Cherry pick Fix Bug-prone code of PE (#18355)

* update pe reduce config
test=release/1.5

*  drop the local_exe_scopes of the previous parallel_executor
test=release/1.5

paddle/fluid/framework/parallel_executor.cc

@@ -311,8 +311,8 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   member_->global_scope_ = scope;
   member_->use_cuda_ = exec_strategy.use_cuda_;
   member_->build_strategy_ = build_strategy;
-  member_->use_all_reduce_ =
-      build_strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce;
+  member_->use_all_reduce_ = member_->build_strategy_.reduce_ ==
+                             BuildStrategy::ReduceStrategy::kAllReduce;
   member_->nranks_ = build_strategy.num_trainers_ * places.size();
   if (!member_->use_all_reduce_ && member_->nranks_ == 1) {
     LOG(INFO) << "If you set build_strategy.reduce with 'Reduce',"
@@ -348,7 +348,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   }
 
   std::vector<ir::Graph *> graphs;
-  if (build_strategy.async_mode_) {
+  if (member_->build_strategy_.async_mode_) {
     PADDLE_ENFORCE(!member_->use_cuda_,
                    "gpu mode does not support async_mode_ now!");
     graphs.push_back(graph);
@@ -362,9 +362,10 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   // FIXME(Yancey1989): parallel graph mode get better performance
   // in GPU allreduce distributed training. Need an elegant way to
   // choice the execution strategy.
-  build_strategy.enable_parallel_graph_ =
-      EnableParallelGraphExecution(*graph, exec_strategy, build_strategy);
-  if (build_strategy.enable_parallel_graph_) {
+  member_->build_strategy_.enable_parallel_graph_ =
+      EnableParallelGraphExecution(*graph, exec_strategy,
+                                   member_->build_strategy_);
+  if (member_->build_strategy_.enable_parallel_graph_) {
     LOG(INFO) << "The Executor would execute the graph by ParallelGraph "
                  "Execution which can get better performance,"
               << "you can force it off by env FLAGS_enable_parallel_graph=0";
@@ -372,7 +373,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
 
   if (member_->use_cuda_ && member_->nranks_ > 1) {
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-    member_->InitOrGetNCCLCommunicator(scope, build_strategy);
+    member_->InitOrGetNCCLCommunicator(scope, member_->build_strategy_);
 
     // Initialize device context's nccl comm, will be used by normal
     // Operators like sync_batch_norm, and collective ops.
@@ -395,7 +396,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   }
   // broadcast parameters from the 0th device to others:
   auto need_broadcast = [&]() -> bool {
-    if (build_strategy.num_trainers_ > 1) {
+    if (member_->build_strategy_.num_trainers_ > 1) {
       // 1. num_tariners would be grater than 1 for nccl distributed training.
       return true;
     } else if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
@@ -407,7 +408,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   };
   // Bcast Parameters to all GPUs
   if (need_broadcast()) {
-    BCastParamsToDevices(bcast_vars, build_strategy.trainer_id_);
+    BCastParamsToDevices(bcast_vars, member_->build_strategy_.trainer_id_);
   }
 
   // Startup Program has been run. All local scopes has correct parameters.
@@ -416,39 +417,40 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   // ncclOp
   std::vector<ir::Graph *> async_graphs(places.size());
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-  if (build_strategy.async_mode_) {
+  if (member_->build_strategy_.async_mode_) {
     VLOG(3) << "use local async mode";
-    graph = build_strategy.Apply(graph, {member_->places_[0]}, loss_var_name,
-                                 {member_->local_scopes_[0]}, 1,
-                                 member_->use_cuda_, member_->nccl_ctxs_);
+    graph = member_->build_strategy_.Apply(
+        graph, {member_->places_[0]}, loss_var_name,
+        {member_->local_scopes_[0]}, 1, member_->use_cuda_,
+        member_->nccl_ctxs_);
     for (size_t i = 1; i < member_->places_.size(); ++i) {
-      graphs[i] =
-          build_strategy.Apply(graphs[i], {member_->places_[i]}, loss_var_name,
-                               {member_->local_scopes_[i]}, 1,
-                               member_->use_cuda_, member_->nccl_ctxs_);
+      graphs[i] = member_->build_strategy_.Apply(
+          graphs[i], {member_->places_[i]}, loss_var_name,
+          {member_->local_scopes_[i]}, 1, member_->use_cuda_,
+          member_->nccl_ctxs_);
       async_graphs[i] = graphs[i];
     }
   } else {
-    graph = build_strategy.Apply(graph, member_->places_, loss_var_name,
-                                 member_->local_scopes_, member_->nranks_,
-                                 member_->use_cuda_, member_->nccl_ctxs_);
+    graph = member_->build_strategy_.Apply(
+        graph, member_->places_, loss_var_name, member_->local_scopes_,
+        member_->nranks_, member_->use_cuda_, member_->nccl_ctxs_);
   }
 #else
-  if (build_strategy.async_mode_) {
+  if (member_->build_strategy_.async_mode_) {
     VLOG(3) << "use local async mode";
-    graph = build_strategy.Apply(graph, {member_->places_[0]}, loss_var_name,
-                                 {member_->local_scopes_[0]}, 1,
-                                 member_->use_cuda_);
+    graph = member_->build_strategy_.Apply(
+        graph, {member_->places_[0]}, loss_var_name,
+        {member_->local_scopes_[0]}, 1, member_->use_cuda_);
     for (size_t i = 1; i < member_->places_.size(); ++i) {
-      graphs[i] = build_strategy.Apply(
+      graphs[i] = member_->build_strategy_.Apply(
           graphs[i], {member_->places_[i]}, loss_var_name,
           {member_->local_scopes_[i]}, 1, member_->use_cuda_);
       async_graphs[i] = graphs[i];
     }
   } else {
-    graph = build_strategy.Apply(graph, member_->places_, loss_var_name,
-                                 member_->local_scopes_, member_->nranks_,
-                                 member_->use_cuda_);
+    graph = member_->build_strategy_.Apply(
+        graph, member_->places_, loss_var_name, member_->local_scopes_,
+        member_->nranks_, member_->use_cuda_);
   }
 #endif
 
@@ -489,11 +491,11 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     }
   }
 
-  if (build_strategy.async_mode_) {
+  if (member_->build_strategy_.async_mode_) {
     VLOG(3) << "use AsyncSSAGraphExecutor";
     member_->executor_.reset(new details::AsyncSSAGraphExecutor(
         exec_strategy, member_->local_scopes_, member_->places_, async_graphs));
-  } else if (build_strategy.enable_parallel_graph_) {
+  } else if (member_->build_strategy_.enable_parallel_graph_) {
     VLOG(3) << "use ParallelSSAGraphExecutor";
 #ifdef PADDLE_WITH_CUDA
     // TODO(Yancey1989): Remove passing in the main_program when
@@ -517,7 +519,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   }
 
   VLOG(3) << "use ScopeBufferedSSAGraphExecutor";
-  if (!build_strategy.async_mode_) {
+  if (!member_->build_strategy_.async_mode_) {
     member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
         exec_strategy, member_->local_scopes_, std::move(var_infos),
         member_->places_, std::move(member_->executor_)));

python/paddle/fluid/compiler.py

@@ -274,6 +274,8 @@ class CompiledProgram(object):
                     "share_vars_from is not compiled and run, so there is no "
                     "var to share.")
             self._local_scopes = self._share_vars_from._executor.local_scopes()
+            # drop the local_exe_scopes of the previous parallel_executor
+            self._share_vars_from._executor.drop_local_exe_scopes()
         else:
             assert scope is not None, ""
             self._local_scopes = []