add GeneralRoleMaker (#22295)

* add GeneralRoleMaker which is for general usage
* test=develop

Dockerfile

@@ -219,6 +219,13 @@ RUN wget -q https://launchpad.net/ubuntu/+archive/primary/+sourcefiles/binutils/
     cd binutils-2.27 && \
     ./configure && make -j && make install && cd .. && rm -rf binutils-2.27 binutils_2.27.orig.tar.gz
 
+RUN wget --no-check-certificate https://pslib.bj.bcebos.com/openmpi-1.4.5.tar.gz && tar -xzf openmpi-1.4.5.tar.gz && \
+    cd openmpi-1.4.5 && ./configure --prefix=/usr/local && make all -j8 && make install -j8 && \
+    export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH && export PATH=/usr/local/bin:$PATH && cd .. && \
+    rm -rf openmpi-1.4.5.tar.gz && pip --no-cache-dir install mpi4py && ln -fs /bin/bash /bin/sh && \
+    apt-get install libprotobuf-dev -y
+RUN pip --no-cache-dir install -U netifaces==0.10.9
+
 # Older versions of patchelf limited the size of the files being processed and were fixed in this pr.
 # https://github.com/NixOS/patchelf/commit/ba2695a8110abbc8cc6baf0eea819922ee5007fa
 # So install a newer version here.

paddle/fluid/framework/CMakeLists.txt

@@ -214,6 +214,7 @@ cc_library(parallel_executor SRCS parallel_executor.cc DEPS
         graph build_strategy
         fast_threaded_ssa_graph_executor variable_helper)
 
+cc_test(dist_multi_trainer_test SRCS dist_multi_trainer_test.cc DEPS executor)
 cc_library(prune SRCS prune.cc DEPS framework_proto boost)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
 cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry

paddle/fluid/framework/data_set.cc

@@ -287,6 +287,7 @@ void DatasetImpl<T>::LocalShuffle() {
 
 template <typename T>
 void DatasetImpl<T>::GlobalShuffle(int thread_num) {
+#ifdef PADDLE_WITH_PSLIB
   VLOG(3) << "DatasetImpl<T>::GlobalShuffle() begin";
   platform::Timer timeline;
   timeline.Start();
@@ -379,6 +380,7 @@ void DatasetImpl<T>::GlobalShuffle(int thread_num) {
   timeline.Pause();
   VLOG(3) << "DatasetImpl<T>::GlobalShuffle() end, cost time="
           << timeline.ElapsedSec() << " seconds";
+#endif
 }
 
 template <typename T>

paddle/fluid/framework/dist_multi_trainer.cc

@@ -41,8 +41,8 @@ void DistMultiTrainer::Initialize(const TrainerDesc &trainer_desc,
       need_dump_field_ = false;
     }
   }
-  mpi_rank_ = trainer_desc.mpi_rank() / 2;
-  mpi_size_ = trainer_desc.mpi_size() / 2;
+  mpi_rank_ = trainer_desc.mpi_rank();
+  mpi_size_ = trainer_desc.mpi_size();
   dump_file_num_ = trainer_desc.dump_file_num();
   const std::vector<paddle::framework::DataFeed *> readers =
       dataset->GetReaders();

paddle/fluid/framework/dist_multi_trainer_test.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 <fstream>
+#include <iostream>
+#include <sstream>
+#include "google/protobuf/io/zero_copy_stream_impl.h"
+#include "google/protobuf/message.h"
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/trainer.h"
+
+#if defined _WIN32 || defined __APPLE__
+#else
+#define _LINUX
+#endif
+
+namespace paddle {
+namespace framework {
+TEST(DisMultiTrainerTest, test1) {
+#ifdef _LINUX
+  std::shared_ptr<DistMultiTrainer> tmp1 = std::make_shared<DistMultiTrainer>();
+  TrainerDesc t;
+  t.set_class_name("DistMultiTrainer");
+  t.set_device_worker_name("DownpourWorker");
+  t.set_thread_num(1);
+  auto* m = t.mutable_downpour_param()->add_program_config();
+  m->set_program_id("123");
+  std::string str;
+  str += "name: \"MultiSlotDataFeed\"\nbatch_size: 2\nmulti_slot_desc {\n";
+  str += "slots {\nname: \"words\"\ntype: \"uint64\"\nis_dense: false\n";
+  str += "is_used: true\n}\nslots {\nname: \"label\"\ntype: \"uint64\"\n";
+  str += "is_dense: false\nis_used: true\n}\n}\n";
+  std::shared_ptr<MultiSlotDataset> dataset =
+      std::make_shared<MultiSlotDataset>();
+  dataset->SetFileList(std::vector<std::string>());
+  dataset->SetThreadNum(1);
+  dataset->SetTrainerNum(1);
+  dataset->SetDataFeedDesc(str);
+  dataset->CreateReaders();
+  tmp1->Initialize(t, dataset.get());
+#endif
+}
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/fleet/gloo_wrapper.cc

@@ -21,6 +21,7 @@ HdfsStore::HdfsStore(const std::string& path) {
   path_ = path;
   wait_sleep_ms_ = 3000;
   wait_timeout_ = std::chrono::seconds(999999999);
+  retry_times_ = 100;
 }
 
 void HdfsStore::set(const std::string& key, const std::vector<char>& data) {
@@ -33,10 +34,27 @@ void HdfsStore::set(const std::string& key, const std::vector<char>& data) {
     paddle::framework::fs_remove(path);
   }
   int err_no = 0;
-  std::shared_ptr<FILE> fp = paddle::framework::fs_open_write(tmp, &err_no, "");
+  for (int i = 1; i <= retry_times_; ++i) {
+    std::shared_ptr<FILE> fp =
+        paddle::framework::fs_open_write(tmp, &err_no, "");
+    if (err_no != 0) {
+      VLOG(0) << "fs_open_write failed, retry times " << i << " err no "
+              << err_no;
+      fp.reset();
+      sleep(wait_sleep_ms_ / 1000);
+      continue;
+    }
     size_t write_count = fwrite_unlocked(data.data(), 1, data.size(), fp.get());
-  VLOG(3) << "HdfsStore::set write_count=" << write_count << " key " << key;
+    if (write_count != data.size()) {
+      VLOG(0) << "fwrite_unlocked failed, retry times " << i << " write_count "
+              << write_count << " data.size() " << data.size();
+      fp.reset();
+      sleep(2);
+      continue;
+    }
     fp.reset();
+    break;
+  }
   paddle::framework::fs_mv(tmp, path);
 #endif
 }
@@ -131,7 +149,7 @@ void GlooWrapper::Init(int rank, int size, const std::string& path,
   }
   rank_ = rank;
   size_ = size;
-  std::string cmd = std::string("hadoop fs");
+  std::string cmd = std::string("${HADOOP_HOME}/bin/hadoop fs");
   cmd += " -D fs.default.name=" + fs_name;
   cmd += " -D hadoop.job.ugi=" + fs_ugi;
   paddle::framework::hdfs_set_command(cmd);
@@ -149,16 +167,19 @@ void GlooWrapper::Init(int rank, int size, const std::string& path,
   is_initialized_ = true;
 }
 
-template void GlooWrapper::AllReduce<int64_t>(
+template std::vector<int64_t> GlooWrapper::AllReduce<int64_t>(
     std::vector<int64_t>& sendbuf,  // NOLINT
-    std::vector<int64_t>& recvbuf,  // NOLINT
     const std::string& mode);
-template void GlooWrapper::AllReduce<double>(
+template std::vector<double> GlooWrapper::AllReduce<double>(
     std::vector<double>& sendbuf,  // NOLINT
-    std::vector<double>& recvbuf,  // NOLINT
+    const std::string& mode);
+template std::vector<uint64_t> GlooWrapper::AllReduce<uint64_t>(
+    std::vector<uint64_t>& sendbuf,  // NOLINT
     const std::string& mode);
 template std::vector<int64_t> GlooWrapper::AllGather<int64_t>(
     int64_t& input);  // NOLINT
+template std::vector<uint64_t> GlooWrapper::AllGather<uint64_t>(
+    uint64_t& input);  // NOLINT
 template std::vector<double> GlooWrapper::AllGather<double>(
     double& input);  // NOLINT
 

paddle/fluid/framework/fleet/gloo_wrapper.h

@@ -70,6 +70,7 @@ class HdfsStore {
   std::string path_;
   int wait_sleep_ms_;
   std::chrono::seconds wait_timeout_;
+  int retry_times_;
 };
 
 }  // namespace rendezvous
@@ -107,9 +108,10 @@ class GlooWrapper {
   }
 
   template <typename T>
-  void AllReduce(std::vector<T>& sendbuf, std::vector<T>& recvbuf,  // NOLINT
-                 const std::string& mode = "sum") {
+  std::vector<T> AllReduce(std::vector<T>& sendbuf,            // NOLINT
+                           const std::string& mode = "sum") {  // NOLINT
     CHECK_EQ(is_initialized_, true);
+    std::vector<T> recvbuf(sendbuf.size(), T());
     CHECK_EQ(sendbuf.size() == recvbuf.size(), true);
 #ifdef PADDLE_WITH_GLOO
     gloo::AllreduceOptions opts(context_);
@@ -133,6 +135,7 @@ class GlooWrapper {
     }
     gloo::allreduce(opts);
 #endif
+    return recvbuf;
   }
 
   template <typename T>

paddle/fluid/framework/fleet/test_fleet.cc

@@ -49,8 +49,7 @@ TEST(TEST_GLOO, store_1) {
   gw.Size();
   gw.Barrier();
   std::vector<double> input;
-  std::vector<double> output;
-  gw.AllReduce(input, output);
+  gw.AllReduce(input);
   int64_t t;
   gw.AllGather(t);
 #endif

paddle/fluid/pybind/gloo_wrapper_py.cc

@@ -37,12 +37,12 @@ void BindGlooWrapper(py::module* m) {
       .def("rank", &framework::GlooWrapper::Rank)
       .def("size", &framework::GlooWrapper::Size)
       .def("barrier", &framework::GlooWrapper::Barrier)
+      .def("all_reduce", &framework::GlooWrapper::AllReduce<uint64_t>)
       .def("all_reduce", &framework::GlooWrapper::AllReduce<int64_t>)
       .def("all_reduce", &framework::GlooWrapper::AllReduce<double>)
+      .def("all_gather", &framework::GlooWrapper::AllGather<uint64_t>)
       .def("all_gather", &framework::GlooWrapper::AllGather<int64_t>)
-      .def("all_gather", &framework::GlooWrapper::AllGather<double>)
-      .def("Allreduce", &framework::GlooWrapper::AllReduce<int64_t>)
-      .def("Allreduce", &framework::GlooWrapper::AllReduce<double>);
+      .def("all_gather", &framework::GlooWrapper::AllGather<double>);
 }  // end BindGlooWrapper
 }  // end namespace pybind
 }  // end namespace paddle

python/paddle/fluid/dataset.py

@@ -526,7 +526,7 @@ class InMemoryDataset(DatasetBase):
         """
         trainer_num = 1
         if fleet is not None:
-            fleet._role_maker._barrier_worker()
+            fleet._role_maker.barrier_worker()
             trainer_num = fleet.worker_num()
         if self.fleet_send_batch_size is None:
             self.fleet_send_batch_size = 1024
@@ -537,14 +537,14 @@ class InMemoryDataset(DatasetBase):
         self.dataset.set_fleet_send_batch_size(self.fleet_send_batch_size)
         self.dataset.set_fleet_send_sleep_seconds(self.fleet_send_sleep_seconds)
         if fleet is not None:
-            fleet._role_maker._barrier_worker()
+            fleet._role_maker.barrier_worker()
         self.dataset.global_shuffle(thread_num)
         if fleet is not None:
-            fleet._role_maker._barrier_worker()
+            fleet._role_maker.barrier_worker()
         if self.merge_by_lineid:
             self.dataset.merge_by_lineid()
         if fleet is not None:
-            fleet._role_maker._barrier_worker()
+            fleet._role_maker.barrier_worker()
 
     def release_memory(self):
         """
@@ -599,7 +599,7 @@ class InMemoryDataset(DatasetBase):
         local_data_size = np.array([local_data_size])
         if fleet is not None:
             global_data_size = local_data_size * 0
-            fleet._role_maker._node_type_comm.Allreduce(local_data_size,
+            fleet._role_maker.all_reduce_worker(local_data_size,
                                                 global_data_size)
             return global_data_size[0]
         return local_data_size[0]
@@ -637,7 +637,7 @@ class InMemoryDataset(DatasetBase):
         local_data_size = np.array([local_data_size])
         if fleet is not None:
             global_data_size = local_data_size * 0
-            fleet._role_maker._node_type_comm.Allreduce(local_data_size,
+            fleet._role_maker.all_reduce_worker(local_data_size,
                                                 global_data_size)
             return global_data_size[0]
         return local_data_size[0]

python/paddle/fluid/incubate/fleet/base/fleet_base.py

@@ -202,6 +202,22 @@ class Fleet(object):
         self._role_maker.generate_role()
         self._is_initialized = True
 
+    def all_reduce_worker(self, input, output):
+        """
+        all reduce between workers, only support array of one dim.
+
+        Args:
+            input(list|numpy.array): array of one dim
+            output(list|numpy.array): array of one dim
+        """
+        self._role_maker.all_reduce_worker(input, output)
+
+    def barrier_worker(self):
+        """
+        barrier between workers
+        """
+        self._role_maker.barrier_worker()
+
     @abc.abstractmethod
     def init_worker(self):
         pass

python/paddle/fluid/incubate/fleet/base/role_maker.py

@@ -11,16 +11,18 @@
 # 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.
+"""Defination of Role Makers."""
 
 from __future__ import print_function
+import paddle.fluid as fluid
+import os
+import time
 
 __all__ = [
     'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker',
-    'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker'
+    'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker', 'GeneralRoleMaker'
 ]
 
-import os
-
 
 class Role:
     WORKER = 1
@@ -107,6 +109,43 @@ class RoleMakerBase(object):
             self._role, self._current_id, self._worker_endpoints,
             self._server_endpoints)
 
+    def all_gather(self, input):
+        """
+        all gather between trainers and pservers
+
+        Args:
+            input(int|float): input value
+
+        Returns:
+            return a list of values
+        """
+        print("warning: RoleMakerBase does not have all gather.")
+        return None
+
+    def all_reduce_worker(self, input, output, mode="sum"):
+        """
+        all reduce between trainers if current role is TRAINER,
+        only support array of one dim.
+
+        Args:
+            input(list/numpy.array): array of one dim
+            output(list/numpy.array): array of one dim
+            mode(str): "sum" or "min" or "max"
+        """
+        print("warning: RoleMakerBase does not have all reduce worker.")
+
+    def barrier_worker(self):
+        """
+        barrier between trainers if current role is TRAINER
+        """
+        print("warning: RoleMakerBase does not have barrier worker.")
+
+    def barrier_all(self):
+        """
+        barrier between trainers if current role is PSERVER
+        """
+        print("warning: RoleMakerBase does not have barrier all.")
+
 
 class MPIRoleMaker(RoleMakerBase):
     """
@@ -115,6 +154,7 @@ class MPIRoleMaker(RoleMakerBase):
     """
 
     def __init__(self):
+        """Init."""
         super(MPIRoleMaker, self).__init__()
         from mpi4py import MPI
         self.MPI = MPI
@@ -124,16 +164,12 @@ class MPIRoleMaker(RoleMakerBase):
         self._ip = None
 
     def _get_rank(self):
-        """
-        return rank
-        """
+        """Return rank."""
         self._rank = self._comm.Get_rank()
         return self._rank
 
     def _get_size(self):
-        """
-        return size
-        """
+        """Return size."""
         self._size = self._comm.Get_size()
         return self._size
 
@@ -174,9 +210,7 @@ class MPIRoleMaker(RoleMakerBase):
         return self._ips
 
     def get_local_ip(self):
-        """
-        return get local ip
-        """
+        """Return get local ip."""
         import socket
         self._ip = socket.gethostbyname(socket.gethostname())
         return self._ip
@@ -196,16 +230,68 @@ class MPISymetricRoleMaker(MPIRoleMaker):
     """
 
     def __init__(self):
+        """Init."""
         super(MPISymetricRoleMaker, self).__init__()
         self._node_type = None
         self._proc_per_node = 2
         self._pserver_rand_port = 0
 
     def _check_role_generation(self):
+        """Check whether role has been generated."""
         if not self._role_is_generated:
             raise NameError("generate_role() should be called first")
         return True
 
+    def all_gather(self, input):
+        """
+        all gather between trainers and pservers
+
+        Args:
+            input(int|float): input value
+
+        Returns:
+            return a list of values
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._all_gather(input)
+
+    def all_reduce_worker(self, input, output, mode="sum"):
+        """
+        all reduce between trainers if current role is TRAINER,
+        only support array of one dim.
+
+        Args:
+            input(list/numpy.array): array of one dim
+            output(list/numpy.array): array of one dim
+            mode(str): "sum" or "min" or "max"
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        if not self.is_worker():
+            print("warning: current role is not worker in all_reduce_worker")
+            return
+        self._all_reduce(input, output, mode)
+
+    def barrier_worker(self):
+        """
+        barrier between trainers if current role is TRAINER
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        if self.is_worker():
+            self._node_type_comm.barrier()
+        else:
+            print("warning: current role is not worker in barrier_worker")
+
+    def barrier_all(self):
+        """
+        barrier between trainers if current role is PSERVER
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        self._comm.barrier()
+
     def is_first_worker(self):
         """
         return whether current process is the first worker assigned by role maker
@@ -215,6 +301,12 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return False
 
     def get_pserver_endpoints(self):
+        """
+        get pserver endpoints
+        
+        Returns:
+            endpoints(list): pserver endpoints
+        """
         if self._pserver_rand_port <= 0:
             import random
             random.seed(self._server_num())
@@ -285,6 +377,28 @@ class MPISymetricRoleMaker(MPIRoleMaker):
             self.generate_role()
             return self._get_size() / self._proc_per_node
 
+    def _all_reduce(self, input, output, mode="sum"):
+        """
+        all reduce between trainers if current role is TRAINER,
+        only support array of one dim.
+
+        Args:
+            input(list/numpy.array): array of one dim
+            output(list/numpy.array): array of one dim
+            mode(str): "sum" or "min" or "max"
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        if mode == "sum":
+            mode = self.MPI.SUM
+        elif mode == "max":
+            mode = self.MPI.MAX
+        elif mode == "min":
+            mode = self.MPI.MIN
+        else:
+            raise ValueError("unknown mode: %s" % mode)
+        self._node_type_comm.Allreduce(input, output, op=mode)
+
     def _barrier_worker(self):
         """
         barrier all workers in current distributed job
@@ -325,12 +439,18 @@ class MPISymetricRoleMaker(MPIRoleMaker):
 
 
 class PaddleCloudRoleMaker(RoleMakerBase):
+    """
+    role maker for paddle cloud,
+    base class is RoleMakerBase
+    """
+
     def __init__(self, is_collective=False):
         super(PaddleCloudRoleMaker, self).__init__()
         self._role_is_generated = False
         self._is_collective = is_collective
 
     def generate_role(self):
+        """Generate role."""
         if not self._role_is_generated:
             if not self._is_collective:
                 try:
@@ -419,17 +539,352 @@ class PaddleCloudRoleMaker(RoleMakerBase):
         return self._trainers_num
 
 
+class GeneralRoleMaker(RoleMakerBase):
+    """
+    This role maker is for general use, you can set os.environ to customize:
+        PADDLE_PSERVERS_IP_PORT_LIST : all pservers' ip:port, seperated by ','
+        PADDLE_TRAINER_ENDPOINTS     : all trainers' ip:port, seperated by ','
+        TRAINING_ROLE                : TRAINER or PSERVER
+        PADDLE_TRAINER_ID            : current trainer id (only for trainer),
+                                       it is index in PADDLE_TRAINER_ENDPOINTS
+        PADDLE_PSERVER_ID            : current pserver id (only for pserver)
+                                       it is index in PADDLE_PSERVERS_IP_PORT_LIST
+    """
+
+    def __init__(self, **kwargs):
+        super(RoleMakerBase, self).__init__()
+        self._role_is_generated = False
+        self._hdfs_name = kwargs.get("hdfs_name", "")
+        self._hdfs_ugi = kwargs.get("hdfs_ugi", "")
+        self._hdfs_path = kwargs.get("path", "")
+        self._iface = self.__get_default_iface()
+        # this environment variable can be empty
+        self._prefix = os.getenv("SYS_JOB_ID", "")
+
+    def generate_role(self):
+        """
+        generate role for general role maker
+        """
+        if not self._role_is_generated:
+            eplist = os.environ["PADDLE_PSERVERS_IP_PORT_LIST"].split(",")
+            training_role = os.environ["TRAINING_ROLE"]
+            worker_endpoints = os.environ["PADDLE_TRAINER_ENDPOINTS"].split(",")
+            trainers_num = len(worker_endpoints)
+            if training_role not in ["TRAINER", "PSERVER"]:
+                raise ValueError("TRAINING_ROLE must be PSERVER or TRAINER")
+            if training_role == "TRAINER":
+                role = Role.WORKER
+                current_id = int(os.environ["PADDLE_TRAINER_ID"])
+                self._node_type = 1
+                self._cur_endpoint = worker_endpoints[current_id]
+                gloo = fluid.core.Gloo()
+                gloo.init(current_id,
+                          len(worker_endpoints),
+                          self._hdfs_path.rstrip("/") + "/trainer",
+                          self._hdfs_name, self._hdfs_ugi, self._iface,
+                          self._prefix)
+                self._node_type_comm = gloo
+            elif training_role == "PSERVER":
+                role = Role.SERVER
+                if os.environ.get("PADDLE_PSERVER_ID") is not None:
+                    current_id = int(os.environ["PADDLE_PSERVER_ID"])
+                    cur_endpoint = eplist[current_id]
+                else:
+                    # this is for compatible with paddlecloud
+                    cur_ip = os.environ["POD_IP"]
+                    cur_port = os.environ["PADDLE_PORT"]
+                    cur_endpoint = ":".join([cur_ip, cur_port])
+                    current_id = eplist.index(cur_endpoint)
+                self._node_type = 0
+                self._cur_endpoint = cur_endpoint
+                gloo = fluid.core.Gloo()
+                gloo.init(current_id,
+                          len(eplist),
+                          self._hdfs_path.rstrip("/") + "/pserver",
+                          self._hdfs_name, self._hdfs_ugi, self._iface,
+                          self._prefix)
+                self._node_type_comm = gloo
+
+            gloo = fluid.core.Gloo()
+            all_list = worker_endpoints + eplist
+            gloo.init(
+                all_list.index(self._cur_endpoint),
+                len(all_list),
+                self._hdfs_path.rstrip("/") + "/all", self._hdfs_name,
+                self._hdfs_ugi, self._iface, self._prefix)
+            self._all_comm = gloo
+            self._trainers_num = trainers_num
+            self._server_endpoints = eplist
+            self._role = role
+            self._current_id = current_id
+            self._rank = all_list.index(self._cur_endpoint)
+            self._size = len(all_list)
+            self._worker_endpoints = worker_endpoints
+            self._role_is_generated = True
+
+    def all_gather(self, input):
+        """
+        all gather between trainers and pservers
+
+        Args:
+            input(int|float): input value
+
+        Returns:
+            return a list of values
+        """
+        return self._all_gather(input)
+
+    def all_reduce_worker(self, input, output, mode="sum"):
+        """
+        all reduce between trainers if current role is TRAINER,
+        only support array of one dim.
+
+        Args:
+            input(list/numpy.array): array of one dim
+            output(list/numpy.array): array of one dim
+            mode(str): "sum" or "min" or "max"
+        """
+        if not self.is_worker():
+            return
+        self._all_reduce(input, output, mode)
+
+    def barrier_worker(self):
+        """
+        barrier between trainers if current role is TRAINER
+        """
+        self._barrier_worker()
+
+    def barrier_all(self):
+        """
+        barrier between trainers if current role is PSERVER
+        """
+        self._barrier_all()
+
+    def get_local_endpoint(self):
+        """
+        get local endpoint of current process
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._cur_endpoint
+
+    def get_trainer_endpoints(self):
+        """
+        get endpoint of all trainers
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._worker_endpoints
+
+    def get_pserver_endpoints(self):
+        """
+        get endpoint of all pservers
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._server_endpoints
+
+    def is_worker(self):
+        """
+        whether current process is worker
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._role == Role.WORKER
+
+    def is_server(self):
+        """
+        whether current process is server
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._role == Role.SERVER
+
+    def is_first_worker(self):
+        """
+        whether current process is worker of rank 0
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._role == Role.WORKER and self._current_id == 0
+
+    def worker_index(self):
+        """
+        get index of current worker
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._current_id
+
+    def server_index(self):
+        """
+        get index of current server
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._current_id
+
+    def worker_num(self):
+        """
+        retrun the current number of worker
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._worker_num()
+
+    def server_num(self):
+        """
+        return the current number of server
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._server_num()
+
+    def _barrier_worker(self):
+        """
+        barrier all workers in current distributed job
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        if self.is_worker():
+            self._node_type_comm.barrier()
+
+    def _barrier_all(self):
+        """
+        barrier all workers and servers in current distributed job
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        self._all_comm.barrier()
+
+    def _barrier_server(self):
+        """
+        barrier all servers in current distributed job
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        if self.is_server():
+            self._node_type_comm.barrier()
+
+    def _worker_num(self):
+        """
+        return the current number of worker
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._trainers_num
+
+    def _server_num(self):
+        """
+        return the current number of server
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return len(self._server_endpoints)
+
+    def _finalize(self):
+        """Default do nothing."""
+        pass
+
+    def _all_reduce(self, input, output, mode="sum"):
+        """
+        all reduce between all workers
+
+        Args:
+            input(list|numpy.array): array of one dim
+            output(list|numpy.array): array of one dim
+            mode(str): "sum" or "min" or "max"
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        input_list = [i for i in input]
+        ans = self._node_type_comm.all_reduce(input_list, mode)
+        for i in range(len(ans)):
+            output[i] = ans[i]
+
+    def _all_gather(self, obj):
+        """
+        gather between all workers and pservers
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        self._barrier_all()
+        return self._all_comm.all_gather(obj)
+
+    def _worker_gather(self, obj):
+        """
+        gather between all workers
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        if not self.is_worker():
+            return None
+        self._barrier_worker()
+        return self._node_type_comm.all_gather(obj)
+
+    def _get_rank(self):
+        """
+        get current rank in all workers and pservers
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._rank
+
+    def _get_size(self):
+        """
+        get total num of all workers and pservers
+        """
+        if not self._role_is_generated:
+            self.generate_role()
+        return self._size
+
+    def __get_default_iface(self):
+        """
+        get default physical interface
+        """
+        default1 = self.__get_default_iface_from_gateway()
+        default2 = self.__get_default_iface_from_interfaces()
+        return default2 if default1 == "lo" else default1
+
+    def __get_default_iface_from_gateway(self):
+        """
+        get default physical interface
+        """
+        import netifaces
+        gateways = netifaces.gateways()
+        if gateways.get(netifaces.AF_INET) != None:
+            gateway = gateways[netifaces.AF_INET]
+            if len(gateway) > 0 and len(gateway[0]) > 1:
+                return gateway[0][1]
+        return "lo"
+
+    def __get_default_iface_from_interfaces(self):
+        """
+        get default physical interface
+        """
+        import netifaces
+        for intf_name in netifaces.interfaces():
+            addresses = netifaces.ifaddresses(intf_name)
+            if netifaces.AF_INET in addresses:
+                ipv4_addresses = addresses[netifaces.AF_INET]
+                for ipv4_address in ipv4_addresses:
+                    if 'broadcast' in ipv4_address:
+                        return intf_name
+        return "lo"
+
+
 class UserDefinedRoleMaker(RoleMakerBase):
-    def __init__(self,
-                 current_id=0,
-                 role=Role.WORKER,
-                 worker_num=0,
-                 server_endpoints=None):
     """
     UserDefinedRoleMaker is designed for worker and server assignment
     under manual. Typically, a worker and a server node will be appointed
     on each physical node, It can be assign by user.
     """
+
+    def __init__(self,
+                 current_id=0,
+                 role=Role.WORKER,
+                 worker_num=0,
+                 server_endpoints=None):
         super(UserDefinedRoleMaker, self).__init__()
 
         if not isinstance(server_endpoints, list):
@@ -495,11 +950,12 @@ class UserDefinedRoleMaker(RoleMakerBase):
 
 
 class UserDefinedCollectiveRoleMaker(RoleMakerBase):
-    def __init__(self, current_id=0, worker_endpoints=None):
     """
     UserDefinedCollectiveRoleMaker is designed for worker assignment
     under manual for collective mode.
     """
+
+    def __init__(self, current_id=0, worker_endpoints=None):
         super(UserDefinedCollectiveRoleMaker, self).__init__()
 
