::array> &input_dict) {
               std::vector<framework::LoDTensor> feed_tensors;
               std::vector<std::string> feed_names;
  
               for (auto &item : input_dict) {
                 framework::LoDTensor t;
                 SetTensorFromPyArray<platform::CPUPlace>(
                     &t, item.second, platform::CPUPlace(), false);
                 feed_names.push_back(item.first);
                 feed_tensors.push_back(t);
               }
  
               framework::interpreter::CostInfo cost_info;
               {
                 pybind11::gil_scoped_release release;
                 cost_info = self.DryRun(feed_names, feed_tensors);
               }
               return cost_info;
             });
  
    m.def("init_gflags", framework::InitGflags);
    m.def("init_glog", framework::InitGLOG);
    m.def("load_op_meta_info_and_register_op",
          framework::LoadOpMetaInfoAndRegisterOp);
    m.def("init_devices", []() { framework::InitDevices(); });
  
    m.def("is_compiled_with_cuda", IsCompiledWithCUDA);
    m.def("is_compiled_with_ascend", IsCompiledWithAscend);
    m.def("is_compiled_with_rocm", IsCompiledWithROCM);
    m.def("is_compiled_with_npu", IsCompiledWithNPU);
    m.def("is_compiled_with_ipu", IsCompiledWithIPU);
    m.def("is_compiled_with_xpu", IsCompiledWithXPU);
    m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN);
    m.def("is_compiled_with_nccl", IsCompiledWithNCCL);
    m.def("is_compiled_with_cinn", IsCompiledWithCINN);
    m.def("is_compiled_with_mlu", IsCompiledWithMLU);
    m.def("_is_compiled_with_heterps", IsCompiledWithHETERPS);
    m.def("supports_bfloat16", SupportsBfloat16);
    m.def("supports_bfloat16_fast_performance", SupportsBfloat16FastPerformance);
    m.def("supports_int8", SupportsInt8);
    m.def("supports_vnni", SupportsVNNI);
    m.def("op_supported_infos", imperative::OpSupportedInfos);
    m.def("is_compiled_with_brpc", IsCompiledWithBrpc);
    m.def("is_compiled_with_dist", IsCompiledWithDIST);
    m.def("_cuda_synchronize", [](const platform::CUDAPlace &place) {
      platform::DeviceContextPool::Instance().Get(place)->Wait();
    });
  
    m.def("get_float_stats", []() {
      std::vector<paddle::platform::ExportedStatValue<float>> float_stats;
      paddle::platform::StatRegistry<float>::Instance().publish(float_stats);
      std::unordered_map<std::string, float> stats_map;
      for (const auto &stat : float_stats) {
        stats_map[stat.key] = stat.value;
      }
      return stats_map;
    });
    m.def("get_int_stats", []() {
      std::vector<paddle::platform::ExportedStatValue<int64_t>> int_stats;
      paddle::platform::StatRegistry<int64_t>::Instance().publish(int_stats);
      std::unordered_map<std::string, int64_t> stats_map;
      for (const auto &stat : int_stats) {
        stats_map[stat.key] = stat.value;
      }
      return stats_map;
    });
    m.def("run_cmd",
          [](const std::string &cmd, int time_out = -1,
             int sleep_inter = -1) -> const std::string {
            return paddle::framework::shell_get_command_output(cmd, time_out,
                                                               sleep_inter);
          },
          py::arg("cmd"), py::arg("time_out") = -1, py::arg("sleep_inter") = -1);
    m.def("shell_execute_cmd",
          [](const std::string &cmd, int time_out = 0, int sleep_inter = 0,
             bool redirect_stderr = false) -> std::vector<std::string> {
            return paddle::framework::shell_execute_cmd(
                cmd, time_out, sleep_inter, redirect_stderr);
          },
          py::arg("cmd"), py::arg("time_out") = 0, py::arg("sleep_inter") = 0,
          py::arg("redirect_stderr") = false);
  
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    m.def("is_float16_supported", [](const platform::CUDAPlace &place) -> bool {
      // Only GPUs with Compute Capability >= 53 support float16
      return platform::GetGPUComputeCapability(place.device) >= 53;
    });
  #endif
  
    m.def("set_feed_variable",
          static_cast<void (*)(Scope *, const LoDTensor &, const std::string &,
                               size_t)>(&framework::SetFeedVariable));
    m.def("set_feed_variable",
          static_cast<void (*)(Scope *, const Strings &, const std::string &,
                               size_t)>(&framework::SetFeedVariable));
    m.def("get_fetch_variable",
          [](const Scope &scope, const std::string &var_name,
             size_t index) -> py::object {
            auto &var = framework::GetFetchVariable(scope, var_name, index);
            if (data_is_lod_tensor(var)) {
              return py::cast(BOOST_GET(LoDTensor, var));
            } else {
              return py::cast(BOOST_GET(LoDTensorArray, var));
            }
          });
    m.def("get_variable_tensor", framework::GetVariableTensor);
  
    m.def("_is_program_version_supported", IsProgramVersionSupported);
  
    BindProgramDesc(&m);
    BindBlockDesc(&m);
    BindVarDsec(&m);
    BindOpDesc(&m);
    BindCostModel(&m);
    BindConstValue(&m);
    BindGlobalValueGetterSetter(&m);
    BindProcessMeshDesc(&m);
    BindFleetExecutor(&m);
    BindTCPStore(&m);
  
    py::class_<framework::LoDRankTable>(m, "LodRankTable")
        .def("items", [](framework::LoDRankTable &table) {
          std::vector<std::pair<size_t, size_t>> res;
          for (auto &item : table.items()) {
            res.push_back({item.index, item.length});
          }
          return res;
        });
  
    py::class_<LoDTensorArray> pylodtensorarray(m, "LoDTensorArray", R"DOC(
      LoDTensorArray is array of LoDTensor, it supports operator[], len() and for-loop iteration.
  
      Examples:
          .. code-block:: python
  
            import paddle.fluid as fluid
  
            arr = fluid.LoDTensorArray()
  )DOC");
    g_framework_lodtensorarray_pytype =
        reinterpret_cast<PyTypeObject *>(pylodtensorarray.ptr());
    pylodtensorarray
        .def("__init__",
             [](LoDTensorArray &instance) { new (&instance) LoDTensorArray(); })
        .def("__getitem__",
             [](LoDTensorArray &self, size_t i) { return &self.at(i); },
             py::return_value_policy::reference)
        .def("__len__", [](LoDTensorArray &self) { return self.size(); })
        .def("__setitem__",
             [](LoDTensorArray &self, size_t i, const LoDTensor &t) {
               PADDLE_ENFORCE_LT(i, self.size(),
                                 platform::errors::InvalidArgument(
                                     "The index to set is larger than the size "
                                     "of LoDTensorArray."));
               self[i].ShareDataWith(t);
               self[i].set_lod(t.lod());
             })
        .def("append",
             [](LoDTensorArray &self, const LoDTensor &t) {
               self.emplace_back();
               self.back().ShareDataWith(t);
               self.back().set_lod(t.lod());
             },
             py::arg("tensor"), R"DOC(
               Append a LoDensor to LoDTensorArray.
                
               Args:
                     tensor (LoDTensor): The LoDTensor to be appended.
  
               Returns:
                     None.
  
               Examples:
                   .. code-block:: python
  
                     import paddle.fluid as fluid
                     import numpy as np
  
                     arr = fluid.LoDTensorArray()
                     t = fluid.LoDTensor()
                     t.set(np.ndarray([5, 30]), fluid.CPUPlace())
                     arr.append(t)
             )DOC")
        .def("_move_to_list",
             [](LoDTensorArray &self) -> py::list {
               py::list res(self.size());
               for (size_t i = 0; i < self.size(); ++i) {
                 res[i] = py::cast(std::move(self[i]));
               }
               self.clear();
               return res;
             },
             py::return_value_policy::take_ownership);
  
    py::class_<FetchList>(m, "FetchList", R"DOC( FetchList is a
          vector of boost::variant<LoDTensor, LoDTensorArray>.
          )DOC")
        .def("_move_to_list",
             [](FetchList &self) -> py::list {
               py::list res(self.size());
               for (size_t i = 0; i < self.size(); ++i) {
                 if (data_is_lod_tensor(self[i])) {
                   auto &data = BOOST_GET(LoDTensor, self[i]);
                   res[i] = py::cast(std::move(data));
                 } else {
                   auto &data = BOOST_GET(LoDTensorArray, self[i]);
                   py::list tmp(data.size());
                   for (size_t j = 0; j < data.size(); ++j) {
                     tmp[j] = py::cast(std::move(data[j]));
                   }
                   res[i] = std::move(tmp);
                 }
               }
               self.clear();
               return res;
             },
             py::return_value_policy::take_ownership)
  
        .def("append",
             [](FetchList &self, const LoDTensor &t) {
               self.emplace_back();
               auto &lod_tensor = BOOST_GET(LoDTensor, self.back());
               lod_tensor.ShareDataWith(t);
               lod_tensor.set_lod(t.lod());
             },
             py::arg("var"))
  
        .def("append",
             [](FetchList &self, const LoDTensorArray &t) {
               self.emplace_back();
               auto &lod_tensor_array = BOOST_GET(LoDTensorArray, self.back());
               for (size_t i = 0; i < t.size(); ++i) {
                 lod_tensor_array[i].ShareDataWith(t[i]);
                 lod_tensor_array[i].set_lod(t[i].lod());
               }
             },
             py::arg("var"));
  
    py::class_<FetchUnmergedList>(m, "FetchUnmergedList", R"DOC(
          FetchUnmergedList is 2-D array of FetchType(boost::variant(LoDTensor, LoDTensorArray)).
          )DOC")
        .def("_move_to_list",
             [](FetchUnmergedList &self) -> py::list {
               py::list res(self.size());
               for (size_t i = 0; i < self.size(); ++i) {
                 py::list tmp(self[i].size());
                 for (size_t j = 0; j < self[i].size(); ++j) {
                   if (data_is_lod_tensor(self[i][j])) {
                     auto &var = BOOST_GET(LoDTensor, self[i][j]);
                     tmp[j] = py::cast(std::move(var));
                   } else {
                     auto &var = BOOST_GET(LoDTensorArray, self[i][j]);
                     py::list tmp_array(var.size());
                     for (size_t k = 0; k < var.size(); ++k) {
                       tmp_array[k] = std::move(var[k]);
                     }
                     tmp[j] = std::move(tmp_array);
                   }
                 }
                 res[i] = std::move(tmp);
                 self[i].clear();
               }
               self.clear();
               return res;
             },
             py::return_value_policy::take_ownership);
  
    m.def("op_support_gpu", OpSupportGPU);
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    m.def("get_cuda_device_count", platform::GetGPUDeviceCount);
    m.def("cuda_empty_cache", [] {
      for (int dev_id : platform::GetSelectedDevices()) {
        auto *dev_ctx = platform::DeviceContextPool::Instance().GetByPlace(
            platform::CUDAPlace(dev_id));
        dev_ctx->cudnn_workspace_handle().ResetWorkspace();
      }
      platform::EmptyCache();
    });
    m.def("get_device_properties",
          [](int id) -> const gpuDeviceProp & {
            return platform::GetDeviceProperties(id);
          },
          py::return_value_policy::copy);
  
    py::class_<gpuDeviceProp>(m, "_gpuDeviceProperties")
        .def_property_readonly(
            "name", [](const gpuDeviceProp &prop) { return prop.name; })
        .def_property_readonly(
            "major", [](const gpuDeviceProp &prop) { return prop.major; })
        .def_property_readonly(
            "minor", [](const gpuDeviceProp &prop) { return prop.minor; })
        .def_property_readonly(
            "total_memory",
            [](const gpuDeviceProp &prop) { return prop.totalGlobalMem; })
        .def_property_readonly(
            "multi_processor_count",
            [](const gpuDeviceProp &prop) { return prop.multiProcessorCount; })
        .def_property_readonly(
            "is_multi_gpu_board",
            [](const gpuDeviceProp &prop) { return prop.isMultiGpuBoard; })
        .def_property_readonly(
            "is_integrated",
            [](const gpuDeviceProp &prop) { return prop.integrated; })
        .def("__repr__", [](const gpuDeviceProp &prop) {
          std::stringstream ostr;
          ostr << "_gpuDeviceProperties(name='" << prop.name
               << "', major=" << prop.major << ", minor=" << prop.minor
               << ", total_memory=" << prop.totalGlobalMem / (1024 * 1024)
               << "MB, multi_processor_count=" << prop.multiProcessorCount << ")";
          return ostr.str();
        });
  
  #if !defined(PADDLE_WITH_HIP) && !defined(_WIN32)
    m.def("nvprof_init", platform::CudaProfilerInit);
    m.def("nvprof_start", platform::CudaProfilerStart);
    m.def("nvprof_stop", platform::CudaProfilerStop);
    m.def("nvprof_nvtx_push", platform::CudaNvtxRangePush);
    m.def("nvprof_nvtx_pop", platform::CudaNvtxRangePop);
    m.def("nvprof_enable_record_event", platform::NvprofEnableRecordEvent);
    m.def("nvprof_disable_record_event", platform::NvprofDisableRecordEvent);
  #endif
  #endif
  
  #ifdef PADDLE_WITH_ASCEND_CL
    m.def("get_npu_device_count", platform::GetNPUDeviceCount);
    m.def("npu_finalize", []() {
      platform::HCCLCommContext::Instance().ReleaseHCCLComms();
  
      auto &pool = platform::DeviceContextPool::Instance();
      auto devices = platform::GetSelectedNPUDevices();
      for (size_t i = 0; i < devices.size(); ++i) {
        platform::NPUDeviceGuard guard(devices[i]);
        pool.Get(platform::NPUPlace(devices[i]))->Wait();
      }
      platform::AclInstance::Instance().Finalize();
    });
  
    py::class_<platform::NPUProfConfigWrapper>(m, "NPUProfConfigWrapper");
  
    m.def("npu_prof_init", platform::NPUProfilerInit);
    m.def("npu_prof_start", [](platform::NPUProfConfigWrapper c) {
      platform::NPUProfilerStart(c.ptr());
    });
    m.def("npu_prof_stop", [](platform::NPUProfConfigWrapper c) {
      platform::NPUProfilerStop(c.ptr());
    });
    m.def("npu_prof_finalize", platform::NPUProfilerFinalize);
    m.def("npu_prof_create_config", []() {
      return platform::NPUProfConfigWrapper(platform::NPUProfilerCreateConfig());
    });
  
    m.def("npu_prof_destropy_config", [](platform::NPUProfConfigWrapper c) {
      platform::NPUProfilerDestroyConfig(c.ptr());
    });
  #endif
  
  #ifdef PADDLE_WITH_IPU
    m.def("get_ipu_device_count", platform::GetIPUDeviceCount);
  #endif
  
  #ifdef PADDLE_WITH_MLU
    m.def("get_mlu_device_count", platform::GetMLUDeviceCount);
  #endif
  
    py::enum_<platform::TracerOption>(m, "TracerOption", py::arithmetic())
        .value("kDefault", platform::TracerOption::kDefault)
        .value("kOpDetail", platform::TracerOption::kOpDetail)
        .value("kAllOpDetail", platform::TracerOption::kAllOpDetail)
        .export_values();
  
    py::enum_<platform::ProfilerState>(m, "ProfilerState", py::arithmetic())
        .value("kDisabled", platform::ProfilerState::kDisabled)
        .value("kCPU", platform::ProfilerState::kCPU)
        .value("kCUDA", platform::ProfilerState::kCUDA)
        .value("kAll", platform::ProfilerState::kAll)
        .export_values();
  
    py::enum_<platform::EventSortingKey>(m, "EventSortingKey", py::arithmetic())
        .value("kDefault", platform::EventSortingKey::kDefault)
        .value("kCalls", platform::EventSortingKey::kCalls)
        .value("kTotal", platform::EventSortingKey::kTotal)
        .value("kMin", platform::EventSortingKey::kMin)
        .value("kMax", platform::EventSortingKey::kMax)
        .value("kAve", platform::EventSortingKey::kAve)
        .export_values();
  
    m.def("set_tracer_option", platform::SetTracerOption);
    m.def("enable_profiler", platform::EnableProfiler);
    m.def("disable_profiler", platform::DisableProfiler);
    m.def("is_profiler_enabled", platform::IsProfileEnabled);
    m.def("reset_profiler", platform::ResetProfiler);
    m.def("register_pass", [](const std::string &pass_type, py::object callable) {
      PADDLE_ENFORCE_EQ(
          framework::ir::PassRegistry::Instance().Has(pass_type), false,
          platform::errors::AlreadyExists("Pass '%s' is registered more than "
                                          "once. Please use another name.",
                                          pass_type));
      callable.inc_ref();
      framework::ir::PassRegistry::Instance().Insert(pass_type, [pass_type,
                                                                 callable]() {
        py::gil_scoped_acquire guard;
        std::unique_ptr<framework::ir::Pass> pass(
            new framework::ir::GeneratePass(py::cast<std::string>(callable())));
        return pass;
      });
    });
    m.def("get_pass", [](const std::string &pass_type) {
      auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
      return std::shared_ptr<framework::ir::Pass>(std::move(pass));
    });
  
    m.def("size_of_dtype", framework::SizeOfType);
  
  #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    m.def("set_cublas_switch", platform::SetAllowTF32Cublas);
    m.def("get_cublas_switch", platform::AllowTF32Cublas);
    m.def("set_cudnn_switch", platform::SetAllowTF32Cudnn);
    m.def("get_cudnn_switch", platform::AllowTF32Cudnn);
  #endif  // PADDLE_WITH_CUDA
    m.def("clear_executor_cache",
          []() { framework::ExecutorInfoCache::Instance().Finalize(); });
  
    using VarQuantScale =
        std::unordered_map<std::string, std::pair<bool, LoDTensor>>;
  
    py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
    pass.def(py::init())
        .def("has", &ir::Pass::Has)
        .def("set_not_owned",
             [](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) {
               self.SetNotOwned<ProgramDesc>(attr_name, &attr);
             })
        .def(
            "set",
            [](ir::Pass &self, const std::string &name, const std::string &attr) {
              self.Set<std::string>(name, new std::string(attr));
            })
        .def("set", [](ir::Pass &self, const std::string &name,
                       bool val) { self.Set<bool>(name, new bool(val)); })
        .def("set", [](ir::Pass &self, const std::string &name,
                       int val) { self.Set<const int>(name, new int(val)); })
        .def("set",
             [](ir::Pass &self, const std::string &name,
                std::vector<std::string> set) {
               self.Set(name, new std::vector<std::string>(set));
             })
        .def("set",
             [](ir::Pass &self, const std::string &name,
                std::unordered_set<std::string> set) {
               self.Set(name, new std::unordered_set<std::string>(set));
             })
        .def("set",
             [](ir::Pass &self, const std::string &name,
                std::unordered_set<int> set) {
               self.Set(name, new std::unordered_set<int>(set));
             })
        .def("set",
             [](ir::Pass &self, const std::string &name, VarQuantScale scales) {
               self.Set(name, new VarQuantScale(scales));
             })
        .def("type", &ir::Pass::Type)
        .def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
          self.Apply(graph.get());
        });
  
    py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(
        m, "PassBuilder");
    pb.def(py::init())
        .def("append_pass",
             [](ir::PassBuilder &self,
                const std::string &pass_type) -> std::shared_ptr<ir::Pass> {
               return self.AppendPass(pass_type);
             })
        .def("all_passes", [](ir::PassBuilder &self) { return self.AllPasses(); })
        .def("insert_pass",
             [](ir::PassBuilder &self, size_t idx, const std::string &pass_type) {
               return self.InsertPass(idx, pass_type);
             })
        .def("remove_pass",
             [](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); });
  
    // -- python binds for parallel executor.
    py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
    py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy", R"DOC(
      ExecutionStrategy allows the user to more preciously control how to run
      the program in ParallelExecutor by setting the property.
  
      Returns:
          ExecutionStrategy: An ExecutionStrategy object.
  
      Examples:
          .. code-block:: python
  
            import paddle
            import paddle.static as static
            import paddle.nn.functional as F
  
            paddle.enable_static()
  
            x = static.data(name='x', shape=[None, 13], dtype='float32')
            y = static.data(name='y', shape=[None, 1], dtype='float32')
            y_predict = static.nn.fc(input=x, size=1, act=None)
  
            cost = F.square_error_cost(input=y_predict, label=y)
            avg_loss = paddle.mean(cost)
  
            sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001)
            sgd_optimizer.minimize(avg_loss)
  
            exec_strategy = static.ExecutionStrategy()
            exec_strategy.num_threads = 4
  
            train_exe = static.ParallelExecutor(use_cuda=False,
                                                loss_name=avg_loss.name,
                                                exec_strategy=exec_strategy)
          )DOC");
  
    py::enum_<paddle::platform::DeviceType>(m, "DeviceType", py::arithmetic())
        .value("CPU", paddle::platform::DeviceType::CPU)
        .value("CUDA", paddle::platform::DeviceType::CUDA)
        .value("XPU", paddle::platform::DeviceType::XPU);
  
    exec_strategy.def(py::init())
        .def_property(
            "num_threads",
            [](const ExecutionStrategy &self) { return self.num_threads_; },
            [](ExecutionStrategy &self, size_t num_threads) {
              self.num_threads_ = num_threads;
            },
            R"DOC(
              The type is INT, num_threads represents the size of thread pool that
              used to run the operators of the current program in ParallelExecutor.
              If :math:`num\_threads=1`, all the operators will execute one by one,
              but the order maybe difference between iterations.
              If it is not set, it will be set in ParallelExecutor according to the
              device type and device count, for GPU, :math:`num\_threads=device\_count*4`, for CPU,
              :math:`num\_threads=CPU\_NUM*4`, the explanation of:math:`CPU\_NUM` is in ParallelExecutor.
              if it is not set, ParallelExecutor will get the cpu count by calling
              `multiprocessing.cpu_count()`. Default 0.
  
              Examples:
                  .. code-block:: python
  
                      import paddle
                      import paddle.static as static
  
                      paddle.enable_static()
  
                      exec_strategy = static.ExecutionStrategy()
                      exec_strategy.num_threads = 4
              )DOC")
        .def_property(
            "_use_device",
            [](const ExecutionStrategy &self) { return self.use_device_; },
            [](ExecutionStrategy &self, paddle::platform::DeviceType use_device) {
              self.use_device_ = use_device;
            })  // NOTE(liuyuhui): Doesn't add doc for 'use_device', because
                // use_device isn‘t exposed to users.
        .def_property(
            "allow_op_delay",
            [](const ExecutionStrategy &self) { return self.allow_op_delay_; },
            [](ExecutionStrategy &self, bool allow_op_delay) {
              self.allow_op_delay_ = allow_op_delay;
            },
            R"DOC(The type is BOOL, allow_op_delay represents whether to delay the
                  communication operators to run, it may make the execution faster.
                  Note that this option is invalid now, and it will be removed in
                  next version. Default False.)DOC")
        .def_property(
            "num_iteration_per_drop_scope",
            [](const ExecutionStrategy &self) {
              return self.num_iteration_per_drop_scope_;
            },
            [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
              self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
            },
            R"DOC(The type is INT, num_iteration_per_drop_scope indicates how
                  many iterations to clean up the temp variables which
                  is generated during execution. It may make the execution faster,
                  because the temp variable's shape maybe the same between two iterations.
                  Default 100.
  
                  .. note::
                      1. If you fetch data when calling the 'run', the ParallelExecutor 
                      will clean up the temp variables at the end of the current iteration. 
                      2. In some NLP model, it may cause the GPU memory is insufficient, 
                      in this case, you should reduce `num_iteration_per_drop_scope`.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          exec_strategy = static.ExecutionStrategy()
                          exec_strategy.num_iteration_per_drop_scope = 10
                )DOC")
        .def_property(
            "num_iteration_per_run",
            [](const ExecutionStrategy &self) {
              return self.num_iteration_per_run_;
            },
            [](ExecutionStrategy &self, size_t num_iteration_per_run) {
              self.num_iteration_per_run_ = num_iteration_per_run;
            },
            R"DOC(This config that how many iteration the executor will run when
                  user call exe.run() in python。Default: 1.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          exec_strategy = static.ExecutionStrategy()
                          exec_strategy.num_iteration_per_run = 10
                )DOC")
        .def_property(
            "use_thread_barrier",
            [](const ExecutionStrategy &self) { return self.thread_barrier_; },
            [](ExecutionStrategy &self, bool use_thread_barrier) {
              self.thread_barrier_ = use_thread_barrier;
            },
            R"DOC(This config that the this is distributed training with parameter server
                )DOC")
        .def_property("_dry_run",
                      [](const ExecutionStrategy &self) { return self.dry_run_; },
                      [](ExecutionStrategy &self, bool dry_run) {
                        self.dry_run_ = dry_run;
                      });
  
    exec_strategy.def_property(
        "use_experimental_executor",
        [](const ExecutionStrategy &self) {
          return self.type_ == ExecutionStrategy::kExperimental;
        },
        [](ExecutionStrategy &self, bool experimental) {
          self.type_ = experimental ? ExecutionStrategy::kExperimental
                                    : ExecutionStrategy::kDefault;
        });
  
    py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy", R"DOC(
      BuildStrategy allows the user to more preciously control how to
      build the SSA Graph in ParallelExecutor by setting the property.
  
      Returns:
          BuildStrategy: An BuildStrategy object.
  
