added warning

pytorch_pretrained_bert/optimization.py

@@ -159,8 +159,8 @@ class BertAdam(Optimizer):
                     # warning for exceeding t_total (only active with warmup_linear
                     if group['schedule'] == "warmup_linear" and progress > 1. and not warned_for_t_total:
                         logger.warning(
-                            "Training beyond specified 't_total' steps. Learning rate set to {}. "
-                            "Please set 't_total' of {} correctly.".format(lr_scheduled, self.__class__.__name__))
+                            "Training beyond specified 't_total' steps with schedule '{}'. Learning rate set to {}. "
+                            "Please set 't_total' of {} correctly.".format(group['schedule'], lr_scheduled, self.__class__.__name__))
                         warned_for_t_total = True
                     # end warning
                 else:

pytorch_pretrained_bert/optimization_openai.py

@@ -29,14 +29,14 @@ def warmup_cosine(x, warmup=0.002):
     return 0.5 * (1.0 + torch.cos(math.pi * x))
 
 def warmup_constant(x, warmup=0.002):
-    """ Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps.
+    """ Linearly increases learning rate over `warmup`*`t_total` (as provided to OpenAIAdam) training steps.
         Learning rate is 1. afterwards. """
     if x < warmup:
         return x/warmup
     return 1.0
 
 def warmup_linear(x, warmup=0.002):
-    """ Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to BertAdam) training step.
+    """ Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to OpenAIAdam) training step.
         After `t_total`-th training step, learning rate is zero. """
     if x < warmup:
         return x/warmup
@@ -142,8 +142,8 @@ class OpenAIAdam(Optimizer):
                     # warning for exceeding t_total (only active with warmup_linear
                     if group['schedule'] == "warmup_linear" and progress > 1. and not warned_for_t_total:
                         logger.warning(
-                            "Training beyond specified 't_total' steps. Learning rate set to {}. "
-                            "Please set 't_total' of {} correctly.".format(lr_scheduled, self.__class__.__name__))
+                            "Training beyond specified 't_total' steps with schedule '{}'. Learning rate set to {}. "
+                            "Please set 't_total' of {} correctly.".format(group['schedule'], lr_scheduled, self.__class__.__name__))
                         warned_for_t_total = True
                     # end warning
                 else: