pybind.cc

/* 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 <map>
#include <mutex>  // NOLINT // for call_once
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>

#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/init.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/parallel_executor.h"
#include "paddle/fluid/framework/prune.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/pybind/const_value.h"
#include "paddle/fluid/pybind/exception.h"
#include "paddle/fluid/pybind/protobuf.h"
#include "paddle/fluid/pybind/pybind.h"  // NOLINT
#include "paddle/fluid/pybind/recordio.h"
#include "paddle/fluid/pybind/tensor_py.h"

#include "paddle/fluid/string/to_string.h"

#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/operators/nccl/nccl_gpu_common.h"
#include "paddle/fluid/platform/cuda_profiler.h"
#include "paddle/fluid/platform/gpu_info.h"
#endif

// disable auto conversion to list in Python
PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);

namespace paddle {
namespace pybind {
bool IsCompiledWithCUDA() {
#ifndef PADDLE_WITH_CUDA
  return false;
#else
  return true;
#endif
}

PYBIND11_PLUGIN(core) {
  py::module m("core", "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);

  py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
      .def_buffer(
          [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
      .def("get_dims",
           [](const Tensor &self) { return vectorize(self.dims()); })
      .def("set_dims",
           [](Tensor &self, const std::vector<int64_t> &dim) {
             self.Resize(make_ddim(dim));
           })
      .def("set_layout",
           [](Tensor &self, const std::string &layout) {
             self.set_layout(StringToDataLayout(layout));
           })
      .def("alloc_float",
           [](Tensor &self, paddle::platform::CUDAPlace &place) {
             self.mutable_data<float>(place);
           })
      .def("alloc_float",
           [](Tensor &self, paddle::platform::CPUPlace &place) {
             self.mutable_data<float>(place);
           })
      .def("alloc_int",
           [](Tensor &self, paddle::platform::CPUPlace &place) {
             self.mutable_data<int>(place);
           })
      .def("alloc_int",
           [](Tensor &self, paddle::platform::CUDAPlace &place) {
             self.mutable_data<int>(place);
           })
      .def("alloc_int",
           [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
             self.mutable_data<int>(place);
           })
      .def("alloc_float",
           [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
             self.mutable_data<float>(place);
           })
      .def("set", PyCPUTensorSetFromArray<float>)
      .def("set", PyCPUTensorSetFromArray<int>)
      .def("set", PyCPUTensorSetFromArray<double>)
      .def("set", PyCPUTensorSetFromArray<int64_t>)
      .def("set", PyCPUTensorSetFromArray<bool>)
      .def("set", PyCPUTensorSetFromArray<uint16_t>)
      .def("set", PyCPUTensorSetFromArray<uint8_t>)
#ifdef PADDLE_WITH_CUDA
      .def("set", PyCUDATensorSetFromArray<float>)
      .def("set", PyCUDATensorSetFromArray<int>)
      .def("set", PyCUDATensorSetFromArray<double>)
      .def("set", PyCUDATensorSetFromArray<int64_t>)
      .def("set", PyCUDATensorSetFromArray<bool>)
      .def("set", PyCUDATensorSetFromArray<uint16_t>)
      .def("set", PyCUDATensorSetFromArray<uint8_t>)
      .def("set", PyCUDAPinnedTensorSetFromArray<float>)
      .def("set", PyCUDAPinnedTensorSetFromArray<int>)
      .def("set", PyCUDAPinnedTensorSetFromArray<double>)
      .def("set", PyCUDAPinnedTensorSetFromArray<int64_t>)
      .def("set", PyCUDAPinnedTensorSetFromArray<bool>)
      .def("set", PyCUDAPinnedTensorSetFromArray<uint16_t>)
      .def("set", PyCUDAPinnedTensorSetFromArray<uint8_t>)
#endif
      .def("shape", [](Tensor &self) { return vectorize(self.dims()); })
      .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("dtype", [](Tensor &self) { return ToDataType(self.type()); });

  py::class_<LoDTensor, Tensor>(m, "LoDTensor")
      .def_buffer(
          [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
      .def("__init__",
           [](LoDTensor &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(
                 CheckLoD(new_offset_lod, -1),
                 "the provided recursive_sequence_lengths info is invalid");
             new (&instance) LoDTensor(new_offset_lod);
           })
      .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
      // 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",
           [](LoDTensor &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(CheckLoD(new_lod, vectorize(self.dims()).front()),
                            "the provided lod info is invalid");
             self.set_lod(new_lod);
           })
      .def("set_recursive_sequence_lengths",
           [](LoDTensor &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(
                 CheckLoD(new_offset_lod, vectorize(self.dims()).front()),
                 "the provided recursive_sequence_lengths info is invalid");
             self.set_lod(new_offset_lod);
           })
      .def("lod",
           [](LoDTensor &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;
           })
      // Set above comments of set_lod.
      .def("recursive_sequence_lengths",
           [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
             // output the length-based lod info
             LoD lod = 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;
           })
      .def("has_valid_recursive_sequence_lengths", [](LoDTensor &self) -> bool {
        // Check that the lod info is valid and match the outermost
        // dimension of the LoDTensor data
        return CheckLoD(self.lod(), vectorize(self.dims()).front());
      });

  py::class_<SelectedRows>(m, "SelectedRows")
      .def("__init__",
           [](SelectedRows &instance) { new (&instance) SelectedRows(); })
      .def("__init__",
           [](SelectedRows &instance, const std::vector<int64_t> rows,
              const int64_t &height) {
             new (&instance) SelectedRows(rows, height);
           })
      .def("get_tensor",
           [](SelectedRows &self) { return self.mutable_value(); },
           py::return_value_policy::reference)
      .def("set_height", &SelectedRows::set_height)
      .def("height", &SelectedRows::height)
      .def("set_rows",
           [](SelectedRows &self, std::vector<int64_t> rows) {
#ifndef PADDLE_WITH_CUDA
             self.set_rows(rows);
#else
        Vector<int64_t> new_rows(rows);
        self.set_rows(new_rows);
#endif
           })
      .def("rows", [](SelectedRows &self) {
#ifndef PADDLE_WITH_CUDA
        return self.rows();
#else
         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;
#endif
      });

  py::class_<Variable>(m, "Variable", R"DOC(Variable Class.

All parameter, weight, gradient are variables in Paddle.
)DOC")
      .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_lod_rank_table",
           [](Variable &self) { return self.GetMutable<LoDRankTable>(); },
           py::return_value_policy::reference)
      .def("get_selected_rows",
           [](Variable &self) -> SelectedRows * {
             return self.GetMutable<SelectedRows>();
           },
           py::return_value_policy::reference)
      .def("get_lod_tensor_array",
           [](Variable &self) { return self.GetMutable<LoDTensorArray>(); },
           py::return_value_policy::reference)
#ifdef PADDLE_WITH_CUDA
      .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(self.IsType<framework::ReaderHolder>());
             return self.GetMutable<framework::ReaderHolder>();
           },
           py::return_value_policy::reference);

  py::class_<framework::ReaderHolder>(m, "Reader", "")
      .def("reset", &framework::ReaderHolder::ReInit);

  using LoDTensorBlockingQueue =
      ::paddle::operators::reader::LoDTensorBlockingQueue;
  using LoDTensorBlockingQueueHolder =
      ::paddle::operators::reader::LoDTensorBlockingQueueHolder;
  py::class_<LoDTensorBlockingQueue>(m, "LoDTensorBlockingQueue", "")
      .def("enqueue",
           [](LoDTensorBlockingQueue &self,
              const std::vector<framework::LoDTensor> &lod_tensor_vec) {
             pybind11::gil_scoped_release release;
             return self.Enqueue(lod_tensor_vec);
           })
      .def("size",
           [](const LoDTensorBlockingQueue &self) { return self.Size(); })
      .def("capacity",
           [](const LoDTensorBlockingQueue &self) { return self.Cap(); })
      .def("close", [](LoDTensorBlockingQueue &self) { return self.Close(); })
      .def("is_closed",
           [](const LoDTensorBlockingQueue &self) { return self.IsClosed(); });

