Merge pull request #16788 from guru4elephant/fix_python_codestyle

Fix python codestyle in incubator

paddle/fluid/platform/lodtensor_printer.cc

@@ -52,16 +52,26 @@ void PrintVar(framework::Scope* scope, const std::string& var_name,
     return;
   }
 
+  framework::LoDTensor printed_tensor;
+  printed_tensor.set_lod(tensor->lod());
+  printed_tensor.Resize(tensor->dims());
+  if (platform::is_cpu_place(tensor->place())) {
+    printed_tensor.ShareDataWith(*tensor);
+  } else {
+    platform::CPUPlace place;
+    framework::TensorCopy(*tensor, place, &printed_tensor);
+  }
+
 #define PrintLoDTensorCallback(cpp_type, proto_type)                    \
   do {                                                                  \
     if (tensor->type() == proto_type) {                                 \
-      print_lod_tensor<cpp_type>(var_name, *tensor, print_info); \
+      print_lod_tensor<cpp_type>(var_name, printed_tensor, print_info); \
       return;                                                           \
     }                                                                   \
   } while (0)
 
   _ForEachDataType_(PrintLoDTensorCallback);
-  VLOG(1) << "PrintVar: unrecognized data type:" << tensor->type();
+  VLOG(1) << "PrintVar: unrecognized data type:" << printed_tensor.type();
 }
 
 }  // end namespace platform

python/paddle/fluid/device_worker.py

@@ -26,8 +26,8 @@ class DeviceWorker(object):
         """
         Init.
         """
-        self.program_ = None
-        self.infer_ = None
+        self._program = None
+        self._infer = None
 
     def _set_infer(self, infer=False):
         """
@@ -36,7 +36,7 @@ class DeviceWorker(object):
         Args:
             infer(bool): whether to do inference
         """
-        self.infer_ = infer
+        self._infer = infer
 
     def _set_fleet_desc(self, fleet_desc):
         """
@@ -45,7 +45,7 @@ class DeviceWorker(object):
         Args:
             fleet_desc(PSParameter): pslib.PSParameter object
         """
-        self.fleet_desc_ = fleet_desc
+        self._fleet_desc = fleet_desc
 
     def _set_program(self, program):
         """
@@ -54,7 +54,7 @@ class DeviceWorker(object):
         Args:
             program(Program): a Program object
         """
-        self.program_ = program
+        self._program = program
 
     def _gen_worker_desc(self, trainer_desc):
         """
@@ -88,7 +88,7 @@ class Hogwild(DeviceWorker):
             trainer_desc(TrainerDesc): a TrainerDesc object
         """
         trainer_desc.device_worker_name = "HogwildWorker"
-        if self.infer_:
+        if self._infer:
             # just ignore feed op for inference model
             trainer_desc.hogwild_param.skip_ops.extend(["feed"])
 
@@ -113,11 +113,11 @@ class DownpourSGD(DeviceWorker):
             trainer_desc(TrainerDesc): a TrainerDesc object
         """
         dense_table_set = set()
-        program_id = str(id(self.program_))
-        if self.program_ == None:
+        program_id = str(id(self._program))
+        if self._program == None:
             print("program of current device worker is not configured")
             exit(-1)
-        opt_info = self.program_._fleet_opt
+        opt_info = self._program._fleet_opt
         program_configs = opt_info["program_configs"]
         downpour = trainer_desc.downpour_param
 
@@ -140,7 +140,7 @@ class DownpourSGD(DeviceWorker):
         trainer_desc.device_worker_name = "DownpourWorker"
         pull_thread = trainer_desc.pull_dense_param
         pull_thread.device_num = trainer_desc.thread_num
-        for i in self.fleet_desc_.trainer_param.dense_table:
+        for i in self._fleet_desc.trainer_param.dense_table:
             if i.table_id in dense_table_set:
                 dense_table = pull_thread.dense_table.add()
                 dense_table.dense_value_name.extend(i.dense_variable_name)
@@ -148,29 +148,29 @@ class DownpourSGD(DeviceWorker):
                     i.table_id
         sparse_table = downpour.sparse_table.add()
         sparse_table.table_id = \
-                    self.fleet_desc_.trainer_param.sparse_table[0].table_id
+                    self._fleet_desc.trainer_param.sparse_table[0].table_id
         sparse_table.sparse_key_name.extend(
-            self.fleet_desc_.trainer_param.sparse_table[0].slot_key)
+            self._fleet_desc.trainer_param.sparse_table[0].slot_key)
         sparse_table.sparse_value_name.extend(
-            self.fleet_desc_.trainer_param.sparse_table[0].slot_value)
+            self._fleet_desc.trainer_param.sparse_table[0].slot_value)
         sparse_table.sparse_grad_name.extend(
-            self.fleet_desc_.trainer_param.sparse_table[0].slot_gradient)
+            self._fleet_desc.trainer_param.sparse_table[0].slot_gradient)
         sparse_table.emb_dim = \
-                    self.fleet_desc_.server_param.downpour_server_param.downpour_table_param[
+                    self._fleet_desc.server_param.downpour_server_param.downpour_table_param[
                         0].accessor.fea_dim - 2
         sparse_table.fea_dim = sparse_table.emb_dim + 2
         # TODO(guru4elephant): hard code here, need to improve
         sparse_table.label_var_name = "click"
 
-        for i in self.fleet_desc_.trainer_param.dense_table:
+        for i in self._fleet_desc.trainer_param.dense_table:
             if i.table_id in dense_table_set:
                 dense_table = downpour.dense_table.add()
                 dense_table.table_id = i.table_id
                 dense_table.dense_value_name.extend(i.dense_variable_name)
                 dense_table.dense_grad_name.extend(
                     i.dense_gradient_variable_name)
-                downpour.skip_ops.extend(self.fleet_desc_.trainer_param.skip_op)
-        if self.infer_:
+                downpour.skip_ops.extend(self._fleet_desc.trainer_param.skip_op)
+        if self._infer:
             downpour.push_dense = False
             downpour.push_sparse = False
 

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

@@ -23,10 +23,10 @@ class RoleMakerBase(object):
     """
 
     def __init__(self):
-        self.role_maker_name_ = ""
-        self.trainer_endpoints_ = []
-        self.pserver_endpoints_ = []
-        self.role_is_generated_ = False
+        self._role_maker_name = ""
+        self._trainer_endpoints = []
+        self._pserver_endpoints = []
+        self._role_is_generated = False
 
     def _is_worker(self):
         """
@@ -45,20 +45,20 @@ class RoleMakerBase(object):
         return get local ip
         """
         import socket
-        self.ip_ = socket.gethostbyname(socket.gethostname())
-        return self.ip_
+        self._ip = socket.gethostbyname(socket.gethostname())
+        return self._ip
 
     def _get_trainer_endpoints(self):
         """
         return trainer endpoints
         """
-        return self.trainer_endpoints_
+        return self._trainer_endpoints
 
     def _get_pserver_endpoints(self):
         """
         return pserver endpoints
         """
-        return self.pserver_endpoints_
+        return self._pserver_endpoints
 
     def _generate_role(self):
         """
@@ -76,59 +76,59 @@ class MPIRoleMaker(RoleMakerBase):
     def __init__(self):
         super(MPIRoleMaker, self).__init__()
         from mpi4py import MPI
-        self.comm_ = MPI.COMM_WORLD
+        self._comm = MPI.COMM_WORLD
         self.MPI = MPI
-        self.ips_ = None
+        self._ips = None
 
     def _get_rank(self):
         """
         return rank
         """
-        self.rank_ = self.comm_.Get_rank()
-        return self.rank_
+        self._rank = self._comm.Get_rank()
+        return self._rank
 
     def _get_size(self):
         """
         return size
         """
-        self.size_ = self.comm_.Get_size()
-        return self.size_
+        self._size = self._comm.Get_size()
+        return self._size
 
     def _all_gather(self, obj):
         """
         all_gather(obj) will call MPI's allgather function
         """
         self._barrier_all()
-        return self.comm_.allgather(obj)
+        return self._comm.allgather(obj)
 
     def _worker_gather(self, obj):
         """
         worker_gather(obj) will call MPI's allgather function
         """
         if self._is_worker():
-            self.node_type_comm_.barrier()
-            return self.node_type_comm_.allgather(obj)
+            self._node_type_comm.barrier()
+            return self._node_type_comm.allgather(obj)
         return None
 
