imperative.cc

/* Copyright (c) 2018 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 "paddle/fluid/pybind/imperative.h"

#include <Python.h>
#include <pybind11/chrono.h>
#include <pybind11/complex.h>
#include <pybind11/functional.h>
#include <pybind11/stl.h>
#include <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/imperative/backward_strategy.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/imperative/nccl_context.h"
#include "paddle/fluid/imperative/profiler.h"
#include "paddle/fluid/imperative/tracer.h"
#include "paddle/fluid/imperative/type_defs.h"

#include "paddle/fluid/pybind/pybind_boost_headers.h"

namespace paddle {
namespace pybind {

namespace py = ::pybind11;

class Layer : public imperative::Layer {
 public:
  using imperative::Layer::Layer;  // Inherit constructors

  std::vector<std::shared_ptr<imperative::VarBase>> Forward(
      const std::vector<std::shared_ptr<imperative::VarBase>> &inputs)
      override {
    PYBIND11_OVERLOAD(std::vector<std::shared_ptr<imperative::VarBase>>, Layer,
                      Forward, inputs);  // NOLINT
  }
};

// warper for pyobject to avoid imperative module depend on python
// TODO(jiabin) Add OpBase's pybind interface back to enable backward hook
class PYBIND11_HIDDEN PyCallableObject {
 public:
  PyCallableObject(std::shared_ptr<py::object> py_obj_ptr)
      : py_obj_ptr_(std::move(py_obj_ptr)) {}
  ~PyCallableObject() {
    py::call_guard<py::gil_scoped_acquire>();
    py_obj_ptr_.reset();
  }
  void operator()() {
    py::call_guard<py::gil_scoped_acquire>();
    py_obj_ptr_->operator()(this);
  }

 private:
  std::shared_ptr<py::object> py_obj_ptr_;
};

// Function like obj.attr_name in Python.
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("Python object is not type of %s", typeid(T).name());
  }
}

// NOTE(zjl): py::handle is a very light wrapper of PyObject *.
// Unlike py::object, py::handle does not change reference count of PyObject *.
static std::vector<std::shared_ptr<imperative::VarBase>>
GetVarBaseListFromPyHandle(const py::handle &handle) {
  PyObject *py_obj = handle.ptr();  // get underlying PyObject
  // Python None is not nullptr in C++!
  if (!py_obj || py_obj == Py_None) {
    return {};
  }

  const char *kIVarField = "_ivar";
  PyObject *py_ivar = GetPythonAttribute(py_obj, kIVarField);
  std::vector<std::shared_ptr<imperative::VarBase>> result;

  if (py_ivar) {  // Variable
    result.emplace_back(
        PyObjectCast<std::shared_ptr<imperative::VarBase>>(py_ivar));
    Py_DECREF(py_ivar);
  } else if (PyList_Check(py_obj)) {  // List of Variable
    size_t len = PyList_GET_SIZE(py_obj);
    result.reserve(len);
    for (size_t i = 0; i < len; ++i) {
      PyObject *py_ivar =
          PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kIVarField);
      PADDLE_ENFORCE_NOT_NULL(py_ivar);
      result.emplace_back(
          PyObjectCast<std::shared_ptr<imperative::VarBase>>(py_ivar));
      Py_DECREF(py_ivar);
    }
  } else if (PyTuple_Check(py_obj)) {  // Tuple of Variable
    size_t len = PyTuple_GET_SIZE(py_obj);
    result.reserve(len);
    for (size_t i = 0; i < len; ++i) {
      PyObject *py_ivar =
          PyObject_GetAttrString(PyTuple_GET_ITEM(py_obj, i), kIVarField);
      PADDLE_ENFORCE_NOT_NULL(py_ivar);
      result.emplace_back(
          PyObjectCast<std::shared_ptr<imperative::VarBase>>(py_ivar));
      Py_DECREF(py_ivar);
    }
  } else {
    PADDLE_THROW(
        "unsupported type %s, must be Variable, list[Variable] or "
        "tuple[Variable]",
        py::str(handle));
  }

  return result;
}

using PyNameVarBaseMap = std::unordered_map<std::string, py::handle>;

static imperative::NameVarBaseMap ConvertToNameVarBaseMap(
    const PyNameVarBaseMap &map) {
  imperative::NameVarBaseMap result;
  for (auto &pair : map) {
    auto var_vec = GetVarBaseListFromPyHandle(pair.second);
    if (!var_vec.empty()) {
      result.emplace(pair.first, std::move(var_vec));
    }
  }

  PADDLE_ENFORCE_EQ(PyErr_Occurred() == nullptr, true,
                    py::str(py::handle(PyErr_Occurred())));
  return result;
}

static std::string GetTypeName(const imperative::VarBase &var) {
  if (var.Type() == framework::proto::VarType::RAW) {
    return "RAW";
  } else if (!var.Var().IsInitialized()) {
    return "nullptr";
  } else {
    return framework::ToTypeName(var.Var().Type());
  }
}

// Bind Methods
void BindImperative(py::module *m_ptr) {
  auto &m = *m_ptr;

  py::class_<imperative::detail::BackwardStrategy> backward_strategy(
      m, "BackwardStrategy", R"DOC(

    BackwardStrategy is a descriptor of how to run the backward process.

    **Note**:
        **This API is only avaliable in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **Mode**

    Attribute:
        **sort_sum_gradient**:

        If framework will sum the gradient by the reverse order of trace. eg. x_var ( :ref:`api_guide_Variable` ) will be the input of multiple OP such as :ref:`api_fluid_layers_scale` , this attr will decide if framework will sum gradient of `x_var` by the reverse order.

        By Default: False

        Examples:
            .. code-block:: python

                import numpy as np
                import paddle.fluid as fluid

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    x_var = fluid.dygraph.to_variable(x)
                    sums_inputs = []
                    # x_var will be multi-scales' input here
                    for _ in range(10):
                        sums_inputs.append(fluid.layers.scale(x_var))
                    ret2 = fluid.layers.sums(sums_inputs)
                    loss2 = fluid.layers.reduce_sum(ret2)
                    backward_strategy = fluid.dygraph.BackwardStrategy()
                    backward_strategy.sort_sum_gradient = True
                    loss2.backward(backward_strategy)
      )DOC");
  backward_strategy.def(py::init())
      .def_property("sort_sum_gradient",
                    [](const imperative::detail::BackwardStrategy &self) {
                      return self.sorted_sum_gradient_;
                    },
                    [](imperative::detail::BackwardStrategy &self,
                       bool sorted_sum_gradient) {
                      self.sorted_sum_gradient_ = sorted_sum_gradient;
                    });

  m.def("start_imperative_gperf_profiler",
        []() { imperative::StartProfile(); });

  m.def("stop_imperative_gperf_profiler", []() { imperative::StopProfile(); });

  m.def("_is_dygraph_debug_enabled",
        []() { return imperative::IsDebugEnabled(); });
  m.def("_dygraph_debug_level", []() { return imperative::GetDebugLevel(); });
  m.def("_switch_tracer",
        [](const std::shared_ptr<imperative::Tracer> &tracer) {
          imperative::SetCurrentTracer(tracer);
        });

  py::class_<imperative::VarBase, std::shared_ptr<imperative::VarBase>>(
      m, "VarBase",
      R"DOC()DOC")
      .def_static("_alive_vars", &imperative::VarBase::AliveVarNames)
      .def("__init__",
           [](imperative::VarBase &self, const std::string &name,
              framework::proto::VarType::Type type,
              framework::proto::VarType::Type dtype,
              const std::vector<int> &dims, bool persistable) {
             new (&self) imperative::VarBase(name);
             self.SetPersistable(persistable);
             self.SetType(type);
             self.SetDataType(dtype);
             if (type == framework::proto::VarType::LOD_TENSOR) {
               auto *tensor =
                   self.MutableVar()->GetMutable<framework::LoDTensor>();
               tensor->Resize(framework::make_ddim(dims));
             }
           })
      .def("_run_backward",
           [](imperative::VarBase &self,
              const imperative::detail::BackwardStrategy &bckst,
              const imperative::Tracer &tracer) {
             // TODO(jiabin): when we impl more backward execution we can select
             // them

             imperative::Engine *engine = tracer.GetDefaultEngine();
             VLOG(3) << "Start backward";
             engine->Init(&self, bckst);
             engine->Execute();
             VLOG(3) << "Finish backward";
           },
           py::call_guard<py::gil_scoped_release>())
      .def("_grad_name", &imperative::VarBase::GradVarName)
      .def("_grad_value",
           [](imperative::VarBase &self) {
             return self.MutableGradVar()->Get<framework::LoDTensor>();
           },
           py::return_value_policy::reference)
      .def("_clear_gradient", &imperative::VarBase::ClearGradient)
      .def("_grad_ivar",
           [](const imperative::VarBase &self) {
             auto &grad_var = self.GradVarBase();
             auto *tensor =
                 grad_var->MutableVar()->GetMutable<framework::LoDTensor>();
             if (grad_var && grad_var->Var().IsInitialized() &&
                 tensor->IsInitialized()) {
               return grad_var;
             } else {
               return std::shared_ptr<imperative::VarBase>(nullptr);
             }
           },
           py::return_value_policy::copy)
      .def("_copy_to",
           [](const imperative::VarBase &self, const platform::CPUPlace &place,
              bool blocking) { return self.NewVarBase(place, blocking); },
           py::return_value_policy::copy)
      .def("_copy_to",
           [](const imperative::VarBase &self, const platform::CUDAPlace &place,
              bool blocking) { return self.NewVarBase(place, blocking); },
           py::return_value_policy::copy)
      .def("value", [](imperative::VarBase &self) { return self.MutableVar(); },
           py::return_value_policy::reference)
      .def_property("name", &imperative::VarBase::Name,
                    &imperative::VarBase::SetName)
      .def_property_readonly(
          "shape",
          [](imperative::VarBase &self) {
            if (self.Var().IsType<framework::LoDTensor>()) {
              return framework::vectorize<int>(
                  self.Var().Get<framework::LoDTensor>().dims());
            } else {
              VLOG(2) << "It is meaningless to get shape of variable type "
                      << GetTypeName(self);
              return std::vector<int>();
            }
          })
      .def_property_readonly("type", &imperative::VarBase::Type)
      .def_property_readonly("dtype", &imperative::VarBase::DataType)
      .def_property("persistable", &imperative::VarBase::Persistable,
                    &imperative::VarBase::SetPersistable)
      .def_property("stop_gradient",
                    &imperative::VarBase::OverridedStopGradient,
                    &imperative::VarBase::SetOverridedStopGradient);

  py::class_<imperative::Layer, Layer /* <--- trampoline*/> layer(m, "Layer");
  layer.def(py::init<>())
      .def("forward",
           [](imperative::Layer &self,
              const std::vector<std::shared_ptr<imperative::VarBase>> &inputs) {
             return self.Forward(inputs);
           });

  py::class_<imperative::jit::ProgramDescTracer>(m, "ProgramDescTracer", "")
      .def("create_program_desc",
           &imperative::jit::ProgramDescTracer::CreateProgramDesc)
      .def("reset", &imperative::jit::ProgramDescTracer::Reset);

  py::class_<imperative::Tracer, std::shared_ptr<imperative::Tracer>>(
      m, "Tracer",
      R"DOC()DOC")
      .def("__init__",
           [](imperative::Tracer &self) { new (&self) imperative::Tracer(); })
      .def_property("_enable_program_desc_tracing",
                    &imperative::Tracer::IsProgramDescTracingEnabled,
                    &imperative::Tracer::SetEnableProgramDescTracing)
      .def_property("_train_mode", &imperative::Tracer::NoGrad,
                    &imperative::Tracer::SetNoGrad)
      .def_property(
          "_expected_place",
          [](const imperative::Tracer &self) -> py::object {
            return py::cast(self.ExpectedPlace());
          },
          [](imperative::Tracer &self, const py::object &obj) {
            if (py::isinstance<platform::CUDAPlace>(obj)) {
              auto p = obj.cast<platform::CUDAPlace *>();
              self.SetExpectedPlace<platform::CUDAPlace>(*p);
            } else if (py::isinstance<platform::CPUPlace>(obj)) {
              auto p = obj.cast<platform::CPUPlace *>();
              self.SetExpectedPlace<platform::CPUPlace>(*p);
            } else if (py::isinstance<platform::CUDAPinnedPlace>(obj)) {
              auto p = obj.cast<platform::CUDAPinnedPlace *>();
              self.SetExpectedPlace<platform::CUDAPinnedPlace>(*p);
            } else {
              PADDLE_THROW(
                  "Incompatible Place Type: supports CUDAPlace, CPUPlace, "
                  "CUDAPinnedPlace, "
                  "but got Unknown Type!");
            }
          })
      .def("_get_program_desc_tracer",
           &imperative::Tracer::GetProgramDescTracer,
           py::return_value_policy::reference)
      .def("trace",
           [](imperative::Tracer &self, const std::string &type,
              const PyNameVarBaseMap &ins, const PyNameVarBaseMap &outs,
              framework::AttributeMap attrs, const platform::CUDAPlace &place,
              bool trace_backward) {
             auto ins_map = ConvertToNameVarBaseMap(ins);
             auto outs_map = ConvertToNameVarBaseMap(outs);
             {
               py::gil_scoped_release release;
               self.TraceOp(type, std::move(ins_map), std::move(outs_map),
                            std::move(attrs), place, trace_backward);
             }
           })
      .def("trace",
           [](imperative::Tracer &self, const std::string &type,
              const PyNameVarBaseMap &ins, const PyNameVarBaseMap &outs,
              framework::AttributeMap attrs, const platform::CPUPlace &place,
              bool trace_backward) {
             auto ins_map = ConvertToNameVarBaseMap(ins);
             auto outs_map = ConvertToNameVarBaseMap(outs);
             {
               py::gil_scoped_release release;
               self.TraceOp(type, std::move(ins_map), std::move(outs_map),
                            std::move(attrs), place, trace_backward);
             }
           });

  // define parallel context
  py::class_<imperative::ParallelStrategy> parallel_strategy(
      m, "ParallelStrategy", "");
  parallel_strategy.def(py::init())
      .def_property(
          "nranks",
          [](const imperative::ParallelStrategy &self) { return self.nranks_; },
          [](imperative::ParallelStrategy &self, int nranks) {
            self.nranks_ = nranks;
          })
      .def_property("local_rank",
                    [](const imperative::ParallelStrategy &self) {
                      return self.local_rank_;
                    },
                    [](imperative::ParallelStrategy &self, int local_rank) {
                      self.local_rank_ = local_rank;
                    })
      .def_property(
          "trainer_endpoints",
          [](const imperative::ParallelStrategy &self) {
            return self.trainer_endpoints_;
          },
          [](imperative::ParallelStrategy &self, std::vector<std::string> eps) {
            self.trainer_endpoints_ = eps;
          })
      .def_property("current_endpoint",
                    [](const imperative::ParallelStrategy &self) {
                      return self.current_endpoint_;
                    },
                    [](imperative::ParallelStrategy &self,
                       const std::string &ep) { self.current_endpoint_ = ep; });
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
  py::class_<imperative::NCCLParallelContext> nccl_ctx(m,
                                                       "NCCLParallelContext");

  nccl_ctx
      .def(py::init<const imperative::ParallelStrategy &,
                    const platform::CUDAPlace &>())
      .def("init", [](imperative::NCCLParallelContext &self) { self.Init(); });
#endif
}

}  // namespace pybind
}  // namespace paddle