Remove backend argument of init_parallel_env

python/paddle/distributed/parallel.py

@@ -29,13 +29,13 @@
 ParallelStrategy = core.ParallelStrategy
 
 
-def init_parallel_env(backend='nccl'):
+def init_parallel_env():
     """
-    Initialize parallel training environments in dynamic mode.
+    Initialize parallel training environment in dynamic graph mode.
 
-    Args:
-        backend(str, optional): The backend to communication between multiple devices.
-            Now only support ``nccl`` . Default value is ``nccl`` .
+    .. note::
+        Now only supports initializing the GPU parallel training 
+        environment and using NCCL for communication.
 
     Returns:
         None
@@ -89,14 +89,12 @@ def train():
                 dist.spawn(train)
     """
 
-    # 1. input check
-    if not isinstance(backend, six.string_types):
-        raise TypeError("input `backend` type error, expected type is str, "
-                        "but received type is %s." % type(backend))
-    if cpt.to_text(backend) != 'nccl':
-        raise ValueError(
-            "backend `%s` is not supported, now only supports `nccl` backend." %
-            backend)
+    # 1. gpu check
+    if not core.is_compiled_with_cuda():
+        raise NotImplementedError(
+            "Cannot initialize parallel environment in CPU-only version, now only "
+            "supports initializing the GPU parallel environment. Please recompile "
+            "or reinstall paddle with GPU support.")
 
     # 2. check env
     def _check_var_exists(var_name):
@@ -112,30 +110,28 @@ def _check_var_exists(var_name):
     _check_var_exists("PADDLE_TRAINERS_NUM")
     _check_var_exists("PADDLE_TRAINER_ENDPOINTS")
 
-    # 3. init ParallelStrategy
+    # 3. init NCCL ParallelStrategy
     strategy = ParallelStrategy()
-    if cpt.to_text(backend) == 'nccl':
-        if parallel_helper._is_parallel_ctx_initialized():
-            warnings.warn("The parallel environment has been initialized.")
-        strategy.nranks = ParallelEnv().world_size
-        strategy.local_rank = ParallelEnv().rank
-        strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
-        strategy.current_endpoint = ParallelEnv().current_endpoint
-        if strategy.nranks < 2:
-            return
-        # NOTE(chenweihang): [ why config global place here? ]
-        # the dygraph mode will be set to default mode, 
-        # users will not call `dygraph.guard` or `enable_dygraph`
-        # directly, if they want to switch default place,
-        # they need to call a function to change default place,
-        # here just set correctly place to users
-        place = core.CUDAPlace(ParallelEnv().device_id)
-        _set_expected_place(place)
-
-        # init nccl context
-        parallel_helper._set_parallel_ctx(
-            core.NCCLParallelContext(strategy, place))
-        parallel_helper._init_parallel_ctx()
+    if parallel_helper._is_parallel_ctx_initialized():
+        warnings.warn("The parallel environment has been initialized.")
+    strategy.nranks = ParallelEnv().world_size
+    strategy.local_rank = ParallelEnv().rank
+    strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
+    strategy.current_endpoint = ParallelEnv().current_endpoint
+    if strategy.nranks < 2:
+        return
+    # NOTE(chenweihang): [ why config global place here? ]
+    # the dygraph mode will be set to default mode, 
+    # users will not call `dygraph.guard` or `enable_dygraph`
+    # directly, if they want to switch default place,
+    # they need to call a function to change default place,
+    # here just set correctly place to users
+    place = core.CUDAPlace(ParallelEnv().device_id)
+    _set_expected_place(place)
+
+    # init nccl context
+    parallel_helper._set_parallel_ctx(core.NCCLParallelContext(strategy, place))
+    parallel_helper._init_parallel_ctx()
 
 
 def get_rank():
@@ -163,7 +159,7 @@ def get_rank():
 
 def get_world_size():
     """
-    The number of trainers (number of processes participating in current job).
+    Returns the number of trainers (number of processes participating in current job).
 
     Its value is equal to the value of the environment variable ``PADDLE_TRAINERS_NUM`` . 
     The default value is 1.

python/paddle/distributed/spawn.py

@@ -246,8 +246,8 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
             variable CUDA_VISIBLE_DEVICES; If use CPU, the currently available
             CPU number is obtained from the environment variable CPU_NUM. 
             For example, export CPU_NUM=4, if the environment variable is not set, 
-            the executor will add the variable to the environment variable and 
-            set its value to 1.
+            the spawn method will add default value to the environment variable 
+            and set its value to 1.
         join (bool, optional): Perform a blocking join on all spawned processes.
             Default: True.
         daemon (bool, optional): The spawned processes' daemon flag. Default: False.
@@ -266,8 +266,8 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
             such as 6170. Default: None; 
             (5) selected_gpus (string): The training process will run on the 
             selected_gpus, such as "0,1,2,3". Default: None; 
-            (6) print_config: Print current parallel training config. Default: False;
-            (7) use_paddlecloud: Whether to use paddlecloud platform to run your 
+            (6) print_config (bool): Print current parallel training config. Default: False;
+            (7) use_paddlecloud (bool): Whether to use paddlecloud platform to run your 
             multi-process job. Default: False.
 
     Returns:

python/paddle/fluid/tests/unittests/test_spawn_and_init_parallel_env.py

@@ -31,14 +31,6 @@
 
 
 class TestInitParallelEnv(unittest.TestCase):
-    def test_beckend_type_error(self):
-        with self.assertRaises(TypeError):
-            dist.init_parallel_env(backend=1)
-
-    def test_backend_value_error(self):
-        with self.assertRaises(ValueError):
-            dist.init_parallel_env(backend="mpi")
-
     def test_check_env_failed(self):
         os.environ['FLAGS_selected_gpus'] = '0'
         os.environ['PADDLE_TRAINER_ID'] = '0'

python/paddle/framework/__init__.py

@@ -20,8 +20,8 @@
 ]
 
 __all__ += [
-    'grad', 'LayerList', 'load', 'save', 'prepare_context', 'to_variable',
-    'no_grad', 'ParallelEnv', 'DataParallel'
+    'grad', 'LayerList', 'load', 'save', 'to_variable', 'no_grad',
+    'DataParallel'
 ]
 
 __all__ += [