polish function name

paddle/fluid/framework/parallel_executor.cc

@@ -95,7 +95,7 @@ ParallelExecutor::ParallelExecutor(
   }
 
   if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
-    BCastParamsToDevs(bcast_vars);
+    BCastParamsToDevices(bcast_vars);
   }
   // Startup Program has been run. All local scopes has correct parameters.
 
@@ -131,7 +131,7 @@ ParallelExecutor::ParallelExecutor(
       member_->places_, std::move(member_->executor_)));
 }
 
-void ParallelExecutor::BCastParamsToDevs(
+void ParallelExecutor::BCastParamsToDevices(
     const std::unordered_set<std::string> &vars) const {
   // the the initializing bcast, all vars would be bcast from device(0),
   // otherwise

paddle/fluid/framework/parallel_executor.h

@@ -66,7 +66,7 @@ class ParallelExecutor {
   void Run(const std::vector<std::string> &fetch_tensors,
            const std::string &fetched_var_name);
 
-  void BCastParamsToDevs(const std::unordered_set<std::string> &vars) const;
+  void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
 
  private:
   ParallelExecutorPrivate *member_;

paddle/fluid/pybind/pybind.cc

@@ -665,7 +665,7 @@ All parameter, weight, gradient are variables in Paddle.
                   const std::string &, Scope *, std::vector<Scope *> &,
                   const ExecutionStrategy &, const BuildStrategy &, size_t,
                   size_t>())
-      .def("bcast_params", &ParallelExecutor::BCastParamsToDevs)
+      .def("bcast_params", &ParallelExecutor::BCastParamsToDevices)
       // NOTE: even we return a vec<Scope*>* to Python use reference policy.
       // We still cannot get local_scope from this vector, since the element
       // of vec<Scope*> will be freed by Python GC. We can only return Scope*