Fix reproducibility in Training for PyTorch 1.11 (#16209)

src/transformers/trainer.py

@@ -1354,9 +1354,18 @@ class Trainer:
         # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
         if not args.ignore_data_skip:
             for epoch in range(epochs_trained):
+                is_random_sampler = hasattr(train_dataloader, "sampler") and isinstance(
+                    train_dataloader.sampler, RandomSampler
+                )
+                if version.parse(torch.__version__) < version.parse("1.11") or not is_random_sampler:
                     # We just need to begin an iteration to create the randomization of the sampler.
+                    # That was before PyTorch 1.11 however...
                     for _ in train_dataloader:
                         break
+                else:
+                    # Otherwise we need to call the whooooole sampler cause there is some random operation added
+                    # AT THE VERY END!
+                    _ = list(train_dataloader.sampler)
 
         for epoch in range(epochs_trained, num_train_epochs):
             if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):