fix training schedules in examples to match new API

examples/run_classifier.py

@@ -38,7 +38,7 @@ from sklearn.metrics import matthews_corrcoef, f1_score
 from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
 from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig
 from pytorch_pretrained_bert.tokenization import BertTokenizer
-from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
+from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
 
 logger = logging.getLogger(__name__)
 
@@ -784,6 +784,8 @@ def main():
             optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
         else:
             optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
+        warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
+                                             t_total=num_train_optimization_steps)
 
     else:
         optimizer = BertAdam(optimizer_grouped_parameters,
@@ -852,7 +854,8 @@ def main():
                     if args.fp16:
                         # modify learning rate with special warm up BERT uses
                         # if args.fp16 is False, BertAdam is used that handles this automatically
-                        lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
+                        lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step/num_train_optimization_steps,
+                                                                                 args.warmup_proportion)
                         for param_group in optimizer.param_groups:
                             param_group['lr'] = lr_this_step
                     optimizer.step()