     def _barrier_all(self):
         """
         barrier_all() will call MPI's barrier_all function
         """
-        self.comm_.barrier()
+        self._comm.barrier()
 
     def _get_ips(self):
         """
         collect current distributed job's ip list
         """
-        if self.ips_ == None:
-            self.ips_ = self.comm_.allgather(self._get_local_ip())
-        return self.ips_
+        if self._ips == None:
+            self._ips = self._comm.allgather(self._get_local_ip())
+        return self._ips
 
     def _finalize(self):
         """
         finalize the current MPI instance.
         """
-        self.comm_.finalize()
+        self._comm.finalize()
 
 
 class MPISymetricRoleMaker(MPIRoleMaker):
@@ -140,11 +140,11 @@ class MPISymetricRoleMaker(MPIRoleMaker):
 
     def __init__(self):
         super(MPISymetricRoleMaker, self).__init__()
-        self.node_type_ = None
-        self.proc_per_node_ = 2
+        self._node_type = None
+        self._proc_per_node = 2
 
     def _check_role_generation(self):
-        if not self.role_is_generated_:
+        if not self._role_is_generated:
             sys.stderr.write("generate_role() should be called first")
             sys.exit(-1)
             return False
@@ -163,7 +163,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return whether current process is worker assigned by role maker
         """
         if self._check_role_generation():
-            return self.node_type_ == 1
+            return self._node_type == 1
         return False
 
     def _is_server(self):
@@ -171,7 +171,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return whether current process is server assigned by role maker
         """
         if self._check_role_generation():
-            return self.node_type_ == 0
+            return self._node_type == 0
         return False
 
     def _worker_num(self):
@@ -197,7 +197,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return the index of worker
         """
         if self._check_role_generation():
-            return self.rank_ / self.proc_per_node_
+            return self._rank / self._proc_per_node
         return 0
 
     def _server_index(self):
@@ -205,7 +205,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return the index of server
         """
         if self._check_role_generation():
-            return self.rank_ / self.proc_per_node_
+            return self._rank / self._proc_per_node
         return 0
 
     def _barrier_worker(self):
@@ -214,7 +214,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         """
         if self._check_role_generation():
             if self._is_worker():
-                self.node_type_comm_.barrier()
+                self._node_type_comm.barrier()
 
     def _barrier_server(self):
         """
@@ -222,20 +222,20 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         """
         if self._check_role_generation():
             if self._is_server():
-                self.node_type_comm_.barrier()
+                self._node_type_comm.barrier()
 
     def _generate_role(self):
         """
         generate currently process's role
         """
-        if not self.role_is_generated_:
+        if not self._role_is_generated:
             # TODO(guru4elephant): only allow to be called once
-            self.trainer_endpoints_ = self._get_ips()
-            self.pserver_endpoints_ = self._get_ips()
+            self._trainer_endpoints = self._get_ips()
+            self._pserver_endpoints = self._get_ips()
 
-            if 0 == self._get_rank() % self.proc_per_node_ % 2:
-                self.node_type_ = 0
+            if 0 == self._get_rank() % self._proc_per_node % 2:
+                self._node_type = 0
             else:
-                self.node_type_ = 1
-            self.node_type_comm_ = self.comm_.Split(self.node_type_)
-            self.role_is_generated_ = True
+                self._node_type = 1
+            self._node_type_comm = self._comm.Split(self._node_type)
+            self._role_is_generated = True

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

@@ -64,9 +64,9 @@ class Fleet(object):
 
     def __init__(self):
         self._opt_info = None  # for fleet only
-        self.role_maker_ = None
-        self.local_ip_ = 0
-        self.is_initialized_ = False
+        self._role_maker = None
+        self._local_ip = 0
+        self._is_initialized = False
 
     def init(self):
         # TODO(guru4elephant)
@@ -78,22 +78,22 @@ class Fleet(object):
             current node's role, e.g. worker, server, etc.
         """
         if not self.is_initialized_:
-            self.role_maker_ = MPISymetricRoleMaker()
-            self.role_maker_._generate_role()
+            self._role_maker = MPISymetricRoleMaker()
+            self._role_maker._generate_role()
             self._fleet_ptr = fluid.core.Fleet()
-            self.is_initialized_ = True
+            self._is_initialized = True
 