      Examples:
          .. code-block:: python
  
              import os
              import paddle
              import paddle.static as static
  
              paddle.enable_static()
  
              os.environ['CPU_NUM'] = str(2)
              places = static.cpu_places()
  
              data = static.data(name="x", shape=[None, 1], dtype="float32")
              hidden = static.nn.fc(input=data, size=10)
              loss = paddle.mean(hidden)
              paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
  
              build_strategy = static.BuildStrategy()
              build_strategy.enable_inplace = True
              build_strategy.memory_optimize = True
              build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
              program = static.CompiledProgram(static.default_main_program())
              program = program.with_data_parallel(loss_name=loss.name,
                                                    build_strategy=build_strategy,
                                                    places=places)
  )DOC");
  
    py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
        .value("Reduce", BuildStrategy::ReduceStrategy::kReduce)
        .value("AllReduce", BuildStrategy::ReduceStrategy::kAllReduce)
        .value("_NoReduce", BuildStrategy::ReduceStrategy::kNoReduce);
    py::enum_<BuildStrategy::GradientScaleStrategy>(build_strategy,
                                                    "GradientScaleStrategy")
        .value("CoeffNumDevice",
               BuildStrategy::GradientScaleStrategy::kCoeffNumDevice)
        .value("One", BuildStrategy::GradientScaleStrategy::kOne)
        .value("Customized", BuildStrategy::GradientScaleStrategy::kCustomized);
  
    build_strategy.def(py::init())
        .def("_clear_finalized", &BuildStrategy::ClearFinalized)
        .def_property(
            "reduce_strategy",
            [](const BuildStrategy &self) { return self.reduce_; },
            [](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.reduce_ = strategy;
            },
            R"DOC((fluid.BuildStrategy.ReduceStrategy, optional): there are two reduce
                  strategies in ParallelExecutor, AllReduce and Reduce. If you want
                  that all the parameters' optimization are done on all devices independently,
                  you should choose AllReduce; otherwise, if you choose Reduce, all the parameters'
                  optimization will be evenly distributed to different devices, and then
                  broadcast the optimized parameter to other devices.
                  Default is 'AllReduce'.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
                    )DOC")
        .def_property(
            "gradient_scale_strategy",
            [](const BuildStrategy &self) { return self.gradient_scale_; },
            [](BuildStrategy &self,
               BuildStrategy::GradientScaleStrategy strategy) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.gradient_scale_ = strategy;
            },
            R"DOC((paddle.static.BuildStrategy.GradientScaleStrategy, optional): there are three
                  ways of defining :math:`loss@grad` in ParallelExecutor, that is, CoeffNumDevice,
                  One and Customized. By default, ParallelExecutor sets the :math:`loss@grad`
                  according to the number of devices. If you want to customize :math:`loss@grad`,
                  you can choose Customized. Default is 'CoeffNumDevice'.
  
                  Examples:
                      .. code-block:: python
  
                          import numpy
                          import os
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          use_cuda = True
                          place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
                          exe = static.Executor(place)
  
                          # NOTE: If you use CPU to run the program, you need
                          # to specify the CPU_NUM, otherwise, paddle will use
                          # all the number of the logic core as the CPU_NUM,
                          # in that case, the batch size of the input should be
                          # greater than CPU_NUM, if not, the process will be
                          # failed by an exception.
                          if not use_cuda:
                              os.environ['CPU_NUM'] = str(2)
                              places = static.cpu_places()
                          else:
                              places = static.cuda_places()
  
                          data = static.data(name='X', shape=[None, 1], dtype='float32')
                          hidden = static.nn.fc(input=data, size=10)
                          loss = paddle.mean(hidden)
                          paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
  
                          exe.run(static.default_startup_program())
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.gradient_scale_strategy = \
                                    static.BuildStrategy.GradientScaleStrategy.Customized
                          compiled_prog = static.CompiledProgram(
                                    static.default_main_program()).with_data_parallel(
                                            loss_name=loss.name, build_strategy=build_strategy,
                                            places=places)
  
                          dev_count =  len(places)
                          x = numpy.random.random(size=(10, 1)).astype('float32')
                          loss_grad = numpy.ones((dev_count)).astype("float32") * 0.01
                          loss_grad_name = loss.name+"@GRAD"
                          loss_data = exe.run(compiled_prog,
                                                feed={"X": x, loss_grad_name : loss_grad},
                                                fetch_list=[loss.name, loss_grad_name])
                     )DOC")
        .def_property(
            "debug_graphviz_path",
            [](const BuildStrategy &self) { return self.debug_graphviz_path_; },
            [](BuildStrategy &self, const std::string &path) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.debug_graphviz_path_ = path;
            },
            R"DOC((str, optional): debug_graphviz_path indicates the path that
                  writing the SSA Graph to file in the form of graphviz.
                  It is useful for debugging. Default is empty string, that is, ""
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.debug_graphviz_path = "./graph"
                      )DOC")
        .def_property(
            "enable_sequential_execution",
            [](const BuildStrategy &self) {
              return self.enable_sequential_execution_;
            },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.enable_sequential_execution_ = b;
            },
            R"DOC((bool, optional): If set True, the execution order of ops would
                  be the same as what is in the program. Default is False.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.enable_sequential_execution = True
            )DOC")
        .def_property(
            "remove_unnecessary_lock",
            [](const BuildStrategy &self) {
              return self.remove_unnecessary_lock_;
            },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.remove_unnecessary_lock_ = b;
            },
            R"DOC((bool, optional): If set True, some locks in GPU ops would be
                  released and ParallelExecutor would run faster. Default is True.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.remove_unnecessary_lock = True
            )DOC")
        .def_property(
            "num_trainers",
            [](const BuildStrategy &self) { return self.num_trainers_; },
            [](BuildStrategy &self, int num_trainers) {
  #ifdef WIN32
              PADDLE_THROW(platform::errors::Unavailable(
                  "Distribution mode is not supported on Windows platform."));
  #endif
              self.num_trainers_ = num_trainers;
            })
        .def_property(
            "trainers_endpoints",
            [](const BuildStrategy &self) { return self.trainers_endpoints_; },
            [](BuildStrategy &self,
               const std::vector<std::string> &trainers_endpoints) {
              self.trainers_endpoints_ = trainers_endpoints;
            })
        .def_property("trainer_id",
                      [](const BuildStrategy &self) { return self.trainer_id_; },
                      [](BuildStrategy &self, int trainer_id) {
                        self.trainer_id_ = trainer_id;
                      })
        .def_property(
            "nccl_comm_num",
            [](const BuildStrategy &self) { return self.nccl_comm_num_; },
            [](BuildStrategy &self, int nccl_comm_num) {
              self.nccl_comm_num_ = nccl_comm_num;
            })
        .def_property(
            "bkcl_comm_num",
            [](const BuildStrategy &self) { return self.bkcl_comm_num_; },
            [](BuildStrategy &self, int bkcl_comm_num) {
              self.bkcl_comm_num_ = bkcl_comm_num;
            })
        .def_property("use_hierarchical_allreduce",
                      [](const BuildStrategy &self) {
                        return self.use_hierarchical_allreduce_;
                      },
                      [](BuildStrategy &self, bool use) {
                        self.use_hierarchical_allreduce_ = use;
                      })
        .def_property("hierarchical_allreduce_inter_nranks",
                      [](const BuildStrategy &self) {
                        return self.hierarchical_allreduce_inter_nranks_;
                      },
                      [](BuildStrategy &self, int nranks) {
                        self.hierarchical_allreduce_inter_nranks_ = nranks;
                      })
  
