Fix TypeError: LRScheduler.__init__() with PyTorch 2.7

`TypeError: LRScheduler.__init__() got an unexpected keyword argument 'verbose'`

Author: aywi

examples/asr/emformer_rnnt/common.py

@@ -93,7 +93,7 @@ def forward(self, input):
 
 
 class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(self, optimizer, warmup_updates, last_epoch=-1, verbose=False):
+    def __init__(self, optimizer, warmup_updates, last_epoch=-1):
         self.warmup_updates = warmup_updates
         super().__init__(optimizer, last_epoch=last_epoch)
 

examples/asr/librispeech_conformer_rnnt/lightning.py

@@ -27,8 +27,6 @@ class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
         force_anneal_step (int): scheduler step at which annealing of learning rate begins.
         anneal_factor (float): factor to scale base learning rate by at each annealing step.
         last_epoch (int, optional): The index of last epoch. (Default: -1)
-        verbose (bool, optional): If ``True``, prints a message to stdout for
-            each update. (Default: ``False``)
     """
 
     def __init__(
@@ -38,12 +36,11 @@ def __init__(
         force_anneal_step: int,
         anneal_factor: float,
         last_epoch=-1,
-        verbose=False,
     ):
         self.warmup_steps = warmup_steps
         self.force_anneal_step = force_anneal_step
         self.anneal_factor = anneal_factor
-        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)
+        super().__init__(optimizer, last_epoch=last_epoch)
 
     def get_lr(self):
         if self._step_count < self.force_anneal_step:

examples/asr/librispeech_conformer_rnnt_biasing/lightning.py

@@ -26,8 +26,6 @@ class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
         force_anneal_step (int): scheduler step at which annealing of learning rate begins.
         anneal_factor (float): factor to scale base learning rate by at each annealing step.
         last_epoch (int, optional): The index of last epoch. (Default: -1)
-        verbose (bool, optional): If ``True``, prints a message to stdout for
-            each update. (Default: ``False``)
     """
 
     def __init__(
@@ -37,12 +35,11 @@ def __init__(
         force_anneal_step: int,
         anneal_factor: float,
         last_epoch=-1,
-        verbose=False,
     ):
         self.warmup_steps = warmup_steps
         self.force_anneal_step = force_anneal_step
         self.anneal_factor = anneal_factor
-        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)
+        super().__init__(optimizer, last_epoch=last_epoch)
 
     def get_lr(self):
         if self._step_count < self.force_anneal_step:

examples/avsr/schedulers.py

@@ -11,11 +11,10 @@ def __init__(
         total_epochs: int,
         steps_per_epoch: int,
         last_epoch=-1,
-        verbose=False,
     ):
         self.warmup_steps = warmup_epochs * steps_per_epoch
         self.total_steps = total_epochs * steps_per_epoch
-        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)
+        super().__init__(optimizer, last_epoch=last_epoch)
 
     def get_lr(self):
         if self._step_count < self.warmup_steps:

examples/hubert/lightning_modules.py

@@ -34,11 +34,10 @@ def __init__(
         warmup_updates: int,
         max_updates: int,
         last_epoch: int = -1,
-        verbose: bool = False,
     ):
         self.warmup_updates = warmup_updates
         self.max_updates = max_updates
-        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)
+        super().__init__(optimizer, last_epoch=last_epoch)
 
     def get_lr(self):
         if self._step_count <= self.warmup_updates:
@@ -62,15 +61,14 @@ def __init__(
         init_lr_scale: float = 0.01,
         final_lr_scale: float = 0.05,
         last_epoch: int = -1,
-        verbose: bool = False,
     ):
         self.warmup_updates = warmup_updates
         self.hold_updates = hold_updates
         self.decay_updates = decay_updates
         self.init_lr_scale = init_lr_scale
         self.final_lr_scale = final_lr_scale
 
-        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)
+        super().__init__(optimizer, last_epoch=last_epoch)
 
     def get_lr(self):
         if self._step_count <= self.warmup_updates:

examples/self_supervised_learning/lr_schedulers/_linear_decay.py

@@ -11,11 +11,10 @@ def __init__(
         warmup_updates: int,
         max_updates: int,
         last_epoch: int = -1,
-        verbose: bool = False,
     ):
         self.warmup_updates = warmup_updates
         self.max_updates = max_updates
-        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)
+        super().__init__(optimizer, last_epoch=last_epoch)
 
     def get_lr(self):
         if self._step_count <= self.warmup_updates: