Update namespaces inside torch.utils.data to the latest. (#13167)

* Update torch.utils.data namespaces to the latest.

* Format

* Update Dataloader.

* Style

examples/legacy/multiple_choice/utils_multiple_choice.py

@@ -77,7 +77,7 @@ class Split(Enum):
 
 if is_torch_available():
     import torch
-    from torch.utils.data.dataset import Dataset
+    from torch.utils.data import Dataset
 
     class MultipleChoiceDataset(Dataset):
         """

examples/legacy/seq2seq/seq2seq_trainer.py

@@ -141,7 +141,7 @@ class Seq2SeqTrainer(Trainer):
             )
         return scheduler
 
-    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
             return None
         elif is_torch_tpu_available():

examples/legacy/token-classification/utils_ner.py

@@ -206,7 +206,7 @@ class TokenClassificationTask:
 if is_torch_available():
     import torch
     from torch import nn
-    from torch.utils.data.dataset import Dataset
+    from torch.utils.data import Dataset
 
     class TokenClassificationDataset(Dataset):
         """

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -31,7 +31,7 @@ import random
 import datasets
 import torch
 from datasets import load_dataset
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -31,7 +31,7 @@ import random
 import datasets
 import torch
 from datasets import load_dataset
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -29,7 +29,7 @@ from typing import Optional, Union
 import datasets
 import torch
 from datasets import load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -28,7 +28,7 @@ import datasets
 import numpy as np
 import torch
 from datasets import load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -28,7 +28,7 @@ import datasets
 import numpy as np
 import torch
 from datasets import load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -29,7 +29,7 @@ import nltk
 import numpy as np
 import torch
 from datasets import load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -21,7 +21,7 @@ import random
 
 import datasets
 from datasets import load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -27,7 +27,7 @@ import random
 import datasets
 import torch
 from datasets import ClassLabel, load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/pytorch/translation/run_translation_no_trainer.py

@@ -28,7 +28,7 @@ import datasets
 import numpy as np
 import torch
 from datasets import load_dataset, load_metric
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers

examples/research_projects/adversarial/utils_hans.py

@@ -88,7 +88,7 @@ class InputFeatures:
 
 if is_torch_available():
     import torch
-    from torch.utils.data.dataset import Dataset
+    from torch.utils.data import Dataset
 
     class HansDataset(Dataset):
         """

examples/research_projects/distillation/grouped_batch_sampler.py

@@ -19,7 +19,7 @@ import copy
 from collections import defaultdict
 
 import numpy as np
-from torch.utils.data.sampler import BatchSampler, Sampler
+from torch.utils.data import BatchSampler, Sampler
 
 from utils import logger
 

src/transformers/data/datasets/glue.py

@@ -20,7 +20,7 @@ from enum import Enum
 from typing import List, Optional, Union
 
 import torch
-from torch.utils.data.dataset import Dataset
+from torch.utils.data import Dataset
 
 from filelock import FileLock
 

src/transformers/data/datasets/language_modeling.py

@@ -21,7 +21,7 @@ import warnings
 from typing import Dict, List, Optional
 
 import torch
-from torch.utils.data.dataset import Dataset
+from torch.utils.data import Dataset
 
 from filelock import FileLock
 

src/transformers/data/datasets/squad.py

@@ -19,7 +19,7 @@ from enum import Enum
 from typing import Dict, List, Optional, Union
 
 import torch
-from torch.utils.data.dataset import Dataset
+from torch.utils.data import Dataset
 
 from filelock import FileLock
 

src/transformers/trainer.py

@@ -49,10 +49,8 @@ import numpy as np
 import torch
 from packaging import version
 from torch import nn
-from torch.utils.data.dataloader import DataLoader
-from torch.utils.data.dataset import Dataset, IterableDataset
+from torch.utils.data import DataLoader, Dataset, IterableDataset, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data.sampler import RandomSampler, SequentialSampler
 
 from . import __version__
 from .configuration_utils import PretrainedConfig
@@ -206,16 +204,16 @@ class Trainer:
             The function to use to form a batch from a list of elements of :obj:`train_dataset` or :obj:`eval_dataset`.
             Will default to :func:`~transformers.default_data_collator` if no ``tokenizer`` is provided, an instance of
             :func:`~transformers.DataCollatorWithPadding` otherwise.
-        train_dataset (:obj:`torch.utils.data.dataset.Dataset` or :obj:`torch.utils.data.dataset.IterableDataset`, `optional`):
+        train_dataset (:obj:`torch.utils.data.Dataset` or :obj:`torch.utils.data.IterableDataset`, `optional`):
             The dataset to use for training. If it is an :obj:`datasets.Dataset`, columns not accepted by the
             ``model.forward()`` method are automatically removed.
 
