Make MultiProccessRunner rpc_layer defaults to "grpc" if not specified.

PiperOrigin-RevId: 318578551
Change-Id: I59ee41ded51ed8117ec63ef4ed31c8ad4c4fab3d

tensorflow/python/distribute/multi_process_runner.py

@@ -138,7 +138,7 @@ class MultiProcessRunner(object):
                     "worker2.example.com:2222"],
          "ps": ["ps0.example.com:2222",
                 "ps1.example.com:2222"]}
-      rpc_layer: RPC layer to use. Default value is 'grpc+loas'.
+      rpc_layer: RPC layer to use. Default value is 'grpc'.
       max_run_time: If set, child processes is forced to exit at approximately
         this many seconds after `start` is called. We achieve this through
         `signal.alarm()` api. Note that this is best effort at Python level
@@ -184,7 +184,7 @@ class MultiProcessRunner(object):
 
     self._proc_func = proc_func
     self._cluster_spec = cluster_spec
-    self._rpc_layer = rpc_layer
+    self._rpc_layer = rpc_layer or 'grpc'
     self._max_run_time = max_run_time
     self._grpc_fail_fast = grpc_fail_fast
     self._stream_stdout = stream_stdout

tensorflow/python/distribute/strategy_combinations.py

@@ -109,11 +109,15 @@ def _get_multi_worker_mirrored_creator(required_gpus):
 
   def _create_multi_worker_mirrored():
     tf_config = cluster_resolver.TFConfigClusterResolver()
+    master = tf_config.master()
+    if tf_config.rpc_layer:
+      # Strip off the rpc_layer suffix.
+      master = master[len("%s://" % tf_config.rpc_layer):]
     resolver = cluster_resolver.SimpleClusterResolver(
         cluster_spec=tf_config.cluster_spec(),
         task_type=tf_config.task_type,
         task_id=tf_config.task_id,
-        master=tf_config.master(),
+        master=master,
         environment=tf_config.environment,
         num_accelerators={"GPU": required_gpus},
         rpc_layer=tf_config.rpc_layer or "grpc",