'fix/format' (#573)

colossalai/engine/ophooks/_memtracer_ophook.py

@@ -106,7 +106,7 @@ class MemTracerOpHook(BaseOpHook):
                 # output file info
                 self._logger.info(f"dump a memory statistics as pickle to {self._data_prefix}-{self._rank}.pkl")
                 home_dir = Path.home()
-                with open (home_dir.joinpath(f".cache/colossal/mem-{self._rank}.pkl"), "wb") as f:
+                with open(home_dir.joinpath(f".cache/colossal/mem-{self._rank}.pkl"), "wb") as f:
                     pickle.dump(self.async_mem_monitor.state_dict, f)
                 self._count += 1
                 self._logger.debug(f"data file has been refreshed {self._count} times")
@@ -115,4 +115,4 @@ class MemTracerOpHook(BaseOpHook):
 
     def save_results(self, data_file: Union[str, Path]):
         with open(data_file, "w") as f:
-            f.write(json.dumps(self.async_mem_monitor.state_dict))
+            f.write(json.dumps(self.async_mem_monitor.state_dict))

colossalai/engine/schedule/_base_schedule.py

@@ -85,8 +85,7 @@ class BaseSchedule(ABC):
                               data_iter: Iterable,
                               forward_only: bool,
                               return_loss: bool = True,
-                              return_output_label: bool = True
-                              ):
+                              return_output_label: bool = True):
         """The process function over a batch of dataset for training or evaluation.
 
         Args:
@@ -107,8 +106,9 @@ class BaseSchedule(ABC):
 
     @staticmethod
     def _call_engine_criterion(engine, outputs, labels):
-        assert isinstance(outputs, (torch.Tensor, list, tuple)
-                          ), f'Expect output of model is (torch.Tensor, list, tuple), got {type(outputs)}'
+        assert isinstance(
+            outputs,
+            (torch.Tensor, list, tuple)), f'Expect output of model is (torch.Tensor, list, tuple), got {type(outputs)}'
         if isinstance(outputs, torch.Tensor):
             outputs = (outputs,)
         if isinstance(labels, torch.Tensor):