Merge pull request #8409 from typhoonzero/fix_grpc_short_conn

Fix grpc short connection

paddle/fluid/operators/listen_and_serv_op.cc

@@ -82,8 +82,8 @@ class ListenAndServOp : public framework::OperatorBase {
     return string::Sprintf("%s.trainer_%d", varname, grads_counter_[varname]++);
   }
 
-  void Run(const framework::Scope &scope,
-           const platform::Place &dev_place) const override {
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &dev_place) const override {
     platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
     auto &dev_ctx = *pool.Get(dev_place);
     framework::Scope &recv_scope = scope.NewScope();

paddle/fluid/operators/recv_op.cc

@@ -32,8 +32,8 @@ class RecvOp : public framework::OperatorBase {
          const framework::AttributeMap& attrs)
       : OperatorBase(type, inputs, outputs, attrs) {}
 
-  void Run(const framework::Scope& scope,
-           const platform::Place& place) const override {
+  void RunImpl(const framework::Scope& scope,
+               const platform::Place& place) const override {
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
 

paddle/fluid/operators/send_op.cc

@@ -48,8 +48,8 @@ class SendOp : public framework::OperatorBase {
          const framework::AttributeMap& attrs)
       : OperatorBase(type, inputs, outputs, attrs) {}
 
-  void Run(const framework::Scope& scope,
-           const platform::Place& place) const override {
+  void RunImpl(const framework::Scope& scope,
+               const platform::Place& place) const override {
     auto ins = Inputs("X");
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");

python/paddle/v2/fluid/distribute_transpiler.py

@@ -121,7 +121,6 @@ def split_dense_variable(var_list,
                 block_size += dim1 - remains
         # update split_count after aligning
         split_count = int(math.ceil(var_numel / float(block_size)))
-        print("###split var ", var.name, var.shape, block_size, split_count)
         for block_id in xrange(split_count):
             curr_block_size = min(block_size, var_numel - (
                 (block_id) * block_size))
@@ -207,7 +206,7 @@ class DistributeTranspiler:
 
         rpc_client_var = program.global_block().create_var(
             name="RPC_CLIENT_VAR",
-            psersistable=True,
+            persistable=True,
             dtype='float32',  # dtype and shape is not used in fact
             shape=[0])
 
@@ -256,15 +255,13 @@ class DistributeTranspiler:
                 splited_shape = [rows]
                 if len(orig_shape) >= 2:
                     splited_shape.extend(orig_shape[1:])
-                print("###splited: ", size, rows, splited_shape)
                 var = program.global_block().create_var(
                     name="%s.block%d" % (varname, i),
-                    psersistable=False,
+                    persistable=False,
                     dtype=orig_var.dtype,
                     type=orig_var.type,
                     shape=splited_shape)  # flattend splited var
                 var_mapping[varname].append(var)
-                print("###created split var ", var)
         return var_mapping
 
     def _clone_var(self, block, var):
@@ -322,7 +319,7 @@ class DistributeTranspiler:
         for i in xrange(trainers):
             var_each = block.create_var(
                 name="%s.trainer_%d" % (var.name, i),
-                psersistable=var.persistable,
+                persistable=var.persistable,
                 dtype=var.dtype,
                 type=var.type,
                 shape=var.shape)
@@ -531,8 +528,6 @@ class DistributeTranspiler:
         """
         # step5
         pserver_program = Program()
-        print("param mapping on pserver: #### ",
-              self.param_grad_ep_mapping[endpoint]["params"])
         for v in self.param_grad_ep_mapping[endpoint]["params"]:
             self._clone_var(pserver_program.global_block(), v)
         for v in self.param_grad_ep_mapping[endpoint]["grads"]: