skip dist. test=develop

paddle/fluid/framework/details/inplace_op_pass.cc

@@ -301,7 +301,7 @@ void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
 
     // 3. if output has been memory optimize by python(fluid.memory_optmize()).
     // this candidate  can not be inplaced. Will be deprecated in the future.
-    if (view_.ReusedInPythonMemOpt(out_node->Name())) {
+    if (view_.InSkipSet(out_node->Name())) {
       VLOG(4) << string::Sprintf(
           "Skiped %s => %s reused previous memory block in python memory "
           "optmize,"
@@ -385,7 +385,7 @@ void GraphView::Build(ir::Graph* g) {
   // resolve data harzards depends on the var nodes in right order.
   ops_ = SortOpLikeDescOrder(*g);
 
-  // track the nodes which reused previous node in Python memory optimize.
+  // 1. track the nodes which reused previous node in Python memory optimize.
   // these node can not be inplaced, otherwise may generate a circle in graph.
   std::unordered_set<std::string> all_vars;
   for (auto& node : g->Nodes()) {
@@ -399,11 +399,28 @@ void GraphView::Build(ir::Graph* g) {
       }
     }
   }
+
+  // 2. track the nodes which used by parameter server.
+  // these node can not be inplaced, otherwise trainer
+  // pserver can not find each other name.
+  for (auto& node : g->Nodes()) {
+    if (!node->IsOp()) continue;
+    if (node->Name() == "send") {
+      for (auto& in : node->inputs) {
+        dup_nodes_.emplace(in->Name());
+      }
+    }
+    if (node->Name() == "recv") {
+      for (auto& out : node->outputs) {
+        dup_nodes_.emplace(out->Name());
+      }
+    }
+  }
 }
 
 const std::vector<ir::Node*>& GraphView::AllOps() { return ops_; }
 
-bool GraphView::ReusedInPythonMemOpt(const std::string& var) const {
+bool GraphView::InSkipSet(const std::string& var) const {
   return dup_nodes_.count(var);
 }
 

paddle/fluid/framework/details/inplace_op_pass.h

@@ -41,11 +41,14 @@ class GraphView {
   std::vector<ir::Node*> PendingOpsOnVar(ir::Node* var);
 
   // Will Deperated in the future.
-  // NOTE(dzhwinter) : Python memory optimize will reuse
+  // NOTE(dzhwinter) :
+  // 1. Python memory optimize will reuse
   // memory based var name, so different op output may
   // have the same variable name. enable inplace on such node
   // will generate a circle in ssa graph.
-  bool ReusedInPythonMemOpt(const std::string& var) const;
+  // 2. DistributeTranspiler will use unique name to
+  // map the parameter and gradient, must be skipped.
+  bool InSkipSet(const std::string& var) const;
 
  private:
   std::vector<ir::Node*> ops_;