polish checkpoint docstring (#637)

colossalai/utils/checkpointing.py

@@ -23,9 +23,10 @@ def broadcast_state_dict(state_dict, parallel_mode):
     return state_dict[0]
 
 
-def partition_tensor_parallel_state_dict(
-    state_dict: OrderedDict, parallel_mode: ParallelMode, dims: dict = dict(), partition_states: dict = dict()
-):
+def partition_tensor_parallel_state_dict(state_dict: OrderedDict,
+                                         parallel_mode: ParallelMode,
+                                         dims: dict = dict(),
+                                         partition_states: dict = dict()):
     src_rank = gpc.get_ranks_in_group(parallel_mode)[0]
     depth = gpc.get_world_size(parallel_mode)
 
@@ -124,11 +125,8 @@ def partition_pipeline_parallel_state_dict(model, state_dict):
 
 
 def gather_pipeline_parallel_state_dict(state_dict):
-    gathered_states = (
-        [None for _ in range(gpc.get_world_size(ParallelMode.PIPELINE))]
-        if gpc.get_local_rank(ParallelMode.PIPELINE) == 0
-        else None
-    )
+    gathered_states = ([None for _ in range(gpc.get_world_size(ParallelMode.PIPELINE))]
+                       if gpc.get_local_rank(ParallelMode.PIPELINE) == 0 else None)
     dist.gather_object(
         state_dict,
         gathered_states,
@@ -136,23 +134,18 @@ def gather_pipeline_parallel_state_dict(state_dict):
         group=gpc.get_cpu_group(ParallelMode.PIPELINE),
     )
 
-    state_dict = (
-        OrderedDict(chain.from_iterable(state.items() for state in gathered_states))
-        if gpc.get_local_rank(ParallelMode.PIPELINE) == 0
-        else OrderedDict()
-    )
+    state_dict = (OrderedDict(chain.from_iterable(state.items() for state in gathered_states))
+                  if gpc.get_local_rank(ParallelMode.PIPELINE) == 0 else OrderedDict())
 
     return state_dict
 
 
-def save_checkpoint(
-    file,
+def save_checkpoint(file,
                     epoch: int,
                     model: torch.nn.Module,
                     optimizer: torch.optim.Optimizer = None,
                     lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
-    **kwargs
-):
+                    **kwargs):
     """Stores the checkpoint to disk. Saves all the training components' parameters or buffers, such as model, optimizer,
     lr_scheduler etc. into a checkpoint dictionary.
 
@@ -162,8 +155,8 @@ def save_checkpoint(
         epoch (int): Epoch number (indicates how many epochs have you trained this model).
         model (:class:`torch.nn.Module`): Model to be saved.
         optimizer (Union[:class:`torch.optim.Optimizer`, :class:`colossalai.nn.optimizer`]): Optimizer to be saved.
-        lr_scheduler (Union[:class:`torch.optim.lr_scheduler`,
-            :class:`colossalai.nn.lr_scheduler`], optional): lr_scheduler to be saved, defaults to None.
+        lr_scheduler (Union[:class:`torch.optim.lr_scheduler`, :class:`colossalai.nn.lr_scheduler`], optional):
+            lr_scheduler to be saved, defaults to None.
         pickle_module: module used for pickling metadata and objects
         pickle_protocol: can be specified to override the default protocol
     """
@@ -211,9 +204,8 @@ def load_checkpoint(
     Raises:
         RuntimeError: Raise error if the model/optimizer cannot successfully be recuperated
     """
-    state_dict = (
-        torch.load(file, map_location=torch.device("cpu")) if gpc.get_local_rank(ParallelMode.MODEL) == 0 else None
-    )
+    state_dict = (torch.load(file, map_location=torch.device("cpu"))
+                  if gpc.get_local_rank(ParallelMode.MODEL) == 0 else None)
 
     # model states
     model_state = state_dict.pop("model") if state_dict is not None else dict()
@@ -231,11 +223,8 @@ def load_checkpoint(
             dist.gather_object(error_msgs, all_error_msgs, dst=dst_rank, group=gpc.get_cpu_group(ParallelMode.MODEL))
             if gpc.get_global_rank() == 0:
                 all_error_msgs = list(chain.from_iterable(all_error_msgs))
-                raise RuntimeError(
-                    "Error(s) in loading state_dict for {}:\n\t{}".format(
-                        model.__class__.__name__, "\n\t".join(all_error_msgs)
-                    )
-                )
+                raise RuntimeError("Error(s) in loading state_dict for {}:\n\t{}".format(
+                    model.__class__.__name__, "\n\t".join(all_error_msgs)))
         else:
             raise e