fix dataset shuffling for Distributed training (#huggingface#3721) (#3766)

examples/run_language_modeling.py

@@ -317,8 +317,12 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
         epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
     )
     set_seed(args)  # Added here for reproducibility
-    for _ in train_iterator:
+    for epoch in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
+
+        if args.local_rank != -1:
+            train_sampler.set_epoch(epoch)
+
         for step, batch in enumerate(epoch_iterator):
 
             # Skip past any already trained steps if resuming training