  m.def("init_lod_tensor_blocking_queue",
        [](Variable &var, size_t capacity,
           const std::vector<std::vector<int64_t>> &shapes)
            -> LoDTensorBlockingQueue * {
              std::vector<DDim> dims(shapes.size());
              std::transform(shapes.begin(), shapes.end(), dims.begin(),
                             [](const std::vector<int64_t> &shape) {
                               return make_ddim(shape);
                             });
              auto *holder = var.GetMutable<LoDTensorBlockingQueueHolder>();
              holder->InitOnce(capacity, dims);
              return holder->GetQueue().get();
            },
        py::return_value_policy::reference);

  py::class_<Scope>(m, "Scope", "")
      .def("var",
           [](Scope &self, const std::string &name) -> Variable * {
             return self.Var(name);
           },
           py::return_value_policy::reference)
      .def("find_var", &Scope::FindVar, py::return_value_policy::reference)
      .def(py::init<>())
      .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
           py::return_value_policy::reference)
      .def("drop_kids", &Scope::DropKids);

  //! @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(
            info.Proto().SerializeToString(&str),
            "Serialize OpProto Error. This could be a bug of Paddle.");
        ret_values.emplace_back(str);
      }
    }
    return ret_values;
  });
  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("prune", [](const ProgramDesc &origin,
                    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;
    Prune(*prog_with_targets.Proto(), &pruned_desc);
    return new ProgramDesc(pruned_desc);
  });
  m.def("inference_optimize", [](ProgramDesc &origin) {
    proto::ProgramDesc pruned_desc;
    InferenceOptimize(*(origin.Proto()), &pruned_desc);
    return new ProgramDesc(pruned_desc);
  });
  m.def("empty_var_name", []() { return framework::kEmptyVarName; });
  m.def("grad_var_suffix", []() { return 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* {
                    return new paddle::platform::CPUDeviceContext();
                  })
      .def_static("create",
                  [](paddle::platform::CUDAPlace& place)
                      -> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_CUDA
                    PADDLE_THROW("CUDAPlace is not supported in CPU device.");
#else
                    return new paddle::platform::CUDADeviceContext(place);
#endif
                  })
          .def_static("create",
                [](paddle::platform::CUDAPinnedPlace& place)
                        -> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_CUDA
                  PADDLE_THROW(
                        "CUDAPinnedPlace is not supported in CPU device.");
#else
                  return new paddle::platform::CUDAPinnedDeviceContext(place);
#endif
                });;
// clang-format on
#ifdef PADDLE_WITH_CUDA
  py::class_<platform::Communicator>(m, "Communicator").def(py::init<>());
#endif
  py::class_<platform::CUDAPlace>(m, "CUDAPlace")
      .def(py::init<int>())
      .def("__str__", string::to_string<const platform::CUDAPlace &>);

  py::class_<paddle::platform::CPUPlace>(m, "CPUPlace")
      .def(py::init<>())
      .def("__str__", string::to_string<const platform::CPUPlace &>);

  py::class_<paddle::platform::CUDAPinnedPlace>(m, "CUDAPinnedPlace")
      .def(py::init<>())
      .def("__str__", string::to_string<const platform::CUDAPinnedPlace &>);

  py::class_<platform::Place>(m, "Place")
      .def(py::init<>())
      .def("set_place",
           [](platform::Place &self, const platform::CPUPlace &cpu_place) {
             self = cpu_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;
      });

  py::class_<OperatorBase>(m, "Operator")
      .def_static("create",
                  [](py::bytes protobin) {
                    proto::OpDesc desc;
                    PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
                                   "Cannot parse user input to OpDesc");
                    PADDLE_ENFORCE(desc.IsInitialized(),
                                   "User OpDesc is not initialized, reason %s",
                                   desc.InitializationErrorString());
                    return OpRegistry::CreateOp(desc);
                  })
      .def("run",
           [](OperatorBase &self, const Scope &scope,
              const platform::CPUPlace &place) { self.Run(scope, place); })
      .def("run",
           [](OperatorBase &self, const Scope &scope,
              const platform::CUDAPlace &place) { self.Run(scope, place); })
      .def("run",
           [](OperatorBase &self, const Scope &scope,
              const platform::CUDAPinnedPlace &place) {
             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::Executor>(m, "Executor")
      .def(py::init<const platform::Place &>())
#ifdef PADDLE_WITH_DISTRIBUTE
      .def("complete", &Executor::Complete)
#endif
      .def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope,
                     int block_id, bool create_local_scope, bool create_vars) {
        pybind11::gil_scoped_release release;
        self.Run(prog, scope, block_id, create_local_scope, create_vars);
      });

