Adding PaddingDataCollator (#6442)

* Data collator with padding

* Add type annotation

* Support tensors as well

* Add comment

* Fix for labels wrong shape

* Data collator with padding

* Add type annotation

* Support tensors as well

* Add comment

* Fix for labels wrong shape

* Remove changes rendered unnecessary

src/transformers/__init__.py

@@ -438,6 +438,7 @@ if is_torch_available():
         DataCollator,
         DataCollatorForLanguageModeling,
         DataCollatorForPermutationLanguageModeling,
+        DataCollatorWithPadding,
     )
     from .data.datasets import (
         GlueDataset,

src/transformers/data/data_collator.py

 from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, NewType, Tuple, Union
+from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
 
 import torch
 from torch.nn.utils.rnn import pad_sequence
 
 from ..tokenization_utils import PreTrainedTokenizer
-from ..tokenization_utils_base import BatchEncoding
+from ..tokenization_utils_base import BatchEncoding, PaddingStrategy
+from ..tokenization_utils_fast import PreTrainedTokenizerFast
 
 
 InputDataClass = NewType("InputDataClass", Any)
@@ -66,6 +67,55 @@ def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Ten
     return batch
 
 
+@dataclass
+class DataCollatorWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+
+    Args:
+        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
+            The tokenizer used for encoding the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding
+            index) among:
+
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
+              single sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+            >= 7.5 (Volta).
+    """
+
+    tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+    padding: Union[bool, str, PaddingStrategy] = True
+    max_length: Optional[int] = None
+    pad_to_multiple_of: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        batch = self.tokenizer.pad(
+            features,
+            padding=self.padding,
+            max_length=self.max_length,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+        if "label" in batch:
+            batch["labels"] = batch["label"]
+            del batch["label"]
+        if "label_ids" in batch:
+            batch["labels"] = batch["label_ids"]
+            del batch["label_ids"]
+        return batch
+
+
 @dataclass
 class DataCollatorForLanguageModeling:
     """