     def stop(self):
         """
         stop(): will be called after a user finishes his/her training task. Fleet instance will be
             destroyed when stop() is called.
         """
-        self.role_maker_._barrier_worker()
-        if self.role_maker_._is_first_worker():
+        self._role_maker._barrier_worker()
+        if self._role_maker._is_first_worker():
             self._fleet_ptr.stop_server()
-        self.role_maker_._barrier_worker()
-        self.role_maker_._barrier_all()
-        self.role_maker_._finalize()
+        self._role_maker._barrier_worker()
+        self._role_maker._barrier_all()
+        self._role_maker._finalize()
 
     def init_pserver(self):
         """
@@ -110,15 +110,15 @@ class Fleet(object):
                 sys.exit(-1)
             self._fleet_ptr.init_server(self._dist_desc_str,
                                         self.role_maker_._get_rank())
-            self.local_ip_ = self._fleet_ptr.run_server()
+            self._local_ip = self._fleet_ptr.run_server()
             # barrier_all for init_server
-            self.role_maker_._barrier_all()
-            self.all_ips_ = self.role_maker_._all_gather(self.local_ip_)
+            self._role_maker._barrier_all()
+            self._all_ips = self._role_maker._all_gather(self.local_ip_)
 
-            self._fleet_ptr.gather_servers(self.all_ips_,
-                                           self.role_maker_._get_size())
+            self._fleet_ptr.gather_servers(self._all_ips,
+                                           self._role_maker._get_size())
             # barrier_all for init_worker, wait all workers start
-            self.role_maker_._barrier_all()
+            self._role_maker._barrier_all()
         else:
             print("You should run DistributedOptimizer.minimize() first")
             sys.exit(-1)
@@ -151,21 +151,21 @@ class Fleet(object):
                 print("You should run DistributedOptimizer.minimize() first")
                 sys.exit(-1)
             # 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_)
-            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._barrier_all()
+            self._all_ips = self._role_maker._all_gather(self.local_ip_)
+            self._fleet_ptr.init_worker(self._dist_desc_str, self._all_ips,
+                                        self._role_maker._get_size(),
+                                        self._role_maker._get_rank())
             # barrier_all for init_worker
-            self.role_maker_._barrier_all()
+            self._role_maker._barrier_all()
             # prepare for client to client communication
             info = self._fleet_ptr.get_clients_info()
-            all_info = self.role_maker_._worker_gather(info[0])
+            all_info = self._role_maker._worker_gather(info[0])
             self._fleet_ptr.gather_clients(all_info)
             self._fleet_ptr.create_client2client_connection()
             # barrier for init model
-            self.role_maker_._barrier_worker()
-            if self.role_maker_._is_first_worker():
+            self._role_maker._barrier_worker()
+            if self._role_maker._is_first_worker():
                 tables = self._dist_desc.trainer_param.dense_table
                 for prog, scope in zip(programs, scopes):
                     prog_id = str(id(prog))
@@ -192,7 +192,7 @@ class Fleet(object):
                                                    int(table.table_id),
                                                    var_name_list)
             # barrier for init model done
-            self.role_maker_._barrier_worker()
+            self._role_maker._barrier_worker()
         else:
             print("You should run DistributedOptimizer.minimize() first")
             sys.exit(-1)
@@ -201,39 +201,39 @@ class Fleet(object):
         """
         return the number of current job's worker num
         """
-        return self.role_maker_._worker_num()
+        return self._role_maker._worker_num()
 
     def get_server_num(self):
         """
         return the number of current job's server num
         """
-        return self.role_maker_._server_num()
+        return self._role_maker._server_num()
 
     def get_worker_index(self):
         """
         return the mpi rank of current worker
         """
-        return self.role_maker_._worker_index()
+        return self._role_maker._worker_index()
 
     def is_worker(self):
         """
         return whether current node is a worker
         """
-        return self.role_maker_._is_worker()
+        return self._role_maker._is_worker()
 
     def is_server(self):
         """
         return whether current node is pserver
         """
-        return self.role_maker_._is_server()
+        return self._role_maker._is_server()
 
