[NFC] polish applications/Chat/coati/trainer/base.py code style

applications/Chat/coati/trainer/base.py

@@ -25,12 +25,13 @@ class SLTrainer(ABC):
         optim (Optimizer): the optimizer to use for training
     """
 
-    def __init__(self,
-                 strategy: Strategy,
-                 max_epochs: int,
-                 model: nn.Module,
-                 optimizer: Optimizer,
-                 ) -> None:
+    def __init__(
+        self,
+        strategy: Strategy,
+        max_epochs: int,
+        model: nn.Module,
+        optimizer: Optimizer,
+    ) -> None:
         super().__init__()
         self.strategy = strategy
         self.max_epochs = max_epochs
@@ -50,10 +51,7 @@ def _before_fit(self):
 
     def fit(self, *args, **kwargs):
         self._before_fit(*args, **kwargs)
-        for epoch in tqdm.trange(self.max_epochs,
-                                 desc="Epochs",
-                                 disable=not is_rank_0() or self.no_epoch_bar
-                                 ):
+        for epoch in tqdm.trange(self.max_epochs, desc="Epochs", disable=not is_rank_0() or self.no_epoch_bar):
             self._train(epoch)
             self._eval(epoch)
 
@@ -75,8 +73,7 @@ def __init__(self,
                  buffer: NaiveReplayBuffer,
                  sample_buffer: bool,
                  dataloader_pin_memory: bool,
-                 callbacks: List[Callback] = []
-                 ) -> None:
+                 callbacks: List[Callback] = []) -> None:
         super().__init__()
         self.strategy = strategy
         self.buffer = buffer
@@ -138,7 +135,7 @@ def _make_experience(self, collect_step: int):
     @abstractmethod
     def _learn(self, update_step: int):
         """
-        Implement this method to learn from experience, either 
+        Implement this method to learn from experience, either
         sample from buffer or transform buffer into dataloader.
         """
         raise NotImplementedError()
@@ -154,13 +151,14 @@ def _update_phase(self, update_step: int):
         self._learn(update_step)
         self._on_learn_epoch_end(update_step)
 
-    def fit(self,
-            prompt_dataloader: DataLoader,
-            pretrain_dataloader: DataLoader,
-            num_episodes: int,
-            num_collect_steps: int,
-            num_update_steps: int,
-            ):
+    def fit(
+        self,
+        prompt_dataloader: DataLoader,
+        pretrain_dataloader: DataLoader,
+        num_episodes: int,
+        num_collect_steps: int,
+        num_update_steps: int,
+    ):
         """
         The main training loop of on-policy rl trainers.
 
@@ -175,23 +173,16 @@ def fit(self,
         self.pretrain_dataloader = CycledDataLoader(pretrain_dataloader)
 
         with self._fit_ctx():
-            for episode in tqdm.trange(num_episodes,
-                                       desc="Episodes",
-                                       disable=not is_rank_0()):
+            for episode in tqdm.trange(num_episodes, desc="Episodes", disable=not is_rank_0()):
                 with self._episode_ctx(episode):
-                    for collect_step in tqdm.trange(num_collect_steps,
-                                                    desc="Collect steps",
-                                                    disable=not is_rank_0()):
+                    for collect_step in tqdm.trange(num_collect_steps, desc="Collect steps", disable=not is_rank_0()):
                         self._collect_phase(collect_step)
                     if not self.sample_buffer:
                         # HACK(cwher): according to the design of boost API, dataloader should also be boosted,
                         #  but it is impractical to adapt this pattern in RL training. Thus, I left dataloader unboosted.
                         #  I only call strategy.setup_dataloader() to setup dataloader.
-                        self.dataloader = self.strategy.setup_dataloader(self.buffer,
-                                                                         self.dataloader_pin_memory)
-                    for update_step in tqdm.trange(num_update_steps,
-                                                   desc="Update steps",
-                                                   disable=not is_rank_0()):
+                        self.dataloader = self.strategy.setup_dataloader(self.buffer, self.dataloader_pin_memory)
+                    for update_step in tqdm.trange(num_update_steps, desc="Update steps", disable=not is_rank_0()):
                         self._update_phase(update_step)
                     # NOTE: this is for on-policy algorithms
                     self.buffer.clear()