Done, need support selectedrows

python/paddle/v2/fluid/distribute_transpiler.py

@@ -148,7 +148,7 @@ class DistributeTranspiler:
             concat = program.global_block().append_op(
                 type="concat",
                 inputs={"X": splited_var},
-                outputs={"Out": orig_param},
+                outputs={"Out": [orig_param]},
                 attrs={"axis": 0})
 
     def _create_vars_from_blocklist(self, program, block_list):
@@ -420,7 +420,6 @@ class DistributeTranspiler:
             else:
                 self._append_pserver_non_opt_ops(optimize_sub_program,
                                                  pserver_program, opt_op)
-        print("****subprogram", optimize_sub_program)
         pserver_program.global_block().append_op(
             type="recv",
             inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
@@ -463,7 +462,6 @@ class DistributeTranspiler:
         pserver_vars = pserver_program.global_block().vars
         created_var_map = dict()
         for _, var in pserver_vars.iteritems():
-            print("create var for startup", var.name, var.shape)
             tmpvar = s_prog.global_block().create_var(
                 name=var.name,
                 persistable=var.persistable,
@@ -485,21 +483,11 @@ class DistributeTranspiler:
                     op_on_pserver = True
                     new_outputs[key] = pserver_vars[var.name]
 
-                # newname, _ = _get_splited_name_and_shape(var.name)
-                # if newname:
-                #     print("updating output", newname, created_var_map[newname])
-                #     new_outputs[key] = created_var_map[newname]
-                # else:
-                #     print("no update output", key, var)
-                #     new_outputs[key] = var
-                # if var.name in created_var_map or \
-                #     newname:
-                #     op_on_pserver = True
-
             if op_on_pserver:
-                if op.type in ["gaussian_random", "fill_constant"]:
+                if op.type in [
+                        "gaussian_random", "fill_constant", "uniform_random"
+                ]:
                     op.attrs["shape"] = new_outputs["Out"].shape
-                print("updated shape", op.attrs["shape"])
                 s_prog.global_block().append_op(
                     type=op.type,
                     inputs=op.inputs,