/* Copyright (c) 2022 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 <fcntl.h>
#ifdef _POSIX_C_SOURCE
#undef _POSIX_C_SOURCE
#endif

#ifdef _XOPEN_SOURCE
#undef _XOPEN_SOURCE
#endif

#include "paddle/fluid/distributed/collective/ProcessGroup.h"
#include "paddle/fluid/distributed/collective/Types.h"
#include "paddle/fluid/distributed/collective/reducer.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/pybind/distributed_py.h"
#include "paddle/fluid/pybind/eager_utils.h"
#include "paddle/phi/api/all.h"

#if defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/distributed/collective/ProcessGroupNCCL.h"
#endif

#if defined(PADDLE_WITH_ASCEND_CL)
#include "paddle/fluid/distributed/collective/ProcessGroupHCCL.h"
#endif

#if defined(PADDLE_WITH_GLOO)
#include "paddle/fluid/distributed/collective/ProcessGroupGloo.h"
#include "paddle/fluid/distributed/store/tcp_store.h"
#endif

namespace py = pybind11;

namespace paddle {
namespace pybind {

using Tensor = paddle::experimental::Tensor;

std::shared_ptr<distributed::EagerReducer> CreateEagerReducer(
    py::handle py_tensors,
    const std::vector<std::vector<size_t>> &group_indices,
    const std::vector<bool> &is_sparse_gradient,
    std::shared_ptr<distributed::ProcessGroup> process_group,
    const std::vector<size_t> &group_size_limits, bool find_unused_parameters) {
  auto params = CastPyArg2VectorOfTensor(py_tensors.ptr(), 0);
  return std::make_shared<distributed::EagerReducer>(
      params, group_indices, is_sparse_gradient, process_group,
      group_size_limits, find_unused_parameters);
}

#if defined(PADDLE_WITH_GLOO)
using ProcessGroupGloo = paddle::distributed::ProcessGroupGloo;
using GlooStore = paddle::distributed::ProcessGroupGloo::GlooStore;
using GlooOptions = paddle::distributed::ProcessGroupGloo::GlooOptions;
#endif

static std::string GLOO_SOCKET_IFNAME_ENV = "GLOO_SOCKET_IFNAME";  // NOLINT

void BindDistributed(py::module *m) {
  py::enum_<distributed::ReduceOp>(*m, "ReduceOp")
      .value("SUM", distributed::ReduceOp::SUM)
      .value("AVG", distributed::ReduceOp::AVG)
      .value("MAX", distributed::ReduceOp::MAX)
      .value("MIN", distributed::ReduceOp::MIN)
      .value("PRODUCT", distributed::ReduceOp::PRODUCT);

  py::class_<distributed::AllreduceOptions>(*m, "AllreduceOptions")
      .def(py::init<>())
      .def_readwrite("reduce_op", &distributed::AllreduceOptions::reduce_op);

  py::class_<distributed::BroadcastOptions>(*m, "BroadcastOptions")
      .def(py::init<>())
      .def_readwrite("source_rank", &distributed::BroadcastOptions::source_rank)
      .def_readwrite("source_root",
                     &distributed::BroadcastOptions::source_root);

  py::class_<distributed::BarrierOptions>(*m, "BarrierOptions")
      .def(py::init<>())
      .def_readwrite("place_ids", &distributed::BarrierOptions::place_ids);

  py::class_<distributed::ReduceOptions>(*m, "ReduceOptions")
      .def(py::init<>())
      .def_readwrite("reduce_op", &distributed::ReduceOptions::reduce_op)
      .def_readwrite("source_root", &distributed::ReduceOptions::root_rank);

  auto ProcessGroup =
      py::class_<distributed::ProcessGroup,
                 std::shared_ptr<distributed::ProcessGroup>>(*m, "ProcessGroup")
          .def("rank", &distributed::ProcessGroup::GetRank)
          .def("size", &distributed::ProcessGroup::GetSize)
          .def("name", &distributed::ProcessGroup::GetBackendName)
          .def("allreduce",
               [](distributed::ProcessGroup &self, py::handle py_tensor,
                  distributed::ReduceOp op) {
                 auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
                 distributed::AllreduceOptions opts;
                 opts.reduce_op = op;
                 std::vector<Tensor> tensors = {tensor};
                 return self.AllReduce(tensors, opts);
               },
               py::arg("tensor"), py::arg("op") = distributed::ReduceOp::SUM,
               py::call_guard<py::gil_scoped_release>())

          .def("broadcast",
               [](distributed::ProcessGroup &self, py::handle py_tensor,
                  int source_rank) {
                 auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
                 distributed::BroadcastOptions opts;
                 opts.source_rank = source_rank;
                 std::vector<Tensor> tensors = {tensor};
                 return self.Broadcast(tensors, opts);
               },
               py::arg("tensor"), py::arg("source_rank"),
               py::call_guard<py::gil_scoped_release>())

          .def("barrier",
               [](distributed::ProcessGroup &self, std::vector<int> place_ids) {
                 distributed::BarrierOptions opts;
                 opts.place_ids = place_ids;
                 return self.Barrier(opts);
               },
               py::arg("place_ids") = std::vector<int>{},
               py::call_guard<py::gil_scoped_release>())

          .def("send",
               [](distributed::ProcessGroup &self, py::handle py_tensor,
                  int dst) {
                 auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
                 std::vector<Tensor> tensors = {tensor};
                 return self.Send(tensors, dst);
               },
               py::arg("tensor"), py::arg("dst"),
               py::call_guard<py::gil_scoped_release>())

          .def("recv",
               [](distributed::ProcessGroup &self, py::handle py_tensor,
                  int src) {
                 auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
                 std::vector<Tensor> tensors = {tensor};
                 return self.Recv(tensors, src);
               },
               py::arg("tensor"), py::arg("src"),
               py::call_guard<py::gil_scoped_release>())

          .def("all_gather",
               [](distributed::ProcessGroup &self, py::handle py_in_tensor,
                  py::handle py_out_tensor) {
                 auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0);
                 auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0);
                 std::vector<Tensor> in_tensors = {in_tensor};
                 std::vector<Tensor> out_tensors = {out_tensor};
                 return self.AllGather(in_tensors, out_tensors);
               },
               py::arg("in"), py::arg("out"),
               py::call_guard<py::gil_scoped_release>())

          .def("alltoall",
               [](distributed::ProcessGroup &self, py::handle py_in_tensor,
                  py::handle py_out_tensor) {
                 auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0);
                 auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0);
                 std::vector<Tensor> in_tensors = {in_tensor};
                 std::vector<Tensor> out_tensors = {out_tensor};
                 return self.AllToAll(in_tensors, out_tensors);
               },
               py::arg("in"), py::arg("out"),
               py::call_guard<py::gil_scoped_release>())

          .def("reduce",
               [](distributed::ProcessGroup &self, py::handle py_in_tensor,
                  int dst, distributed::ReduceOp op) {
                 auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0);
                 distributed::ReduceOptions opts;
                 opts.reduce_op = op;
                 opts.root_rank = dst;
                 std::vector<Tensor> tensors = {in_tensor};
                 return self.Reduce(tensors, opts);
               },
               py::arg("tensor"), py::arg("dst"),
               py::arg("op") = distributed::ReduceOp::SUM,
               py::call_guard<py::gil_scoped_release>())

          .def("scatter",
               [](distributed::ProcessGroup &self, py::handle py_in_tensor,
                  py::handle py_out_tensor, int src) {
                 auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0);
                 auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0);
                 distributed::ScatterOptions opts;
                 opts.root_rank = src;
                 std::vector<Tensor> in_tensors = {in_tensor};
                 std::vector<Tensor> out_tensors = {out_tensor};
                 return self.Scatter(in_tensors, out_tensors, opts);
               },
               py::arg("in"), py::arg("out"), py::arg("src"),
               py::call_guard<py::gil_scoped_release>());

#if defined(PADDLE_WITH_NCCL)
  py::class_<distributed::ProcessGroupNCCL,
             std::shared_ptr<distributed::ProcessGroupNCCL>>(
      *m, "ProcessGroupNCCL", ProcessGroup)
      .def(py::init<const std::shared_ptr<distributed::Store> &, int, int>(),
           py::call_guard<py::gil_scoped_release>());
#endif