         if not isinstance(worker_endpoints, list):

python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py

@@ -40,7 +40,9 @@ class PSLib(Fleet):
         self._client2client_max_retry = 3
 
     def init(self, role_maker=None):
-        super(PSLib, self).init(MPISymetricRoleMaker())
+        if role_maker is None:
+            role_maker = MPISymetricRoleMaker()
+        super(PSLib, self).init(role_maker)
         self._fleet_ptr = fluid.core.Fleet()
 
     def _set_client_communication_config(self, request_timeout_ms,
@@ -75,9 +77,10 @@ class PSLib(Fleet):
             # barrier_all for init_server, wait for server starts
             self._role_maker._barrier_all()
             self.all_ips_ = self._role_maker._all_gather(self._local_ip)
+            # worker_index * 2 is for compatible with older versions of pslib
             self._fleet_ptr.init_worker(self._dist_desc_str, self.all_ips_,
                                         self._role_maker._get_size(),
-                                        self._role_maker._get_rank())
+                                        self._role_maker.worker_index() * 2)
             # barrier_all for init_worker
             self._role_maker._barrier_all()
             # prepare for client to client communication
@@ -160,9 +163,16 @@ class PSLib(Fleet):
             else:
                 raise Exception(
                     "You should run DistributedOptimizer.minimize() first")
+            # server_index * 2 is for compatible with older versions of pslib
             self._fleet_ptr.init_server(self._dist_desc_str,
-                                        self._role_maker._get_rank())
+                                        self._role_maker.server_index() * 2)
+            if isinstance(self._role_maker, MPISymetricRoleMaker):
                 self._local_ip = self._fleet_ptr.run_server()
+            else:
+                local_endpoint = self._role_maker.get_local_endpoint()
+                local_endpoint = local_endpoint.split(":")
+                self._local_ip = self._fleet_ptr.run_server(
+                    str(local_endpoint[0]), int(local_endpoint[1]))
 
             # barrier_all for init_server
             self._role_maker._barrier_all()
@@ -632,8 +642,8 @@ class DownpourOptimizer(DistributedOptimizer):
                           parameter_list,
                           no_grad_set,
                           self._strategy)
-        opt_info["mpi_rank"] = fleet._role_maker._get_rank()
-        opt_info["mpi_size"] = fleet._role_maker._get_size()
+        opt_info["mpi_rank"] = fleet.worker_index()
+        opt_info["mpi_size"] = fleet.worker_num()
         fleet._set_opt_info(opt_info)
 
         programs = [loss.block.program for loss in losses]

python/paddle/fluid/incubate/fleet/utils/fleet_util.py

@@ -206,7 +206,7 @@ class FleetUtil(object):
         pos = pos.reshape(-1)
         global_pos = np.copy(pos) * 0
         # mpi allreduce
-        fleet._role_maker._node_type_comm.Allreduce(pos, global_pos)
+        fleet._role_maker._all_reduce(pos, global_pos)
         # reshape to its original shape
         global_pos = global_pos.reshape(old_pos_shape)
 
@@ -215,7 +215,7 @@ class FleetUtil(object):
         old_neg_shape = np.array(neg.shape)
         neg = neg.reshape(-1)
         global_neg = np.copy(neg) * 0
-        fleet._role_maker._node_type_comm.Allreduce(neg, global_neg)
+        fleet._role_maker._all_reduce(neg, global_neg)
         global_neg = global_neg.reshape(old_neg_shape)
 
         # calculate auc
@@ -1350,7 +1350,7 @@ class FleetUtil(object):
         pos = pos.reshape(-1)
         global_pos = np.copy(pos) * 0
         # mpi allreduce
-        fleet._role_maker._node_type_comm.Allreduce(pos, global_pos)
+        fleet._role_maker._all_reduce(pos, global_pos)
         # reshape to its original shape
         global_pos = global_pos.reshape(old_pos_shape)
         # auc neg bucket
@@ -1358,7 +1358,7 @@ class FleetUtil(object):
         old_neg_shape = np.array(neg.shape)
         neg = neg.reshape(-1)
         global_neg = np.copy(neg) * 0
-        fleet._role_maker._node_type_comm.Allreduce(neg, global_neg)
+        fleet._role_maker._all_reduce(neg, global_neg)
         global_neg = global_neg.reshape(old_neg_shape)
 
         num_bucket = len(global_pos[0])
@@ -1368,7 +1368,7 @@ class FleetUtil(object):
             old_metric_shape = np.array(metric.shape)
             metric = metric.reshape(-1)
             global_metric = np.copy(metric) * 0
-            fleet._role_maker._node_type_comm.Allreduce(metric, global_metric)
+            fleet._role_maker._all_reduce(metric, global_metric)
             global_metric = global_metric.reshape(old_metric_shape)
             return global_metric[0]
 

python/paddle/fluid/tests/unittests/test_dataset.py

@@ -733,7 +733,7 @@ class TestDataset2(unittest.TestCase):
             place = fluid.CPUPlace()
             exe = fluid.Executor(place)
             try:
-                fleet.init(exe)
+                fleet.init()
             except ImportError as e:
                 print("warning: no mpi4py")
             adam = fluid.optimizer.Adam(learning_rate=0.000005)
@@ -795,7 +795,7 @@ class TestDataset2(unittest.TestCase):
             place = fluid.CPUPlace()
             exe = fluid.Executor(place)
             try:
-                fleet.init(exe)
+                fleet.init()
             except ImportError as e:
                 print("warning: no mpi4py")
             adam = fluid.optimizer.Adam(learning_rate=0.000005)
@@ -824,6 +824,10 @@ class TestDataset2(unittest.TestCase):
             dataset.set_pipe_command("cat")
             dataset.set_use_var(slots_vars)
             dataset.load_into_memory()
+            try:
+                dataset.global_shuffle(fleet)
+            except:
+                print("warning: catch expected error")
             fleet._opt_info = None
             fleet._fleet_ptr = None
 

python/paddle/fluid/tests/unittests/test_fleet_rolemaker.py

@@ -11,36 +11,41 @@
 # 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.
+"""Test cloud role maker."""
 
 from __future__ import print_function
 import os
 import unittest
-
 import paddle.fluid.incubate.fleet.base.role_maker as role_maker
 
 
 class TestCloudRoleMaker(unittest.TestCase):
+    """
+    Test cases for PaddleCloudRoleMaker.
+    """
+
     def setUp(self):
+        """Set up, set envs."""
         os.environ["PADDLE_TRAINERS_NUM"] = "2"
         os.environ[
             "PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"
 
     def test_tr_rolemaker(self):
+        """Test tr rolenamer."""
         os.environ["TRAINING_ROLE"] = "TRAINER"
         os.environ["PADDLE_TRAINER_ID"] = "0"
 
         ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
         ro.generate_role()
-
         self.assertTrue(ro.is_worker())
         self.assertFalse(ro.is_server())
         self.assertEqual(ro.worker_num(), 2)
 
     def test_ps_rolemaker(self):
+        """Test ps rolemaker."""
         os.environ["TRAINING_ROLE"] = "PSERVER"
         os.environ["POD_IP"] = "127.0.0.1"
         os.environ["PADDLE_PORT"] = "36001"
-
         ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
         ro.generate_role()
         self.assertFalse(ro.is_worker())
@@ -48,10 +53,75 @@ class TestCloudRoleMaker(unittest.TestCase):
         self.assertEqual(ro.worker_num(), 2)
 
     def test_traing_role(self):
+        """Test training role."""
         os.environ["TRAINING_ROLE"] = "TEST"
         ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
         self.assertRaises(ValueError, ro.generate_role)
 
+    def test_pslib_1(self):
+        """Test cases for pslib."""
+        import paddle.fluid as fluid
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
+        from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
+        try:
+            import netifaces
+        except:
+            print("warning: no netifaces, skip test_pslib_1")
+            return
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        role_maker = GeneralRoleMaker()
+        role_maker.generate_role()
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        fleet.init(role_maker)
+        train_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(train_program, startup_program):
+            show = fluid.layers.data(name="show", shape=[-1, 1], \
+                dtype="float32", lod_level=1, append_batch_size=False)
+            fc = fluid.layers.fc(input=show, size=1, act=None)
+            label = fluid.layers.data(name="click", shape=[-1, 1], \
+                dtype="int64", lod_level=1, append_batch_size=False)
+            label_cast = fluid.layers.cast(label, dtype='float32')
+            cost = fluid.layers.log_loss(fc, label_cast)
+        try:
+            adam = fluid.optimizer.Adam(learning_rate=0.000005)
+            adam = fleet.distributed_optimizer(adam)
+            adam.minimize([cost], [scope])
+            fleet.run_server()
+        except:
+            print("do not support pslib test, skip")
+            return
+        from paddle.fluid.incubate.fleet.base.role_maker import \
+            MPISymetricRoleMaker
+        try:
+            role = MPISymetricRoleMaker()
+            role._all_reduce([1], [2])
+        except:
+            print("catch expected error of not inited")
+        try:
+            role = MPISymetricRoleMaker()
+            role._all_reduce([1], [2], "min")
+        except:
+            print("catch expected error of not inited")
+        try:
+            role = MPISymetricRoleMaker()
+            role._all_reduce([1], [2], "max")
+        except:
+            print("catch expected error of not inited")
+        try:
+            role = MPISymetricRoleMaker()
+            role._all_reduce([1], [2], "unknown")
+        except:
+            print("catch expected error of unknown type")
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_fleet_rolemaker_2.py

+#   Copyright (c) 2019 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.
+"""Test cases for role makers."""
+
+from __future__ import print_function
+import os
+import unittest
+
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+
+class TestCloudRoleMaker2(unittest.TestCase):
+    """
+    Test cases for paddle cloud role makers.
+    """
+
+    def setUp(self):
+        """Set up, set envs."""
+        pass
+
+    def test_pslib_2(self):
+        """Test cases for pslib."""
+        import paddle.fluid as fluid
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+        from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
+        from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
+        from paddle.fluid.incubate.fleet.base.role_maker import RoleMakerBase
+        try:
+            import netifaces
+        except:
+            print("warning: no netifaces, skip test_pslib_2")
+            return
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        os.environ["PADDLE_TRAINERS_NUM"] = "1"
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        try:
+            fleet.init(None)
+        except:
+            print("no mpi4py, skip test_pslib_2")
+            return
+        train_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(train_program, startup_program):
+            show = fluid.layers.data(name="show", shape=[-1, 1], \
+                dtype="float32", lod_level=1, append_batch_size=False)
+            fc = fluid.layers.fc(input=show, size=1, act=None)
+            label = fluid.layers.data(name="click", shape=[-1, 1], \
+                dtype="int64", lod_level=1, append_batch_size=False)
+            label_cast = fluid.layers.cast(label, dtype='float32')
+            cost = fluid.layers.log_loss(fc, label_cast)
+        try:
+            adam = fluid.optimizer.Adam(learning_rate=0.000005)
+            adam = fleet.distributed_optimizer(adam)
+            adam.minimize([cost], [scope])
+            fleet.run_server()
+        except:
+            print("do not support pslib test, skip")
+            return
+        os.environ["TRAINING_ROLE"] = "wrong"
+        try:
+            role1 = GeneralRoleMaker(path="./test_gloo_1")
+            role1.generate_role()
+        except:
+            print("catch expected error of wrong TRAINING_ROLE")
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        role2 = GeneralRoleMaker(path="./test_gloo_2")
+        role2._finalize()
+        role2._all_gather(1)
+        role2._all_gather(1)
+        role2._barrier_server()
+        role2.all_gather(1)
+        role3 = GeneralRoleMaker(path="./test_gloo_3")
+        role3._worker_gather(1)
+        role3._worker_gather(1)
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
+        role4 = GeneralRoleMaker(path="./test_gloo_4")
+        role4._worker_gather(1)
+        role4._get_rank()
+        role4._get_size()
+        role4._all_comm.init(0, 0, "", "", "", "", "")
+        role5 = GeneralRoleMaker(path="./test_gloo_5")
+        role5.get_local_endpoint()
+        role5.get_local_endpoint()
+        role6 = GeneralRoleMaker(path="./test_gloo_6")
+        role6.get_trainer_endpoints()
+        role6.get_trainer_endpoints()
+        role7 = GeneralRoleMaker(path="./test_gloo_7")
+        role7.get_pserver_endpoints()
+        role7.get_pserver_endpoints()
+        role8 = GeneralRoleMaker(path="./test_gloo_8")
+        role8.is_worker()
+        role8.is_worker()
+        role9 = GeneralRoleMaker(path="./test_gloo_9")
+        role9.is_server()
+        role9.is_server()
+        role10 = GeneralRoleMaker(path="./test_gloo_10")
+        role10.is_first_worker()
+        role10.is_first_worker()
+        role11 = GeneralRoleMaker(path="./test_gloo_11")
+        role11.worker_index()
+        role11.worker_index()
+        role12 = GeneralRoleMaker(path="./test_gloo_12")
+        role12.server_index()
+        role12.server_index()
+        role13 = GeneralRoleMaker(path="./test_gloo_13")
+        role13.worker_num()
+        role13.worker_num()
+        role14 = GeneralRoleMaker(path="./test_gloo_14")
+        role14.server_num()
+        role14.server_num()
+        role15 = GeneralRoleMaker(path="./test_gloo_15")
+        role15._barrier_worker()
+        role15._barrier_worker()
+        role16 = GeneralRoleMaker(path="./test_gloo_16")
+        role16._barrier_all()
+        role16._barrier_all()
+        role17 = GeneralRoleMaker(path="./test_gloo_17")
+        role17._barrier_server()
+        role17._barrier_server()
+        role18 = GeneralRoleMaker(path="./test_gloo_18")
+        role18._worker_num()
+        role18._worker_num()
+        role19 = GeneralRoleMaker(path="./test_gloo_19")
+        role19._server_num()
+        role19._server_num()
+        role20 = GeneralRoleMaker(path="./test_gloo_20")
+        a = [1]
+        b = [0]
+        role20._all_reduce(a, b)
+        role21 = GeneralRoleMaker(path="./test_gloo_21")
+        role21.all_reduce_worker([], [])
+        role21.all_reduce_worker([], [])
+        role21.barrier_worker()
+        role21.barrier_all()
+        role22 = GeneralRoleMaker(path="./test_gloo_22")
+        role22._get_rank()
+        role22._get_rank()
+        os.environ["PADDLE_PSERVER_ID"] = "0"
+        role23 = GeneralRoleMaker(path="./test_gloo_23")
+        role23._get_size()
+        role23._get_size()
+        with open("test_fleet_gloo_role_maker_1.txt", "w") as f:
+            data = "1 1 1 1\n"
+            f.write(data)
+
+        dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+        dataset.set_filelist(["test_fleet_gloo_role_maker_1.txt"])
+        dataset.set_use_var([show, label])
+        dataset.load_into_memory()
+        dataset.get_memory_data_size(fleet)
+        dataset.get_shuffle_data_size(fleet)
+        os.remove("./test_fleet_gloo_role_maker_1.txt")
+
+        class TmpClass():
+            """
+            dummy tmp class
+            """
+
+            def __init__(self):
+                pass
+
+            def all_reduce_worker(self, input, output):
+                """
+                dummy all reduce worker
+
+                Args:
+                    input(None): fake input
+                    output(None): fale output
+                """
+                pass
+
+            def barrier_worker(self):
+                """
+                dummy barrier worker
+                """
+                pass
+
+        from paddle.fluid.incubate.fleet.base.fleet_base import Fleet
+
+        class TmpFleet(Fleet):
+            """
+            dummy tmp fleet
+            """
+
+            def __init__(self):
+                super(Fleet, self).__init__()
+                self._role_maker = None
+
+            def init_worker(self):
+                """
+                dummy init worker
+                """
+                pass
+
+            def init_server(self, model_dir=None):
+                """
+                dummy init server
+
+                Args:
+                    model_dir(None): fake model_dir
+                """
+                pass
+
+            def run_server(self):
+                """
+                dummy run server
+                """
+                pass
+
+            def stop_worker(self):
+                """
+                dummy stop worker
+                """
+                pass
+
+            def distributed_optimizer(self, optimizer, strategy=None):
+                """
+                dummy distributed optimizer
+                
+                Args:
+                    optimizer(None): fake optimizer
+                    strategy(None): fake strategy
+                """
+                pass
+
+            def save_inference_model(self):
+                """
+                dummy save inference model
+                """
+                pass
+
+            def save_persistables(self):
+                """
+                dummy save persistables
+                """
+                pass
+
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        tmp = TmpFleet()
+        tmp._role_maker = TmpClass()
+        tmp.all_reduce_worker([], [])
+        tmp.barrier_worker()
+        from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
+        tmp = RoleMakerBase()
+        tmp.all_gather(1)
+        tmp.all_reduce_worker([], [])
+        tmp.barrier_worker()
+        tmp.barrier_all()
+        from paddle.fluid.incubate.fleet.base.role_maker import \
+            MPISymetricRoleMaker
+        tmp1 = MPISymetricRoleMaker()
+        tmp1.all_gather(1)
+        tmp1.all_gather(1)
+        tmp2 = MPISymetricRoleMaker()
+        tmp2.all_reduce_worker([], [])
+        tmp3 = MPISymetricRoleMaker()
+        tmp3.barrier_worker()
+        tmp3.barrier_worker()
+        tmp4 = MPISymetricRoleMaker()
+        tmp4.barrier_all()
+        tmp4.barrier_all()
+
+
+if __name__ == "__main__":
+    unittest.main()