-            Note that if it's a :obj:`torch.utils.data.dataset.IterableDataset` with some randomization and you are
-            training in a distributed fashion, your iterable dataset should either use a internal attribute
-            :obj:`generator` that is a :obj:`torch.Generator` for the randomization that must be identical on all
-            processes (and the Trainer will manually set the seed of this :obj:`generator` at each epoch) or have a
-            :obj:`set_epoch()` method that internally sets the seed of the RNGs used.
-        eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
+            Note that if it's a :obj:`torch.utils.data.IterableDataset` with some randomization and you are training in
+            a distributed fashion, your iterable dataset should either use a internal attribute :obj:`generator` that
+            is a :obj:`torch.Generator` for the randomization that must be identical on all processes (and the Trainer
+            will manually set the seed of this :obj:`generator` at each epoch) or have a :obj:`set_epoch()` method that
+            internally sets the seed of the RNGs used.
+        eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):
              The dataset to use for evaluation. If it is an :obj:`datasets.Dataset`, columns not accepted by the
              ``model.forward()`` method are automatically removed.
         tokenizer (:class:`PreTrainedTokenizerBase`, `optional`):
@@ -537,7 +535,7 @@ class Trainer:
         else:
             return dataset.remove_columns(ignored_columns)
 
-    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if not isinstance(self.train_dataset, collections.abc.Sized):
             return None
 
@@ -617,7 +615,7 @@ class Trainer:
         if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
             train_dataset = self._remove_unused_columns(train_dataset, description="training")
 
-        if isinstance(train_dataset, torch.utils.data.dataset.IterableDataset):
+        if isinstance(train_dataset, torch.utils.data.IterableDataset):
             if self.args.world_size > 1:
                 train_dataset = IterableDatasetShard(
                     train_dataset,
@@ -647,7 +645,7 @@ class Trainer:
             pin_memory=self.args.dataloader_pin_memory,
         )
 
-    def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.sampler.Sampler]:
+    def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.Sampler]:
         # Deprecated code
         if self.args.use_legacy_prediction_loop:
             if is_torch_tpu_available():
@@ -683,7 +681,7 @@ class Trainer:
         Subclass and override this method if you want to inject some custom behavior.
 
         Args:
-            eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
+            eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):
                 If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not
                 accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.
         """
@@ -694,7 +692,7 @@ class Trainer:
         if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
             eval_dataset = self._remove_unused_columns(eval_dataset, description="evaluation")
 
-        if isinstance(eval_dataset, torch.utils.data.dataset.IterableDataset):
+        if isinstance(eval_dataset, torch.utils.data.IterableDataset):
             if self.args.world_size > 1:
                 eval_dataset = IterableDatasetShard(
                     eval_dataset,
@@ -730,14 +728,14 @@ class Trainer:
         Subclass and override this method if you want to inject some custom behavior.
 
         Args:
-            test_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
+            test_dataset (:obj:`torch.utils.data.Dataset`, `optional`):
                 The test dataset to use. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                 ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.
         """
         if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
             test_dataset = self._remove_unused_columns(test_dataset, description="test")
 
-        if isinstance(test_dataset, torch.utils.data.dataset.IterableDataset):
+        if isinstance(test_dataset, torch.utils.data.IterableDataset):
             if self.args.world_size > 1:
                 test_dataset = IterableDatasetShard(
                     test_dataset,

src/transformers/trainer_callback.py

@@ -175,9 +175,9 @@ class TrainerCallback:
             The optimizer used for the training steps.
         lr_scheduler (:obj:`torch.optim.lr_scheduler.LambdaLR`):
             The scheduler used for setting the learning rate.
-        train_dataloader (:obj:`torch.utils.data.dataloader.DataLoader`, `optional`):
+        train_dataloader (:obj:`torch.utils.data.DataLoader`, `optional`):
             The current dataloader used for training.
-        eval_dataloader (:obj:`torch.utils.data.dataloader.DataLoader`, `optional`):
+        eval_dataloader (:obj:`torch.utils.data.DataLoader`, `optional`):
             The current dataloader used for training.
         metrics (:obj:`Dict[str, float]`):
             The metrics computed by the last evaluation phase.

src/transformers/trainer_pt_utils.py

@@ -29,9 +29,8 @@ import numpy as np
 import torch
 from packaging import version
 from torch import nn
-from torch.utils.data.dataset import Dataset, IterableDataset
+from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
 from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data.sampler import RandomSampler, Sampler
 
 from .file_utils import is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_torch_tpu_available
 from .tokenization_utils_base import BatchEncoding
@@ -290,7 +289,7 @@ class SequentialDistributedSampler(Sampler):
         return self.num_samples
 
 
-def get_tpu_sampler(dataset: torch.utils.data.dataset.Dataset, bach_size: int):
+def get_tpu_sampler(dataset: torch.utils.data.Dataset, batch_size: int):
     if xm.xrt_world_size() <= 1:
         return RandomSampler(dataset)
     return DistributedSampler(dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
@@ -690,7 +689,7 @@ class IterableDatasetShard(IterableDataset):
 
 
     Args:
-        dataset (:obj:`torch.utils.data.dataset.IterableDataset`):
+        dataset (:obj:`torch.utils.data.IterableDataset`):
             The batch sampler to split in several shards.
         batch_size (:obj:`int`, `optional`, defaults to 1):
             The size of the batches per shard.