Use generators tqdm progressbars (huggingface#6696)

src/transformers/trainer.py

@@ -641,28 +641,30 @@ def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", D
         logging_loss = 0.0
         model.zero_grad()
         disable_tqdm = self.args.disable_tqdm or not self.is_local_process_zero()
-        train_iterator = trange(epochs_trained, int(np.ceil(num_train_epochs)), desc="Epoch", disable=disable_tqdm)
-        for epoch in train_iterator:
+        train_pbar = trange(epochs_trained, int(np.ceil(num_train_epochs)), desc="Epoch", disable=disable_tqdm)
+        for epoch in range(epochs_trained, int(np.ceil(num_train_epochs))):
             if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                 train_dataloader.sampler.set_epoch(epoch)
 
             if is_torch_tpu_available():
                 parallel_loader = pl.ParallelLoader(train_dataloader, [self.args.device]).per_device_loader(
                     self.args.device
                 )
-                epoch_iterator = tqdm(parallel_loader, desc="Iteration", disable=disable_tqdm)
+                epoch_iterator = parallel_loader
             else:
-                epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=disable_tqdm)
+                epoch_iterator = train_dataloader
 
             # Reset the past mems state at the beginning of each epoch if necessary.
             if self.args.past_index >= 0:
                 self._past = None
 
+            epoch_pbar = tqdm(epoch_iterator, desc="Iteration", disable=disable_tqdm)
             for step, inputs in enumerate(epoch_iterator):
 
                 # Skip past any already trained steps if resuming training
                 if steps_trained_in_current_epoch > 0:
                     steps_trained_in_current_epoch -= 1
+                    epoch_pbar.update(1)
                     continue
 
                 tr_loss += self.training_step(model, inputs)
@@ -745,11 +747,12 @@ def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", D
                             torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                             torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
 
+                epoch_pbar.update(1)
                 if self.args.max_steps > 0 and self.global_step >= self.args.max_steps:
-                    epoch_iterator.close()
                     break
+            epoch_pbar.close()
+            train_pbar.update(1)
             if self.args.max_steps > 0 and self.global_step >= self.args.max_steps:
-                train_iterator.close()
                 break
             if self.args.tpu_metrics_debug or self.args.debug:
                 if is_torch_tpu_available():
@@ -761,6 +764,7 @@ def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", D
                         "configured. Check your training configuration if this is unexpected."
                     )
 
+        train_pbar.close()
         if self.tb_writer:
             self.tb_writer.close()
         if self.args.past_index and hasattr(self, "_past"):