     def init_pserver_model(self):
         """
         init pserver model called from pserver
         """
-        if self.role_maker_._is_first_worker():
+        if self._role_maker._is_first_worker():
             self._fleet_ptr.init_model()
-        self.role_maker_._barrier_worker()
+        self._role_maker._barrier_worker()
 
     def save_pserver_model(self, save_path):
         """

python/paddle/fluid/incubate/fleet/parameter_server/node.py

@@ -42,13 +42,13 @@ class DownpourServer(Server):
     """
 
     def __init__(self):
-        self.server_ = pslib.ServerParameter()
-        self.server_.downpour_server_param.service_param.start_server_port = 0
-        self.server_.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
-        self.server_.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
-        self.server_.downpour_server_param.service_param.service_class = "DownpourPsService"
-        self.server_.downpour_server_param.service_param.start_server_port = 0
-        self.server_.downpour_server_param.service_param.server_thread_num = 12
+        self._server = pslib.ServerParameter()
+        self._server.downpour_server_param.service_param.start_server_port = 0
+        self._server.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
+        self._server.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
+        self._server.downpour_server_param.service_param.service_class = "DownpourPsService"
+        self._server.downpour_server_param.service_param.start_server_port = 0
+        self._server.downpour_server_param.service_param.server_thread_num = 12
 
     def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
                          slot_value_var):
@@ -62,7 +62,7 @@ class DownpourServer(Server):
         Returns:
             return None 
         """
-        table = self.server_.downpour_server_param.downpour_table_param.add()
+        table = self._server.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
         table.table_class = "DownpourSparseTable"
         table.type = pslib.PS_SPARSE_TABLE
@@ -123,7 +123,7 @@ class DownpourServer(Server):
         Returns:
             return None 
         """
-        table = self.server_.downpour_server_param.downpour_table_param.add()
+        table = self._server.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
         table.table_class = "DownpourDenseTable"
         table.type = pslib.PS_DENSE_TABLE
@@ -140,7 +140,7 @@ class DownpourServer(Server):
         """
         Return downpour server program_desc
         """
-        return self.server_
+        return self._server
 
 
 class DownpourWorker(Worker):
@@ -155,7 +155,7 @@ class DownpourWorker(Worker):
 
     def __init__(self, window):
         self.window = window
-        self.worker_ = pslib.DownpourTrainerParameter()
+        self._worker = pslib.DownpourTrainerParameter()
 
     def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
                          slot_value_vars):
@@ -187,7 +187,7 @@ class DownpourWorker(Worker):
         Returns:
             return None 
         """
-        table = self.worker_.dense_table.add()
+        table = self._worker.dense_table.add()
         table.table_id = table_id
         table.dense_variable_name.extend(
             filter(lambda x: x.find("embedding") == -1,
@@ -200,4 +200,4 @@ class DownpourWorker(Worker):
         """
         Return downpour worker program_desc
         """
-        return self.worker_
+        return self._worker

python/paddle/fluid/incubate/fleet/parameter_server/optimizer_factory.py

@@ -24,9 +24,9 @@ from .node import DownpourWorker, DownpourServer
 
 class DistributedOptimizerImplBase(object):
     def __init__(self, optimizer):
-        self.optimizer_ = optimizer
-        self.learning_rate_ = optimizer._learning_rate
-        self.regularization_ = optimizer.regularization
+        self._optimizer = optimizer
+        self._learning_rate = optimizer._learning_rate
+        self._regularization = optimizer.regularization
 
     def minimize(self,
                  losses,
@@ -41,7 +41,7 @@ class DistributedAdam(DistributedOptimizerImplBase):
         # todo(guru4elephant): add more optimizers here as argument
         # todo(guru4elephant): make learning_rate as a variable
         super(DistributedAdam, self).__init__(optimizer)
-        self.window_ = 1
+        self._window = 1
         self.type = "downpour"
         self.data_norm_name = [
             ".batch_size", ".batch_square_sum", ".batch_sum",
@@ -79,9 +79,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
         server = DownpourServer()
         worker = DownpourWorker(self.window_)
         sparse_table_index = 0
-        server.add_sparse_table(sparse_table_index, self.learning_rate_,
+        server.add_sparse_table(sparse_table_index, self._learning_rate,
                                 prefetch_slots, prefetch_slots_emb)
-        worker.add_sparse_table(sparse_table_index, self.learning_rate_,
+        worker.add_sparse_table(sparse_table_index, self._learning_rate,
                                 prefetch_slots, prefetch_slots_emb)
         dense_table_index = 1
         program_configs = {}
@@ -124,9 +124,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
                         data_norm_grads.append(i[1])
                 if not is_data_norm_data:
                     grads.append(i[1])
-            server.add_dense_table(dense_table_index, self.learning_rate_,
+            server.add_dense_table(dense_table_index, self._learning_rate,
                                    params, grads)
-            worker.add_dense_table(dense_table_index, self.learning_rate_,
+            worker.add_dense_table(dense_table_index, self._learning_rate,
                                    params, grads)
             program_configs[program_id]["pull_dense"] = [dense_table_index]
             program_configs[program_id]["push_dense"] = [dense_table_index]
@@ -135,9 +135,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
             if len(data_norm_params) != 0 and len(data_norm_grads) != 0:
                 dense_table_index += 1
                 server.add_data_norm_table(dense_table_index,
-                                           self.learning_rate_,
+                                           self._learning_rate,
                                            data_norm_params, data_norm_grads)
-                worker.add_dense_table(dense_table_index, self.learning_rate_,
+                worker.add_dense_table(dense_table_index, self._learning_rate,
                                        data_norm_params, data_norm_grads)
                 #program_config.pull_dense_table_id.extend([dense_table_index])
                 #program_config.push_dense_table_id.extend([dense_table_index])

python/paddle/fluid/trainer_desc.py

@@ -28,10 +28,10 @@ class TrainerDesc(object):
         import multiprocessing as mp
         # set default thread num == cpu count
         self.proto_desc.thread_num = mp.cpu_count()
-        self.fleet_desc_ = None
-        self.device_worker_ = None
-        self.program_ = None
-        self.infer_ = False
+        self._fleet_desc = None
+        self._device_worker = None
+        self._program = None
+        self._infer = False
 
     def _set_fetch_var_and_info(self, fetch_vars, fetch_info, print_period):
         for i, v in enumerate(fetch_vars):
@@ -47,19 +47,19 @@ class TrainerDesc(object):
         self.proto_desc.thread_num = thread_num
 
     def _set_device_worker(self, device_worker):
-        self.device_worker_ = device_worker
+        self._device_worker = device_worker
 
     def _set_infer(self, infer):
-        self.infer_ = infer
+        self._infer = infer
 
     def _set_fleet_desc(self, fleet_desc):
-        self.fleet_desc_ = fleet_desc
+        self._fleet_desc = fleet_desc
 
     def _gen_trainer_desc(self):
         pass
 
     def _set_program(self, program):
-        self.program_ = program
+        self._program = program
 
     def _desc(self):
         from google.protobuf import text_format
@@ -73,13 +73,13 @@ class MultiTrainer(TrainerDesc):
 
     def _set_program(self, program):
         super(MultiTrainer, self)._set_program(program)
-        self.program_ = program
+        self._program = program
 
     def _gen_trainer_desc(self):
         super(MultiTrainer, self)._gen_trainer_desc()
         self.proto_desc.class_name = "MultiTrainer"
-        self.device_worker_._set_infer(self.infer_)
-        self.device_worker_._gen_worker_desc(self.proto_desc)
+        self._device_worker._set_infer(self._infer)
+        self._device_worker._gen_worker_desc(self.proto_desc)
 
 
 class DistMultiTrainer(TrainerDesc):
@@ -89,13 +89,13 @@ class DistMultiTrainer(TrainerDesc):
 
     def _set_program(self, program):
         super(DistMultiTrainer, self)._set_program(program)
-        self.program_ = program
+        self._program = program
 
     def _gen_trainer_desc(self):
         super(DistMultiTrainer, self)._gen_trainer_desc()
         self.proto_desc.class_name = "DistMultiTrainer"
-        if self.program_ == None:
+        if self._program == None:
             raise RuntimeError("None Program")
-        self.device_worker_._set_infer(self.infer_)
-        self.device_worker_._set_program(self.program_)
-        self.device_worker_._gen_worker_desc(self.proto_desc)
+        self._device_worker._set_infer(self._infer)
+        self._device_worker._set_program(self._program)
+        self._device_worker._gen_worker_desc(self.proto_desc)