        .def_property(
            "fuse_elewise_add_act_ops",
            [](const BuildStrategy &self) {
              return self.fuse_elewise_add_act_ops_;
            },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.fuse_elewise_add_act_ops_ = b;
            },
            R"DOC((bool, optional): fuse_elewise_add_act_ops indicate whether
                  to fuse elementwise_add_op and activation_op,
                  it may make the execution faster. Default is False.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.fuse_elewise_add_act_ops = True
                       )DOC")
        .def_property(
            "fuse_bn_act_ops",
            [](const BuildStrategy &self) { return self.fuse_bn_act_ops_; },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.fuse_bn_act_ops_ = b;
            },
            R"DOC((bool, optional): fuse_bn_act_ops indicate whether
                  to fuse batch_norm and activation_op,
                  it may make the execution faster. Default is False.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.fuse_bn_act_ops = True
                       )DOC")
        .def_property(
            "fuse_bn_add_act_ops",
            [](const BuildStrategy &self) { return self.fuse_bn_add_act_ops_; },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.fuse_bn_add_act_ops_ = b;
            },
            R"DOC((bool, optional): fuse_bn_add_act_ops indicate whether
                  to fuse batch_norm, elementwise_add and activation_op,
                  it may make the execution faster. Default is True
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.fuse_bn_add_act_ops = True
                       )DOC")
        .def_property(
            "enable_auto_fusion",
            [](const BuildStrategy &self) { return self.enable_auto_fusion_; },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.enable_auto_fusion_ = b;
            },
            R"DOC((bool, optional): Whether to enable fusing subgraph to a
                  fusion_group. Now we only support fusing subgraph that composed
                  of elementwise-like operators, such as elementwise_add/mul
                  without broadcast and activations.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.enable_auto_fusion = True
                      )DOC")
        .def_property(
            "fuse_relu_depthwise_conv",
            [](const BuildStrategy &self) {
              return self.fuse_relu_depthwise_conv_;
            },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.fuse_relu_depthwise_conv_ = b;
            },
            R"DOC((bool, optional): fuse_relu_depthwise_conv indicate whether
                  to fuse relu and depthwise_conv2d,
                  it will save GPU memory and may make the execution faster.
                  This options is only available in GPU devices.
                  Default is False.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.fuse_relu_depthwise_conv = True
            )DOC")
        .def_property("fuse_broadcast_ops",
                      [](const BuildStrategy &self) {
                        return self.fuse_broadcast_ops_ == true ||
                               self.fuse_broadcast_ops_ == paddle::none;
                      },
                      [](BuildStrategy &self, bool b) {
                        PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                          platform::errors::PreconditionNotMet(
                                              "BuildStrategy has been finlaized, "
                                              "cannot be configured again."));
                        self.fuse_broadcast_ops_ = b;
                      },
                      R"DOC((bool, optional): fuse_broadcast_op indicates whether
                        to fuse the broadcast ops. Note that, in Reduce mode,
                        fusing broadcast ops may make the program faster. Because
                        fusing broadcast OP equals delaying the execution of all
                        broadcast Ops, in this case, all nccl streams are used only
                        for NCCLReduce operations for a period of time. Default False.
  
                        Examples:
                            .. code-block:: python
  
                                import paddle
                                import paddle.static as static
  
                                paddle.enable_static()
  
                                build_strategy = static.BuildStrategy()
                                build_strategy.fuse_broadcast_ops = True
                      )DOC")
        .def_property("fuse_all_optimizer_ops",
                      [](const BuildStrategy &self) {
                        return self.fuse_all_optimizer_ops_ == true ||
                               self.fuse_all_optimizer_ops_ == paddle::none;
                      },
                      [](BuildStrategy &self, bool b) {
                        PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                          platform::errors::PreconditionNotMet(
                                              "BuildStrategy has been finlaized, "
                                              "cannot be configured again."));
                        self.fuse_all_optimizer_ops_ = b;
                      })
        .def_property(
            "sync_batch_norm",
            [](const BuildStrategy &self) { return self.sync_batch_norm_; },
            [](BuildStrategy &self, bool b) {
              PADDLE_ENFORCE_NE(self.IsFinalized(), true,
                                platform::errors::PreconditionNotMet(
                                    "BuildStrategy has been finlaized, cannot be "
                                    "configured again."));
              self.sync_batch_norm_ = b;
            },
            R"DOC((bool, optional): sync_batch_norm indicates whether to use
                  synchronous batch normalization which synchronizes the mean
                  and variance through multi-devices in training phase.
                  Current implementation doesn't support FP16 training and CPU.
                  And only synchronous on one machine, not all machines. 
                  Default is False.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.sync_batch_norm = True
                  )DOC")
        .def_property(
            "memory_optimize",
            [](const BuildStrategy &self) -> py::object {
              if (self.memory_optimize_) {
                return py::cast(self.memory_optimize_.get());
              } else {
                return py::cast(nullptr);
              }
            },
            [](BuildStrategy &self, const py::handle &value) {
              auto *py_obj = value.ptr();
              if (py_obj == nullptr || py_obj == Py_None) {
                self.memory_optimize_ = paddle::none;
              } else if (PyBool_Check(py_obj)) {
                self.memory_optimize_ = (py_obj == Py_True);
              } else {
                PADDLE_THROW(platform::errors::InvalidArgument(
                    "BuildStrategy.memory_optimize must be set to None, False "
                    "or True"));
              }
            },
            R"DOC((bool, optional): memory opitimize aims to save total memory
                  consumption, set to True to enable it.
  
                  Default None. None means framework would choose to use or not use 
                  this strategy automatically. Currently, None means that it is 
                  enabled when GC is disabled, and disabled when GC is enabled. 
                  True means enabling and False means disabling. Default is None.
  
                  Examples:
                      .. code-block:: python
  
                          import paddle
                          import paddle.static as static
  
                          paddle.enable_static()
  
                          build_strategy = static.BuildStrategy()
                          build_strategy.memory_optimize = True
                  
                  )DOC")
        .def_property(
            "is_distribution",
            [](const BuildStrategy &self) { return self.is_distribution_; },
            [](BuildStrategy &self, bool b) {
  #ifdef WIN32
              if (b) {
                PADDLE_THROW(platform::errors::Unavailable(
                    "Distribution mode is not supported on Windows platform."));
              }
  #else
              self.is_distribution_ = b;
  #endif
            })
        .def_property("async_mode",
                      [](const BuildStrategy &self) { return self.async_mode_; },
                      [](BuildStrategy &self, bool b) { self.async_mode_ = b; })
        .def_property(
            "enable_inplace",
            [](const BuildStrategy &self) { return self.enable_inplace_; },
            [](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
        .def_property(
            "enable_addto",
            [](const BuildStrategy &self) { return self.enable_addto_; },
            [](BuildStrategy &self, bool b) { self.enable_addto_ = b; })
        .def_property(
            "fuse_all_reduce_ops",
            [](const BuildStrategy &self) {
              return self.fuse_all_reduce_ops_ == true ||
                     self.fuse_all_reduce_ops_ == paddle::none;
            },
            [](BuildStrategy &self, bool b) { self.fuse_all_reduce_ops_ = b; })
        .def_property("enable_backward_optimizer_op_deps",
                      [](const BuildStrategy &self) {
                        return self.enable_backward_optimizer_op_deps_;
                      },
                      [](BuildStrategy &self, bool b) {
                        self.enable_backward_optimizer_op_deps_ = b;
                      })
        .def_property(
            "cache_runtime_context",
            [](const BuildStrategy &self) { return self.cache_runtime_context_; },
            [](BuildStrategy &self, bool b) { self.cache_runtime_context_ = b; })
        .def_property(
            "mkldnn_enabled_op_types",
            [](const BuildStrategy &self) {
              return self.mkldnn_enabled_op_types_;
            },
            [](BuildStrategy &self,
               const std::unordered_set<std::string> &mkldnn_enabled_op_types) {
              self.mkldnn_enabled_op_types_ = mkldnn_enabled_op_types;
            })
        .def_property(
            "fix_op_run_order",
            [](const BuildStrategy &self) { return self.fix_op_run_order_; },
            [](BuildStrategy &self, bool fix_op_run_order) {
              self.fix_op_run_order_ = fix_op_run_order;
            })
        .def_property("allow_cuda_graph_capture",
                      [](const BuildStrategy &self) {
                        return self.allow_cuda_graph_capture_;
                      },
                      [](BuildStrategy &self, bool allow_cuda_graph_capture) {
                        self.allow_cuda_graph_capture_ = allow_cuda_graph_capture;
                      })
        .def("_copy",
             [](const BuildStrategy &self) {
               auto new_bs = self;
               new_bs.ClearFinalized();
               return new_bs;
             })
        .def("_finalize_strategy_and_create_passes",
             [](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
               return self.CreatePassesFromStrategy(true);
             },
             R"DOC(Allow user to customized passes. Normally model-specific
                  optimization passes should be defined in this way. BuildStrategy
                  cannot be updated after being finalized.)DOC");
  
    m.def("_set_cached_executor_build_strategy",
          [](int64_t program_id, const BuildStrategy &build_strategy) {
            auto &cached_exe_info = framework::ExecutorInfoCache::Instance();
            cached_exe_info.SetBuildStrategy(program_id, build_strategy);
          });
  
    pe.def(py::init<const std::vector<platform::Place> &,
                    const std::vector<std::string> &, const std::string &,
                    Scope *, std::vector<Scope *> &, const ExecutionStrategy &,
                    const BuildStrategy &, ir::Graph *>())
        // NOTE: even we return a vec<Scope*>* to Python use reference policy.
        // We still cannot get local_scope from this vector, since the element
        // of vec<Scope*> will be freed by Python GC. We can only return Scope*
        // one by one and mark them as reference.
        .def("local_scopes",
             [](ParallelExecutor &self) -> std::vector<Scope *> * {
               return &self.GetLocalScopes();
             },
             py::return_value_policy::reference)
        .def("drop_local_exe_scopes", &ParallelExecutor::DropLocalExeScopes)
        .def("_need_create_local_exe_scopes",
             &ParallelExecutor::NeedCreateLocalExeScope)
        .def("feed_tensors_into_local_scopes",
             &ParallelExecutor::FeedTensorsIntoLocalScopes)
        .def("feed_and_split_tensor_into_local_scopes",
             &ParallelExecutor::FeedAndSplitTensorIntoLocalScopes)
        .def("run",
             [](ParallelExecutor &self,
                const std::vector<std::string> &fetch_tensors,
                bool return_merged) -> py::object {
               paddle::framework::FetchResultType ret;
               {
                 pybind11::gil_scoped_release release;
                 ret = self.Run(fetch_tensors, return_merged);
               }
               if (return_merged) {
                 return py::cast(
                     std::move(BOOST_GET(paddle::framework::FetchList, ret)));
               } else {
                 return py::cast(std::move(
                     BOOST_GET(paddle::framework::FetchUnmergedList, ret)));
               }
             })
        .def("device_count", &ParallelExecutor::DeviceCount);
  
  #ifdef PADDLE_WITH_IPU
    py::class_<platform::ipu::IpuBackend,
               std::unique_ptr<platform::ipu::IpuBackend, py::nodelete>>(
        m, "IpuBackend")
        // manage IpuBackend in C++
        .def("get_instance",
             []() {
               return std::unique_ptr<platform::ipu::IpuBackend, py::nodelete>(
                   platform::ipu::IpuBackend::GetInstance());
             },
             py::return_value_policy::reference)
        .def("detach", &platform::ipu::IpuBackend::Detach)
        .def("reset", &platform::ipu::IpuBackend::Reset)
        .def("set_scope", &platform::ipu::IpuBackend::SetScope)
        .def("set_ipu_strategy", &platform::ipu::IpuBackend::SetIpuStrategy)
        .def("save_model_proto", &platform::ipu::IpuBackend::SaveModelProto);
  
    py::class_<platform::ipu::IpuStrategy>(m, "IpuStrategy")
        .def(py::init())
        .def("set_options",
             [](platform::ipu::IpuStrategy &self, const py::dict &opt) {
               for (auto element : opt) {
                 auto option_name = element.first.cast<std::string>();
                 VLOG(10) << "Set option: " << option_name;
                 if (py::isinstance<py::bool_>(element.second)) {
                   self.AddBoolOption(option_name, element.second.cast<bool>());
                 } else if (py::isinstance<py::float_>(element.second)) {
                   self.AddDoubleOption(option_name,
                                        element.second.cast<double>());
                 } else if (py::isinstance<py::int_>(element.second)) {
                   self.AddUint64Option(option_name,
                                        element.second.cast<std::uint64_t>());
                 } else if (py::isinstance<py::str>(element.second)) {
                   self.AddStringOption(option_name,
                                        element.second.cast<std::string>());
                 } else if (py::isinstance<py::set>(element.second) ||
                            py::isinstance<py::list>(element.second)) {
                   for (auto option : element.second.cast<py::list>()) {
                     std::string option_val;
                     if (py::isinstance<py::str>(option)) {
                       option_val = option.cast<std::string>();
                     } else if (py::isinstance<py::int_>(option)) {
                       option_val = std::to_string(option.cast<std::uint64_t>());
                     } else {
                       PADDLE_THROW(platform::errors::Unimplemented(
                           "Failed to convert type: %s when set IpuStrategy "
                           "option: %s",
                           option.get_type(), option_name));
                     }
                     self.InsertStringOption(option_name, option_val);
                   }
                 } else if (py::isinstance<py::dict>(element.second)) {
                   if (option_name.rfind("location_", 0) == 0) {
                     for (auto option : element.second.cast<py::dict>()) {
                       self.SetTensorLocation(
                           option_name, option.first.cast<std::string>(),
                           option.second.cast<std::uint64_t>());
                     }
                   } else if (option_name == "custom_op") {
                     std::string paddle_op;
                     std::string popart_op;
                     std::string domain;
                     int version = -1;
                     for (auto option : element.second.cast<py::dict>()) {
                       std::string option_key = option.first.cast<std::string>();
                       if (option_key == "paddle_op") {
                         paddle_op = option.second.cast<std::string>();
                       } else if (option_key == "popart_op") {
                         popart_op = option.second.cast<std::string>();
                       } else if (option_key == "domain") {
                         domain = option.second.cast<std::string>();
                       } else if (option_key == "version") {
                         version = option.second.cast<int>();
                       } else {
                         PADDLE_THROW(platform::errors::InvalidArgument(
                             "Invalid argument, key must be one of paddle_op, "
                             "popart_op, domain or version, but revecived %s",
                             option_key));
                       }
                     }
                     self.AddCustomOp(paddle_op, popart_op, domain, version);
                   } else {
                     for (auto option : element.second.cast<py::dict>()) {
                       std::string option_key = option.first.cast<std::string>();
                       std::string option_val;
                       if (py::isinstance<py::str>(option.second)) {
                         option_val = option.second.cast<std::string>();
                       } else if (py::isinstance<py::int_>(option.second)) {
                         option_val =
                             std::to_string(option.second.cast<std::uint64_t>());
                       } else {
                         PADDLE_THROW(platform::errors::Unimplemented(
                             "Failed to convert value type: %s when set "
                             "IpuStrategy option: %s",
                             option.second.get_type(), option_key));
                       }
                       self.InsertStringPairOption(option_name, option_key,
                                                   option_val);
                     }
                   }
                 } else {
                   PADDLE_THROW(platform::errors::InvalidArgument(
                       "Invalid IpuStrategy option value type: %s, please check "
                       "input value for option: %s",
                       element.second.get_type(), option_name));
                 }
               }
             })
        .def("get_option",
             [](platform::ipu::IpuStrategy &self, const std::string &name) {
               py::dict res;
               auto option_type = self.GetOptionType(name);
               res["name"] = name;
               res["type"] = option_type;
               if (option_type == "vector") {
                 auto value = self.GetVectorOption(name);
                 res["value"] = value;
               } else if (option_type == "map") {
                 auto value = self.GetMapOption(name);
                 res["value"] = value;
               } else {
                 auto value_s = self.GetOption(name);
                 res["value_s"] = value_s;
                 if (option_type == "bool") {
                   res["value"] = static_cast<bool>(std::stoi(value_s));
                 } else if (option_type == "uint64") {
                   res["value"] = std::stoul(value_s);
                 } else if (option_type == "double") {
                   res["value"] = std::stod(value_s);
                 } else if (option_type == "string") {
                   res["value"] = value_s;
                 }
               }
               return res;
             })
        .def("get_all_option_names",
             &platform::ipu::IpuStrategy::GetAllOptionNames)
        .def("enable_pattern", &platform::ipu::IpuStrategy::EnablePattern)
        .def("disable_pattern", &platform::ipu::IpuStrategy::DisablePattern)
        .def("is_pattern_enabled", &platform::ipu::IpuStrategy::IsPatternEnabled);
  #endif
  
    BindFleetWrapper(&m);
    BindIO(&m);
  
  #if defined(PADDLE_WITH_PSLIB) && !defined(PADDLE_WITH_HETERPS)
    BindHeterWrapper(&m);
    BindMetrics(&m);
  #endif
  #ifdef PADDLE_WITH_HETERPS
    BindPSGPUWrapper(&m);
  #endif
    BindGlooWrapper(&m);
    BindBoxHelper(&m);
  #ifdef PADDLE_WITH_BOX_PS
    BindBoxWrapper(&m);
  #endif
  #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
    BindNCCLWrapper(&m);
  #endif
  #ifdef PADDLE_WITH_GLOO
    BindGlooContext(&m);
  #endif
    BindGraph(&m);
    BindNode(&m);
    BindPass(&m);
    BindInferenceApi(&m);
    BindCompatible(&m);
    BindDataset(&m);
    BindGenerator(&m);
  #ifndef PADDLE_ON_INFERENCE
    BindDistributed(&m);
  #endif
  #ifdef PADDLE_WITH_ASCEND
    BindAscendWrapper(&m);
    BindAscendGraph(&m);
    BindAscendDevice(&m);
  #endif
  #ifdef PADDLE_WITH_CRYPTO
    BindCrypto(&m);
  #endif
  
  #if defined PADDLE_WITH_PSCORE
    BindDistFleetWrapper(&m);
    BindPSHost(&m);
    BindCommunicatorContext(&m);
    BindDistCommunicator(&m);
    BindHeterClient(&m);
    BindGraphPyFeatureNode(&m);
    BindGraphNode(&m);
    BindGraphPyService(&m);
    BindGraphPyServer(&m);
    BindGraphPyClient(&m);
    BindIndexNode(&m);
    BindTreeIndex(&m);
    BindIndexWrapper(&m);
    BindIndexSampler(&m);
    BindSparseShardingTools(&m);
  #endif
  }
  }  // namespace pybind
  }  // namespace paddle
  