Polish parallel api impl & doc details (#28980)

* polish parallel api impl & doc details

* add unittest for coverage

* remove spawn test in py2.7

* add parallel api into white list

python/paddle/distributed/parallel.py

@@ -32,6 +32,17 @@ __all__ = ["init_parallel_env"]
 
 ParallelStrategy = core.ParallelStrategy
 
+# NOTE(chenweihang): Maintain a global parallel env to avoid 
+# initializing ParallelEnv every time and improve performance
+_global_parallel_env = None
+
+
+def _get_global_parallel_env():
+    global _global_parallel_env
+    if _global_parallel_env is None:
+        _global_parallel_env = ParallelEnv()
+    return _global_parallel_env
+
 
 def _start_kv_server(port, http_server_d):
     from paddle.distributed.fleet.utils.http_server import KVServer
@@ -48,8 +59,7 @@ def init_parallel_env():
     Initialize parallel training environment in dynamic graph mode.
 
     .. note::
-        Now only supports initializing the GPU parallel training 
-        environment and using NCCL for communication.
+        Now initialize both `NCCL` and `GLOO` contexts for communication.
 
     Returns:
         None
@@ -72,13 +82,10 @@ def init_parallel_env():
                     return self._linear2(self._linear1(x))
 
             def train():
-                # 1. enable dynamic mode
-                paddle.disable_static()
-                
-                # 2. initialize parallel environment
+                # 1. initialize parallel environment
                 dist.init_parallel_env()
 
-                # 3. create data parallel layer & optimizer
+                # 2. create data parallel layer & optimizer
                 layer = LinearNet()
                 dp_layer = paddle.DataParallel(layer)
 
@@ -86,7 +93,7 @@ def init_parallel_env():
                 adam = opt.Adam(
                     learning_rate=0.001, parameters=dp_layer.parameters())
 
-                # 4. run layer
+                # 3. run layer
                 inputs = paddle.randn([10, 10], 'float32')
                 outputs = dp_layer(inputs)
                 labels = paddle.randn([10, 1], 'float32')
@@ -101,6 +108,18 @@ def init_parallel_env():
                 dist.spawn(train)
     """
 
+    # 0. get env & check world size
+    global _global_parallel_env
+    # when call init_parallel_env, need update `_global_parallel_env`
+    _global_parallel_env = ParallelEnv()
+    parallel_env = _global_parallel_env
+    # if not parallel, `init_parallel_env` do nothing
+    if parallel_env.world_size < 2:
+        warnings.warn(
+            "Currently not a parallel execution environment, `paddle.distributed.init_parallel_env` will not do anything."
+        )
+        return
+
     # 1. gpu check
     if not core.is_compiled_with_cuda():
         raise NotImplementedError(
@@ -122,17 +141,14 @@ def init_parallel_env():
     _check_var_exists("PADDLE_TRAINERS_NUM")
     _check_var_exists("PADDLE_TRAINER_ENDPOINTS")
 
-    if ParallelEnv().world_size < 2:
-        return
-
     # 3: init gloo context (step 1: httpsever start)
-    ep_rank_0 = ParallelEnv().trainer_endpoints[0].split(":")
-    ep_rank = ParallelEnv().trainer_endpoints[ParallelEnv().rank].split(":")
+    ep_rank_0 = parallel_env.trainer_endpoints[0].split(":")
+    ep_rank = parallel_env.trainer_endpoints[parallel_env.rank].split(":")
     manager = Manager()
     # glboal dict to store status
     http_server_d = manager.dict()
     http_server_d["running"] = False
-    if ParallelEnv().rank == 0:
+    if parallel_env.rank == 0:
         http_server = Process(
             target=_start_kv_server, args=(int(ep_rank_0[1]), http_server_d))
         http_server.daemon = True
@@ -143,10 +159,10 @@ def init_parallel_env():
     strategy = ParallelStrategy()
     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
+    strategy.nranks = parallel_env.world_size
+    strategy.local_rank = parallel_env.rank
+    strategy.trainer_endpoints = parallel_env.trainer_endpoints
+    strategy.current_endpoint = parallel_env.current_endpoint
 
     # NOTE(chenweihang): [ why config global place here? ]
     # the dygraph mode will be set to default mode,
@@ -154,7 +170,7 @@ def init_parallel_env():
     # 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)
+    place = core.CUDAPlace(parallel_env.device_id)
     _set_expected_place(place)
 
     # init nccl context
@@ -165,11 +181,11 @@ def init_parallel_env():
     # dividing init_gloo into two part beacause nccl and gloo
     # are separately looking for free ports which sometimes
     # leads to port-conflict.
-    wait_server_ready([ParallelEnv().trainer_endpoints[0]])
+    wait_server_ready([parallel_env.trainer_endpoints[0]])
 
     gloo_strategy = core.GlooParallelStrategy()
-    gloo_strategy.rank = ParallelEnv().rank
-    gloo_strategy.rank_num = ParallelEnv().world_size
+    gloo_strategy.rank = parallel_env.rank
+    gloo_strategy.rank_num = parallel_env.world_size
     gloo_strategy.ip_address = ep_rank_0[0]
     gloo_strategy.ip_port = int(ep_rank_0[1])
     default_init_timeout_seconds = 3600
@@ -178,7 +194,7 @@ def init_parallel_env():
     gloo_strategy.run_seconds = default_run_timeout_seconds
     gloo = core.GlooParallelContext(gloo_strategy)
     gloo.init()
-    if ParallelEnv().rank == 0:
+    if parallel_env.rank == 0:
         http_server_d["running"] = False
         http_server.join()
 
@@ -203,7 +219,7 @@ def get_rank():
             print("The rank is %d" % dist.get_rank())
             # The rank is 0
     """
-    return ParallelEnv().rank
+    return _get_global_parallel_env().rank
 
 
 def get_world_size():
@@ -226,4 +242,4 @@ def get_world_size():
             print("The world_size is %d" % dist.get_world_size())
             # The world_size is 4
     """
-    return ParallelEnv().world_size
+    return _get_global_parallel_env().world_size

python/paddle/distributed/spawn.py

@@ -68,6 +68,18 @@ def _py_supported_check():
             "`paddle.distributed.launch` instead.")
 
 
+def _options_valid_check(options):
+    supported_options = [
+        'start_method', 'cluster_node_ips', 'node_ip', 'started_port',
+        'selected_gpus', 'print_config', 'use_paddlecloud'
+    ]
+    for key in options:
+        if key not in supported_options:
+            raise ValueError(
+                "The config option (%s) of `paddle.distributed.spawn` is not supported."
+                % key)
+
+
 def _get_subprocess_env_list(nprocs, options):
     # contruct processes env list
     processes_env_list = []
@@ -290,14 +302,11 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
                 def forward(self, x):
                     return self._linear2(self._linear1(x))
 
-            def train(print_result=False):
-                # 1. enable dynamic mode
-                paddle.disable_static()
-                
-                # 2. initialize parallel environment
+            def train(print_result=False): 
+                # 1. initialize parallel environment
                 dist.init_parallel_env()
 
-                # 3. create data parallel layer & optimizer
+                # 2. create data parallel layer & optimizer
                 layer = LinearNet()
                 dp_layer = paddle.DataParallel(layer)
 
@@ -305,7 +314,7 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
                 adam = opt.Adam(
                     learning_rate=0.001, parameters=dp_layer.parameters())
 
-                # 4. run layer
+                # 3. run layer
                 inputs = paddle.randn([10, 10], 'float32')
                 outputs = dp_layer(inputs)
                 labels = paddle.randn([10, 1], 'float32')
@@ -344,13 +353,13 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
             # Usage 4: pass function, arguments, nprocs and selected_gpus.
             # If your training method need some arguments, and 
             # only use part of visible devices for parallel training,
-            # but you can't set your machine's environment varibale 
+            # but you can't set your machine's environment variable 
             # CUDA_VISIBLE_DEVICES, such as it is None or all cards
-            # {0,1,2,3,4,5,6,7}, you can pass `selelcted_gpus` to 
+            # {0,1,2,3,4,5,6,7}, you can pass `selected_gpus` to 
             # select the GPU cards you want to use. For example,
             # this case will use cards {4,5} if your machine hold 8 cards.
             if __name__ == '__main__':
-                dist.spawn(train, args=(True,), nprocs=2, selelcted_gpus='4,5')
+                dist.spawn(train, args=(True,), nprocs=2, selected_gpus='4,5')
     """
     # NOTE(chenweihang): [ why only supports python3.4+ ? ]
     # Python supported setting the child process startup method
@@ -359,6 +368,10 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
     # cannot support CUDA runtime multi-process
     _py_supported_check()
 
+    # Give an error hint when the users enter a configuration option 
+    # that does not exist
+    _options_valid_check(options)
+
     # get default nprocs
     if nprocs == -1:
         device = get_device()

python/paddle/fluid/dygraph/parallel.py

@@ -377,13 +377,10 @@ class DataParallel(layers.Layer):
                     return self._linear2(self._linear1(x))
 
             def train():
-                # 1. enable dynamic mode
-                paddle.disable_static()
-                
-                # 2. initialize parallel environment
+                # 1. initialize parallel environment
                 dist.init_parallel_env()
 
-                # 3. create data parallel layer & optimizer
+                # 2. create data parallel layer & optimizer
                 layer = LinearNet()
                 dp_layer = paddle.DataParallel(layer)
 
@@ -391,7 +388,7 @@ class DataParallel(layers.Layer):
                 adam = opt.Adam(
                     learning_rate=0.001, parameters=dp_layer.parameters())
 
-                # 4. run layer
+                # 3. run layer
                 inputs = paddle.randn([10, 10], 'float32')
                 outputs = dp_layer(inputs)
                 labels = paddle.randn([10, 1], 'float32')
@@ -450,28 +447,28 @@ class DataParallel(layers.Layer):
                    include_sublayers=True,
                    structured_name_prefix=""):
         '''
-        Get all parameters of self._layers and its sub-layers. And set all the parameters into a dict
+        Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict
 
         Parameters:
-            destination(dict, optional) : If provide, all the parameters will set to this dict . Default: None
-            include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True
-            structured_name_prefix(str, optional): If not empty str, all the key in state dict will start 
-                                                 with structured_name_prefix
+            destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None
+            include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True
 
         Retruns:
-            dict: a dict contains all the parameters of self._layers
+            dict: a dict contains all the parameters and persistable buffers.
 
         Examples:
             .. code-block:: python
 
-                import paddle.fluid as fluid
-                with fluid.dygraph.guard():
-                    strategy=fluid.dygraph.prepare_context()
-                    emb = fluid.dygraph.Embedding([10, 10])
-                    emb = fluid.dygraph.DataParallel(emb, strategy)
+                import paddle
+                import paddle.distributed as dist
+
+                dist.init_parallel_env()
+
+                emb = fluid.dygraph.Embedding([10, 10])
+                emb = fluid.dygraph.DataParallel(emb)
 
-                    state_dict = emb.state_dict()
-                    fluid.save_dygraph( state_dict, "paddle_dy")
+                state_dict = emb.state_dict()
+                paddle.save(state_dict, "paddle_dy.pdparams")
 
         '''
 
@@ -486,12 +483,12 @@ class DataParallel(layers.Layer):
                        include_sublayers=True,
                        use_structured_name=True):
         '''
-        Set parameters of self._layers from state_dict. All the parameters of self._layers will be reset by the tensor in the state_dict
+        Set parameters and persistable buffers from state_dict. All the parameters and buffers will be reset by the tensor in the state_dict
 
         Parameters:
-            state_dict(dict) : Dict contains all the parameters
-            include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True
-            use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter name as key. 
+            state_dict(dict) : Dict contains all the parameters and persistable buffers.
+            include_sublayers(bool, optional) : If true, also include the parameters and peresistable buffers from sublayers. Default: True
+            use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter or buffer name as key. 
                                                   Default: True
         Returns:
             None
@@ -499,18 +496,18 @@ class DataParallel(layers.Layer):
         Examples:
             .. code-block:: python
 
-                import paddle   
+                import paddle
+                import paddle.distributed as dist
 
-                paddle.disable_static()
+                dist.init_parallel_env()
 
                 emb = paddle.nn.Embedding(10, 10)
-                emb = fluid.dygraph.DataParallel(emb, strategy)
+                emb = fluid.dygraph.DataParallel(emb)
 
                 state_dict = emb.state_dict()
                 paddle.save(state_dict, "paddle_dy.pdparams")
 
                 para_state_dict = paddle.load("paddle_dy.pdparams")
-
                 emb.set_state_dict(para_state_dict)
 
         '''

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

@@ -37,7 +37,7 @@ class TestInitParallelEnv(unittest.TestCase):
         os.environ['FLAGS_selected_gpus'] = '0'
         os.environ['PADDLE_TRAINER_ID'] = '0'
         os.environ['PADDLE_CURRENT_ENDPOINT'] = '127.0.0.1:6170'
-        os.environ['PADDLE_TRAINERS_NUM'] = '1'
+        os.environ['PADDLE_TRAINERS_NUM'] = '2'
         with self.assertRaises(ValueError):
             dist.init_parallel_env()
 

tools/wlist.json

@@ -379,6 +379,8 @@
         "While.block",
         "DGCMomentumOptimizer",
         "ParallelEnv",
+        "spawn",
+        "init_parallel_env",
         "DataParallel",
         "DataParallel.scale_loss",
         "DataParallel.apply_collective_grads",