update by comments

paddle/fluid/framework/parallel_executor.h

@@ -42,7 +42,7 @@ class ParallelExecutor {
                             const std::vector<Scope*>& local_scopes,
                             bool allow_op_delay, bool use_default_grad_scale,
                             bool balance_parameter_opt_between_cards,
-                            size_t num_trainers = 0, size_t trainer_id = 0);
+                            size_t num_trainers = 1, size_t trainer_id = 0);
 
   ~ParallelExecutor();
 

paddle/fluid/operators/gen_nccl_id_op.cc

@@ -75,29 +75,29 @@ class GenNCCLIdOp : public framework::OperatorBase {
     // NOTE: Can not use unique_ptr here because the default
     // deleter will call GRPC Server's base class's dtor and
     // that will cause a wired crash.
-    rpc_service_ = new detail::AsyncGRPCServer(endpoint, true);
+
+    detail::AsyncGRPCServer rpc_service(endpoint, true);
     framework::ProgramDesc empty_program;
     framework::Executor executor(dev_ctx.GetPlace());
-    rpc_service_->SetScope(scope);
-    rpc_service_->SetDevCtx(&dev_ctx);
-    rpc_service_->SetProgram(&empty_program);
-    rpc_service_->SetExecutor(&executor);
+    rpc_service.SetScope(scope);
+    rpc_service.SetDevCtx(&dev_ctx);
+    rpc_service.SetProgram(&empty_program);
+    rpc_service.SetExecutor(&executor);
 
     std::thread server_thread(
-        std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, rpc_service_));
-    rpc_service_->SetCond(0);
+        std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, &rpc_service));
+    rpc_service.SetCond(0);
     VLOG(3) << "start getting nccl id from trainer 0...";
-    auto recv = rpc_service_->Get();
+    auto recv = rpc_service.Get();
     VLOG(3) << "got nccl id and stop server...";
-    rpc_service_->ShutDown();
+    rpc_service.ShutDown();
     VLOG(3) << "rpc server stopped";
     // TODO(wuyi): reinit nccl communicators
     server_thread.join();
-    delete rpc_service_;
   }
 
- protected:
-  mutable detail::AsyncGRPCServer* rpc_service_ = nullptr;
+  //  protected:
+  // mutable detail::AsyncGRPCServer* rpc_service_ = nullptr;
 };
 
 class GenNCCLIdOpMaker : public framework::OpProtoAndCheckerMaker {

paddle/fluid/platform/nccl_helper.h

@@ -78,7 +78,7 @@ struct NCCLContextMap {
 
   explicit NCCLContextMap(const std::vector<platform::Place> &places,
                           ncclUniqueId *nccl_id = nullptr,
-                          size_t num_trainers = 0, size_t trainer_id = 0) {
+                          size_t num_trainers = 1, size_t trainer_id = 0) {
     PADDLE_ENFORCE(!places.empty());
     order_.reserve(places.size());
     for (auto &p : places) {
@@ -100,7 +100,7 @@ struct NCCLContextMap {
       PADDLE_ENFORCE(platform::dynload::ncclCommInitAll(
           comms.get(), static_cast<int>(order_.size()), order_.data()));
     } else {
-      PADDLE_ENFORCE_GT(num_trainers, 0);
+      PADDLE_ENFORCE_GT(num_trainers, 1);
       // TODO(wuyi): need to ensure each node have same number of GPUs
       {
         int nranks = num_trainers * order_.size();

python/paddle/fluid/parallel_executor.py

@@ -32,7 +32,7 @@ class ParallelExecutor(object):
                  share_vars_from=None,
                  use_default_grad_scale=True,
                  balance_parameter_opt_between_cards=False,
-                 num_trainers=0,
+                 num_trainers=1,
                  trainer_id=0):
         """
         ParallelExecutor can run program in parallel.
@@ -57,7 +57,7 @@ class ParallelExecutor(object):
             balance_parameter_opt_between_cards(bool, default True): Whether
                 updating different gradients on different cards. Currently, it
                 is not recommended.
-            num_trainers(int, default 0): If greater than 0, NCCL will be
+            num_trainers(int, default 1): If greater than 1, NCCL will be
                 initialized with multpile rank of nodes, each node should have
                 same number of GPUs. Distributed training will be enabled then.
             trainer_id(int, default 0): Must use together with num_trainers.