diff --git a/colossalai/nn/lr_scheduler/cosine.py b/colossalai/nn/lr_scheduler/cosine.py
index 179585cd7..aab523bef 100644
--- a/colossalai/nn/lr_scheduler/cosine.py
+++ b/colossalai/nn/lr_scheduler/cosine.py
@@ -62,8 +62,10 @@ class CosineAnnealingWarmupLR(WarmupScheduler):
     """
 
     def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, eta_min: float = 0., last_epoch: int = -1):
-        base_scheduler = _CosineAnnealingLR(
-            optimizer, total_steps - warmup_steps, eta_min=eta_min, last_epoch=last_epoch)
+        base_scheduler = _CosineAnnealingLR(optimizer,
+                                            total_steps - warmup_steps,
+                                            eta_min=eta_min,
+                                            last_epoch=last_epoch)
         super().__init__(optimizer, warmup_steps, base_scheduler)
 
 
@@ -81,12 +83,10 @@ class FlatAnnealingLR(DelayerScheduler):
 
     def __init__(self, optimizer, total_steps: int, pct_start: float = 0.72, last_epoch: int = -1, **kwargs):
         if not (0.0 <= pct_start <= 1.0):
-            raise ValueError(
-                f'pct_start must >= 0.0 and <= 1.0, got {pct_start}')
+            raise ValueError(f'pct_start must >= 0.0 and <= 1.0, got {pct_start}')
         flat_steps = int(total_steps * pct_start)
         anneal_steps = total_steps - flat_steps
-        base_scheduler = _CosineAnnealingLR(
-            optimizer, anneal_steps)
+        base_scheduler = _CosineAnnealingLR(optimizer, anneal_steps)
         super().__init__(optimizer, flat_steps, base_scheduler, last_epoch=last_epoch)
 
 
@@ -105,14 +105,17 @@ class FlatAnnealingWarmupLR(WarmupDelayerScheduler):
             the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
     """
 
-    def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, pct_start: float = 0.72, eta_min: int = 0,
-                 last_epoch: int = -1, **kwargs):
+    def __init__(self,
+                 optimizer,
+                 total_steps: int,
+                 warmup_steps: int = 0,
+                 pct_start: float = 0.72,
+                 eta_min: int = 0,
+                 last_epoch: int = -1,
+                 **kwargs):
         if not (0.0 <= pct_start <= 1.0):
-            raise ValueError(
-                f'pct_start must >= 0.0 and <= 1.0, got {pct_start}')
+            raise ValueError(f'pct_start must >= 0.0 and <= 1.0, got {pct_start}')
         flat_steps = int((total_steps - warmup_steps) * pct_start)
         anneal_steps = total_steps - warmup_steps - flat_steps
-        base_scheduler = _CosineAnnealingLR(
-            optimizer, anneal_steps, eta_min=eta_min)
-        super().__init__(optimizer, warmup_steps, flat_steps,
-                         base_scheduler, last_epoch=last_epoch)
+        base_scheduler = _CosineAnnealingLR(optimizer, anneal_steps, eta_min=eta_min)
+        super().__init__(optimizer, warmup_steps, flat_steps, base_scheduler, last_epoch=last_epoch)