pybind.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 <fstream>
#include <vector>

#include "paddle/framework/backward.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cond_op.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/pybind.h"
#include "paddle/pybind/tensor_py.h"
#include "paddle/string/to_string.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"

namespace py = pybind11;

namespace paddle {
namespace framework {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;

static size_t UniqueIntegerGenerator() {
  static std::atomic<size_t> generator;
  return generator.fetch_add(1);
}

bool IsCompileGPU() {
#ifdef PADDLE_ONLY_CPU
  return false;
#else
  return true;
#endif
}

template <typename T>
inline std::vector<T> RepeatedToVector(
    const google::protobuf::RepeatedField<T> &repeated_field) {
  std::vector<T> ret;
  ret.reserve(repeated_field.size());
  std::copy(
      repeated_field.begin(), repeated_field.end(), std::back_inserter(ret));
  return ret;
}

template <typename T, typename RepeatedField>
inline void VectorToRepeated(const std::vector<T> &vec,
                             RepeatedField *repeated_field) {
  repeated_field->Reserve(vec.size());
  for (auto &elem : vec) {
    *repeated_field->Add() = elem;
  }
}

PYBIND11_PLUGIN(core) {
  py::module m("core", "C++ core of PaddlePaddle");

  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("alloc_float",
           [](Tensor &self, paddle::platform::GPUPlace &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::GPUPlace &place) {
             self.mutable_data<int>(place);
           })
      .def("set", PyCPUTensorSetFromArray<float>)
      .def("set", PyCPUTensorSetFromArray<int>)
#ifndef PADDLE_ONLY_CPU
      .def("set", PyCUDATensorSetFromArray<float>)
      .def("set", PyCUDATensorSetFromArray<int>)
#endif
      .def("shape", [](Tensor &self) { return vectorize(self.dims()); })
      .def("set_float_element",
           [](Tensor &self, size_t offset, float f) {
             // TODO(yuyang18): Only support GPU now.
             self.data<float>()[offset] = f;
           })
      .def("get_float_element", [](Tensor &self, size_t offset) -> float {
        // TODO(yuyang18): Only support GPU now.
        return self.data<float>()[offset];
      });

  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>> &lod) {
#ifdef PADDLE_ONLY_CPU
            new (&instance) LoDTensor(lod);
#else
             paddle::framework::LoD new_lod;
             new_lod.reserve(lod.size());
             std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
             new (&instance) LoDTensor(new_lod);
#endif
          })
      .def("set_lod",
           [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
#ifdef PADDLE_ONLY_CPU
             self.set_lod(lod);
#else
             paddle::framework::LoD new_lod;
             new_lod.reserve(lod.size());
             std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
             self.set_lod(new_lod);
#endif
           })
      .def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
#ifdef PADDLE_ONLY_CPU
        return self.lod();
#else
           auto lod = self.lod();
           std::vector<std::vector<size_t>> new_lod;
           new_lod.reserve(lod.size());
           std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
               [](paddle::framework::Vector<size_t> item) ->
                   std::vector<size_t> {
                 std::vector<size_t> v;
                 v.reserve(item.size());
                 std::copy(item.begin(), item.end(), std::back_inserter(v));
                 return v;
               });
           return new_lod;
#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("get_tensor",
           [](Variable &self) -> LoDTensor * {
             return self.GetMutable<LoDTensor>();
           },
           py::return_value_policy::reference)
      .def("get_net",
           [](Variable &self) -> operators::NetOp * {
             return self.GetMutable<operators::NetOp>();
           },
           py::return_value_policy::reference);

  py::class_<Scope>(m, "Scope", "")
      .def("new_var",
           [](Scope &self, const std::string &name) -> Variable * {
             return self.NewVar(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;

    OpInfoMap::Instance().IterAllInfo([&ret_values](const std::string &type,
                                                    const OpInfo &info) {
      if (!info.HasOpProtoAndChecker()) return;
      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_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::GPUPlace& place)
                      -> paddle::platform::DeviceContext* {
#ifdef PADDLE_ONLY_CPU
                    PADDLE_THROW("GPUPlace is not supported in CPU device.");
#else
                    return new paddle::platform::CUDADeviceContext(place);
#endif
                  });
  // clang-format on

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

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

  py::class_<OperatorBase>(m, "Operator")
      .def_static("create",
                  [](py::bytes protobin) {
                    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("backward",
           [](const OperatorBase &forwardOp,
              const std::unordered_set<std::string> &no_grad_vars) {
             return Backward(forwardOp, no_grad_vars).release();
           })
      .def("infer_shape", &OperatorBase::InferShape)
      .def("run", &OperatorBase::Run)
      .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_<operators::NetOp, OperatorBase>(m, "Net")
      .def_static("create",
                  []() -> operators::NetOp * {
                    auto *retv = new operators::NetOp;
                    retv->SetType("plain_net");
                    return retv;
                  })
      .def("append_op",
           [](operators::NetOp &self, const OperatorBase &op) {
             self.AppendOp(op);
           })
      .def("complete_add_op", &operators::NetOp::CompleteAddOp)
      .def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
        self->CompleteAddOp();
      });

