Change customize_loss_grad to use_default_grad_scale.

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -34,7 +34,7 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
     const std::vector<platform::Place> &places,
     const std::string &loss_var_name,
     const std::unordered_set<std::string> &params,
-    const std::vector<Scope *> &local_scopes, bool skip_scale_loss,
+    const std::vector<Scope *> &local_scopes, bool use_default_grad_scale,
     platform::NCCLContextMap *nccl_ctxs)
     : loss_var_name_(loss_var_name),
       places_(places),
@@ -45,15 +45,15 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
     const std::vector<platform::Place> &places,
     const std::string &loss_var_name,
     const std::unordered_set<std::string> &params,
-    const std::vector<Scope *> &local_scopes, bool skip_scale_loss)
+    const std::vector<Scope *> &local_scopes, bool use_default_grad_scale)
     : loss_var_name_(loss_var_name),
       places_(places),
       local_scopes_(local_scopes) {
 #endif
   for (auto &p : params) {
     grad_names_.insert(GradVarName(p));
   }
-  skip_scale_loss_ = skip_scale_loss;
+  use_default_grad_scale_ = use_default_grad_scale;
 }
 
 void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
@@ -126,8 +126,8 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
     } else if (IsDistTrainOp(*op, send_op)) {
       CreateComputationalOps(&result, *op, 1);
     } else if (IsScaleLossOp(*op)) {
-      // user can customize loss@grad if skip_scale_loss_
-      if (!skip_scale_loss_) {
+      // user can customize loss@grad if not use_default_grad_scale_
+      if (use_default_grad_scale_) {
         CreateScaleLossGradOp(&result);
       }
       is_forwarding = false;

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -41,7 +41,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
                           const std::string &loss_var_name,
                           const std::unordered_set<std::string> &params,
                           const std::vector<Scope *> &local_scopes,
-                          bool skip_scale_loss);
+                          bool use_default_grad_scale);
 #endif
 
   std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
@@ -59,7 +59,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
 #ifdef PADDLE_WITH_CUDA
   platform::NCCLContextMap *nccl_ctxs_;
 #endif
-  bool skip_scale_loss_;
+  bool use_default_grad_scale_;
 
   bool IsScaleLossOp(const OpDesc &op) const;
 

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 @@ def __init__(self,
                  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 +46,11 @@ def __init__(self,
                 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
+                gradients of each device and scaled gradients would be
+                aggregated. Otherwise, a customized scale value should be fed
+                to the network.
 
         Returns:
             A ParallelExecutor object.
@@ -124,7 +129,7 @@ def __init__(self,
             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):