Change `customize_loss_grad` to `use_default_grad_scale`.

paddle/fluid/framework/parallel_executor.cc

@@ -58,7 +58,7 @@ ParallelExecutor::ParallelExecutor(
     const std::unordered_set<std::string> &bcast_vars,
     const ProgramDesc &main_program, const std::string &loss_var_name,
     Scope *scope, const std::vector<Scope *> &local_scopes, bool allow_op_delay,
-    bool customize_scale_loss)
+    bool use_default_grad_scale)
     : member_(new ParallelExecutorPrivate(places)) {
   member_->global_scope_ = scope;
 
@@ -93,11 +93,11 @@ ParallelExecutor::ParallelExecutor(
 #ifdef PADDLE_WITH_CUDA
   details::MultiDevSSAGraphBuilder builder(
       member_->places_, loss_var_name, params, member_->local_scopes_,
-      customize_scale_loss, member_->nccl_ctxs_.get());
+      use_default_grad_scale, member_->nccl_ctxs_.get());
 #else
   details::MultiDevSSAGraphBuilder builder(member_->places_, loss_var_name,
                                            params, member_->local_scopes_,
-                                           customize_scale_loss);
+                                           use_default_grad_scale);
 #endif
   auto graph = builder.Build(main_program);
 

paddle/fluid/framework/parallel_executor.h

@@ -40,7 +40,7 @@ class ParallelExecutor {
                             const ProgramDesc& main_program,
                             const std::string& loss_var_name, Scope* scope,
                             const std::vector<Scope*>& local_scopes,
-                            bool allow_op_delay, bool customize_scale_loss);
+                            bool allow_op_delay, bool use_default_grad_scale);
 
   ~ParallelExecutor();
 

paddle/fluid/pybind/pybind.cc

@@ -502,11 +502,11 @@ All parameter, weight, gradient are variables in Paddle.
               const std::unordered_set<std::string> &bcast_vars,
               const ProgramDesc &main_program, const std::string &loss_var_name,
               Scope *scope, std::vector<Scope *> &local_scopes,
-              bool allow_op_delay, bool customize_loss_grad) {
-             new (&self) ParallelExecutor(num_threads, use_event, places,
-                                          params, bcast_vars, main_program,
-                                          loss_var_name, scope, local_scopes,
-                                          allow_op_delay, customize_loss_grad);
+              bool allow_op_delay, bool use_default_grad_scale) {
+             new (&self) ParallelExecutor(
+                 num_threads, use_event, places, params, bcast_vars,
+                 main_program, loss_var_name, scope, local_scopes,
+                 allow_op_delay, use_default_grad_scale);
            })
       .def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
       // NOTE: even we return a vec<Scope*>* to Python use reference policy.

python/paddle/fluid/parallel_executor.py

@@ -30,7 +30,7 @@ class ParallelExecutor(object):
                  num_threads=None,
                  allow_op_delay=False,
                  share_vars_from=None,
-                 customize_loss_grad=False):
+                 use_default_grad_scale=True):
         """
         ParallelExecutor can run program in parallel.
 
@@ -46,6 +46,10 @@ class ParallelExecutor(object):
                 improve performance in some cases, defalut False.
             share_vars_from(ParallelExecutor, default None): If provied,
                 it will share variables from the specified ParallelExecutor.
+            use_default_grad_scale(bool, default True): If set True, a default
+                scale value equal to `1./device_count` would be multiplied to
+                the gradients. Otherwise, a customized scale value should be
+                feeded to the network.
 
         Returns:
             A ParallelExecutor object.
@@ -124,7 +128,7 @@ class ParallelExecutor(object):
             scope,
             local_scopes,
             allow_op_delay,
-            customize_loss_grad)
+            use_default_grad_scale)
         self.scope = scope
 
     def run(self, fetch_list, feed=None, feed_dict=None):