  // recurrent_op
  py::class_<operators::RecurrentOp, OperatorBase>(m, "RecurrentOp")
      .def_static(
          "create",
          [](py::bytes protobin) -> operators::RecurrentOp * {
            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());
            auto rnn_op = OpRegistry::CreateOp(desc);
            return static_cast<operators::RecurrentOp *>(rnn_op.release());
          })
      .def("set_stepnet",
           [](operators::RecurrentOp &self, const operators::NetOp &net)
               -> void { self.set_stepnet(net.Clone()); });

  // cond_op
  py::class_<operators::CondOp, OperatorBase>(m, "CondOp")
      .def_static("create",
                  [](py::bytes protobin) -> operators::CondOp * {
                    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());
                    auto cond_op = OpRegistry::CreateOp(desc);
                    return static_cast<operators::CondOp *>(cond_op.release());
                  })
      .def("set_truenet",
           [](operators::CondOp &self, const operators::NetOp &net) -> void {
             self.set_truenet(net.Clone());
           })
      .def("set_falsenet",
           [](operators::CondOp &self, const operators::NetOp &net) -> void {
             self.set_falsenet(net.Clone());
           });

  m.def("unique_integer", UniqueIntegerGenerator);

  m.def("is_compile_gpu", IsCompileGPU);

  py::class_<ProgramDesc>(m, "ProgramDesc", "")
      .def_static("instance",
                  [] { return &GetProgramDesc(); },
                  py::return_value_policy::reference)
      .def_static("__create_program_desc__",
                  [] {
                    // Only used for unit-test
                    auto *prog_desc = new ProgramDesc;
                    auto *block = prog_desc->mutable_blocks()->Add();
                    block->set_idx(0);
                    block->set_parent_idx(-1);
                    return prog_desc;
                  })
      .def("append_block",
           [](ProgramDesc &self, BlockDesc &parent) {
             auto desc = self.add_blocks();
             desc->set_idx(self.mutable_blocks()->size() - 1);
             desc->set_parent_idx(parent.idx());
             return desc;
           },
           py::return_value_policy::reference)
      .def("root_block",
           [](ProgramDesc &self) { return self.mutable_blocks()->Mutable(0); },
           py::return_value_policy::reference)
      .def("__str__", [](ProgramDesc &self) { return self.DebugString(); });

  py::class_<BlockDesc>(m, "BlockDesc", "")
      .def("id", [](BlockDesc &self) { return self.idx(); })
      .def("parent", [](BlockDesc &self) { return self.parent_idx(); })
      .def("append_op",
           [](BlockDesc &self) { return self.add_ops(); },
           py::return_value_policy::reference)
      .def("new_var",
           [](BlockDesc &self) { return self.add_vars(); },
           py::return_value_policy::reference);

  py::class_<VarDesc>(m, "VarDesc", "")
      .def(py::init<>())
      .def("set_name",
           [](VarDesc &self, const std::string &name) { self.set_name(name); })
      .def("set_shape",
           [](VarDesc &self, const std::vector<int64_t> &dims) {
             LoDTensorDesc *lod_tensor_desc = self.mutable_lod_tensor();
             for (const int64_t &i : dims) {
               lod_tensor_desc->add_dims(i);
             }
           })
      .def("set_data_type",
           [](VarDesc &self, int type_id) {
             LoDTensorDesc *lod_tensor_desc = self.mutable_lod_tensor();
             lod_tensor_desc->set_data_type(static_cast<DataType>(type_id));
           })
      .def("shape", [](VarDesc &self) {
        const LoDTensorDesc &lod_tensor_desc = self.lod_tensor();
        int rank = lod_tensor_desc.dims_size();
        std::vector<int64_t> res(rank);
        for (int i = 0; i < rank; ++i) {
          res[i] = lod_tensor_desc.dims(i);
        }
        return res;
      });

  auto op_desc_set_var = [](OpDesc::Var *var,
                            const std::string &parameter,
                            const std::vector<std::string> &arguments) {
    var->set_parameter(parameter);
    VectorToRepeated(arguments, var->mutable_arguments());
  };

  auto op_desc_set_attr = [](OpDesc &desc, const std::string &name) {
    auto attr = desc.add_attrs();
    attr->set_name(name);
    return attr;
  };

  py::class_<OpDesc>(m, "OpDesc", "")
      .def("type", [](OpDesc &op) { return op.type(); })
      .def("set_input",
           [op_desc_set_var](OpDesc &self,
                             const std::string &parameter,
                             const std::vector<std::string> &arguments) {
             auto ipt = self.add_inputs();
             op_desc_set_var(ipt, parameter, arguments);
           })
      .def("input_names",
           [](OpDesc &self) {
             std::vector<std::string> ret_val;
             ret_val.reserve(static_cast<size_t>(self.inputs().size()));
             std::transform(
                 self.inputs().begin(),
                 self.inputs().end(),
                 std::back_inserter(ret_val),
                 [](const OpDesc::Var &var) { return var.parameter(); });
             return ret_val;
           })
      .def("__str__", [](OpDesc &self) { return self.DebugString(); })
      .def("set_output",
           [op_desc_set_var](OpDesc &self,
                             const std::string &parameter,
                             const std::vector<std::string> &arguments) {
             auto opt = self.add_outputs();
             op_desc_set_var(opt, parameter, arguments);
           })
      .def("set_attr",
           [op_desc_set_attr](OpDesc &self, const std::string &name, int i) {
             op_desc_set_attr(self, name)->set_i(i);
           });

  return m.ptr();
}
}  // namespace framework
}  // namespace paddle