#if defined(PADDLE_WITH_ASCEND_CL)
  py::class_<distributed::ProcessGroupHCCL,
             std::shared_ptr<distributed::ProcessGroupHCCL>>(
      *m, "ProcessGroupHCCL", ProcessGroup)
      .def(py::init<const std::shared_ptr<distributed::Store> &, int, int>(),
           py::call_guard<py::gil_scoped_release>());
#endif

  py::class_<distributed::ProcessGroup::Task,
             std::shared_ptr<distributed::ProcessGroup::Task>>(*m, "task")
      .def("is_completed", &distributed::ProcessGroup::Task::IsCompleted)
      .def("wait", &distributed::ProcessGroup::Task::Wait,
           py::arg("timeout") = kWaitTimeout,
           py::call_guard<py::gil_scoped_release>())
      .def("synchronize", &distributed::ProcessGroup::Task::Synchronize,
           py::call_guard<py::gil_scoped_release>());

#if defined(PADDLE_WITH_GLOO)
  py::class_<GlooOptions>(*m, "GlooOptions")
      .def(py::init<>())
      .def_readwrite("_device", &GlooOptions::device)
      .def_static("create", &GlooOptions::create);

  py::class_<GlooStore, std::shared_ptr<GlooStore>>(*m, "GlooStore")
      .def(py::init(
               [](const std::shared_ptr<paddle::distributed::TCPStore> &store) {
                 return std::make_shared<GlooStore>(store);
               }),
           py::call_guard<py::gil_scoped_release>());

  py::class_<ProcessGroupGloo, std::shared_ptr<ProcessGroupGloo>>(
      *m, "ProcessGroupGloo", ProcessGroup)
      .def(py::init<const std::shared_ptr<GlooStore> &, int, int,
                    std::shared_ptr<GlooOptions> &>(),
           py::call_guard<py::gil_scoped_release>())
      .def(py::init([](const std::shared_ptr<GlooStore> &store, int rank,
                       int world_size) {
             auto opts = GlooOptions::create();
             char *ifname = getenv(GLOO_SOCKET_IFNAME_ENV.c_str());
             if (ifname && strlen(ifname) > 1) {
               opts->device = ProcessGroupGloo::createDeviceForInterface(
                   std::string(ifname));
             } else {
               opts->device = ProcessGroupGloo::createDefaultDevice();
             }
             return std::make_shared<ProcessGroupGloo>(store, rank, world_size,
                                                       opts);
           }),
           py::arg("store"), py::arg("rank"), py::arg("world_size"),
           py::call_guard<py::gil_scoped_release>())
      .def_static("create_default_device",
                  &ProcessGroupGloo::createDefaultDevice);
#endif

  m->def("eager_assign_group_by_size",
         [](py::handle py_tensors, std::vector<bool> is_sparse_gradient,
            std::vector<size_t> group_size_limits,
            std::vector<int64_t> tensor_indices) {
           auto tensors = CastPyArg2VectorOfTensor(py_tensors.ptr(), 0);
           return distributed::Eager_AssignGroupBySize(
               tensors, is_sparse_gradient, group_size_limits, tensor_indices);
         },
         py::arg("tensors"), py::arg("is_sparse_gradient"),
         py::arg("group_size_limits") = std::vector<size_t>{25 * 1024 * 1024},
         py::arg("tensor_indices") = std::vector<int64_t>{},
         py::call_guard<py::gil_scoped_release>());

  py::class_<distributed::EagerReducer,
             std::shared_ptr<distributed::EagerReducer>>(*m, "EagerReducer",
                                                         R"DOC()DOC")
      .def(py::init(&CreateEagerReducer))
      .def("prepare_for_backward",
           [](distributed::EagerReducer &self, py::handle py_tensors) {
             auto params = CastPyArg2VectorOfTensor(py_tensors.ptr(), 0);
             self.PrepareForBackward(params);
           },
           py::arg("tensors"), py::call_guard<py::gil_scoped_release>());
}

}  // end namespace pybind
}  // namespace paddle