customTrainer fix

customTrainer.py

@@ -72,12 +72,6 @@ from transformers.modelcard import TrainingSummary
 from transformers.modeling_utils import PreTrainedModel, load_sharded_checkpoint, unwrap_model
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES
 from transformers.optimization import Adafactor, get_scheduler
-from transformers.pytorch_utils import (
-    ALL_LAYERNORM_LAYERS,
-    is_torch_greater_or_equal_than_1_6,
-    is_torch_greater_or_equal_than_1_10,
-    is_torch_less_than_1_11,
-)
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from transformers.trainer_callback import (
     CallbackHandler,
@@ -168,14 +162,6 @@ if is_in_notebook():
 
     DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback
 
-
-if is_torch_greater_or_equal_than_1_6:
-    _is_torch_generator_available = True
-    _is_native_cuda_amp_available = True
-
-if is_torch_greater_or_equal_than_1_10:
-    _is_native_cpu_amp_available = True
-
 if is_datasets_available():
     import datasets
 
@@ -371,15 +357,7 @@ class CustomTrainer(Seq2SeqTrainer):
                 is_random_sampler = hasattr(train_dataloader, "sampler") and isinstance(
                     train_dataloader.sampler, RandomSampler
                 )
-                if is_torch_less_than_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)
+                _ = list(train_dataloader.sampler)
 
         for epoch in range(epochs_trained, num_train_epochs):
             if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):