  m.def("init_gflags", framework::InitGflags);
  m.def("init_glog", framework::InitGLOG);
  m.def("init_devices",
        [](bool init_p2p) { framework::InitDevices(init_p2p); });

  m.def("is_compiled_with_cuda", IsCompiledWithCUDA);
#ifdef PADDLE_WITH_CUDA
  m.def("is_float16_supported", [](const platform::CUDAPlace &place) -> bool {
    // Only GPUs with Compute Capability >= 53 support float16
    return platform::GetCUDAComputeCapability(place.device) >= 53;
  });
#endif

  m.def("set_feed_variable", framework::SetFeedVariable);
  m.def("get_fetch_variable", framework::GetFetchVariable);

  BindProgramDesc(&m);
  BindBlockDesc(&m);
  BindVarDsec(&m);
  BindOpDesc(&m);
  BindConstValue(&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>(m, "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());
             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());
      });

  m.def("IsInplace",
        [](std::string op) -> bool { return operators::IsInplace(op); });

  m.def("op_support_gpu", OpSupportGPU);
#ifdef PADDLE_WITH_CUDA
  m.def("get_cuda_device_count", platform::GetCUDADeviceCount);

  m.def("nvprof_init", platform::CudaProfilerInit);
  m.def("nvprof_start", platform::CudaProfilerStart);
  m.def("nvprof_stop", platform::CudaProfilerStop);
#endif

  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("enable_profiler", platform::EnableProfiler);
  m.def("disable_profiler", platform::DisableProfiler);
  m.def("is_profiler_enabled", platform::IsProfileEnabled);
  m.def("reset_profiler", platform::ResetProfiler);

  // -- python binds for parallel executor.
  py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
  py::class_<ExecutionStrategy>(pe, "ExecutionStrategy")
      .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;
          })
      .def_property(
          "use_cuda",
          [](const ExecutionStrategy &self) { return self.use_cuda_; },
          [](ExecutionStrategy &self, bool use_cuda) {
            self.use_cuda_ = use_cuda;
          })
      .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;
          })
      .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;
          });
  py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");

  py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
      .value("Reduce", BuildStrategy::ReduceStrategy::kReduce)
      .value("AllReduce", BuildStrategy::ReduceStrategy::kAllReduce);
  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_property(
          "reduce_strategy",
          [](const BuildStrategy &self) { return self.reduce_; },
          [](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) {
            self.reduce_ = strategy;
          })
      .def_property(
          "gradient_scale_strategy",
          [](const BuildStrategy &self) { return self.gradient_scale_; },
          [](BuildStrategy &self,
             BuildStrategy::GradientScaleStrategy strategy) {
            self.gradient_scale_ = strategy;
          })
      .def_property(
          "debug_graphviz_path",
          [](const BuildStrategy &self) { return self.debug_graphviz_path_; },
          [](BuildStrategy &self, const std::string &path) {
            self.debug_graphviz_path_ = path;
          });

  pe.def(py::init<const std::vector<platform::Place> &,
                  const std::unordered_set<std::string> &,
                  const std::unordered_set<std::string> &, const ProgramDesc &,
                  const std::string &, Scope *, std::vector<Scope *> &,
                  const ExecutionStrategy &, const BuildStrategy &, size_t,
                  size_t>())
      .def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
      // 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("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,
                     const std::string &fetched_var_name) {
        pybind11::gil_scoped_release release;
        self.Run(fetch_tensors, fetched_var_name);
      });

  BindRecordIOWriter(&m);
  return m.ptr();
}
}  // namespace pybind
}  // namespace paddle