Immutability for data collators (#30603)

* immutability fix for seq2seq as well as immutability tests for the collators

* ensure we don't act on none labels and formatting

* remove tf/pt in respective tests as they are not required

* more type error fixes tf/np

* remove todo

* apply suggestions from code review

* formatting / style

src/transformers/data/data_collator.py

@@ -585,51 +585,84 @@ class DataCollatorForSeq2Seq:
     def __call__(self, features, return_tensors=None):
         if return_tensors is None:
             return_tensors = self.return_tensors
-        labels = [feature["labels"] for feature in features] if "labels" in features[0].keys() else None
-        # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
-        # same length to return tensors.
-        no_padding = self.padding is False or self.padding == PaddingStrategy.DO_NOT_PAD
-        if labels is not None and not no_padding:
-            max_padding = self.padding == PaddingStrategy.MAX_LENGTH and self.max_length is not None
-            max_label_length = max(len(l) for l in labels) if not max_padding else self.max_length
-            if self.pad_to_multiple_of is not None:
-                max_label_length = (
-                    (max_label_length + self.pad_to_multiple_of - 1)
-                    // self.pad_to_multiple_of
-                    * self.pad_to_multiple_of
-                )
-
-            padding_side = self.tokenizer.padding_side
-            for feature in features:
-                remainder = [self.label_pad_token_id] * (max_label_length - len(feature["labels"]))
-                if isinstance(feature["labels"], list):
-                    feature["labels"] = (
-                        feature["labels"] + remainder if padding_side == "right" else remainder + feature["labels"]
-                    )
-                elif padding_side == "right":
-                    feature["labels"] = np.concatenate([feature["labels"], remainder]).astype(np.int64)
-                else:
-                    feature["labels"] = np.concatenate([remainder, feature["labels"]]).astype(np.int64)
 
-        features = pad_without_fast_tokenizer_warning(
+        label_name = "label" if "label" in features[0].keys() else "labels"
+        labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
+        # reconvert list[None] to None if necessary
+        # this might occur when we pass {..., "labels": None}
+        if labels is not None and all(label is None for label in labels):
+            labels = None
+        non_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]
+
+        # run through tokenizer without labels to ensure no side effects
+        batch = pad_without_fast_tokenizer_warning(
             self.tokenizer,
-            features,
+            non_labels_features,
             padding=self.padding,
             max_length=self.max_length,
             pad_to_multiple_of=self.pad_to_multiple_of,
             return_tensors=return_tensors,
         )
 
+        # we have to pad the labels manually as we cannot rely on `tokenizer.pad` and we need them to be of the same length to return tensors
+        no_padding = self.padding is False or self.padding == PaddingStrategy.DO_NOT_PAD
+        if labels is not None:
+            if no_padding:
+                if isinstance(features[0][label_name], list):
+                    batch["labels"] = list(labels)
+                else:
+                    batch["labels"] = [np.concatenate([label, []]) for label in labels]
+            else:
+                max_padding = self.padding == PaddingStrategy.MAX_LENGTH and self.max_length is not None
+                max_label_length = max(len(l) for l in labels) if not max_padding else self.max_length
+                if self.pad_to_multiple_of is not None:
+                    max_label_length = (
+                        (max_label_length + self.pad_to_multiple_of - 1)
+                        // self.pad_to_multiple_of
+                        * self.pad_to_multiple_of
+                    )
+
+                padding_side = self.tokenizer.padding_side
+                if isinstance(features[0][label_name], list):
+                    batch["labels"] = [
+                        label + [self.label_pad_token_id] * (max_label_length - len(label))
+                        if padding_side == "right"
+                        else [self.label_pad_token_id] * (max_label_length - len(label)) + label
+                        for label in labels
+                    ]
+                else:
+                    batch["labels"] = [
+                        np.concatenate([label, [self.label_pad_token_id] * (max_label_length - len(label))])
+                        if padding_side == "right"
+                        else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
+                        for label in labels
+                    ]
+
+        # reintroduce side effects via tokenizer that return respective datatypes for the `return_tensors` argument
+        if batch.get("labels", None) is not None:
+            if return_tensors == "pt":
+                import torch
+
+                batch["labels"] = torch.tensor(batch["labels"], dtype=torch.int64)
+            elif return_tensors == "tf":
+                import tensorflow as tf
+
+                batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
+            else:
+                batch["labels"] = np.array(batch["labels"], dtype=np.int64)
+        else:
+            batch["labels"] = None
+
         # prepare decoder_input_ids
         if (
             labels is not None
             and self.model is not None
             and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
         ):
-            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=features["labels"])
-            features["decoder_input_ids"] = decoder_input_ids
+            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=batch["labels"])
+            batch["decoder_input_ids"] = decoder_input_ids
 
-        return features
+        return batch
 
 
 @dataclass

tests/trainer/test_data_collator.py

@@ -439,6 +439,330 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["sentence_order_label"].shape, torch.Size((2,)))
 
 
+@require_torch
+class DataCollatorImmutabilityTest(unittest.TestCase):
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
+        self.vocab_file = os.path.join(self.tmpdirname, "vocab.txt")
+        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def _turn_to_none(self, item):
+        """used to convert `item` to `None` type"""
+        return None
+
+    def _validate_original_data_against_collated_data(self, collator, original_data, batch_data):
+        # we only care about side effects, the results are tested elsewhere
+        collator(batch_data)
+
+        # we go through every item and convert to `primitive` datatypes if necessary
+        # then compares for equivalence for the original data and the data that has been passed through the collator
+        for original, batch in zip(original_data, batch_data):
+            for original_val, batch_val in zip(original.values(), batch.values()):
+                if isinstance(original_val, (np.ndarray, torch.Tensor)):
+                    self.assertEqual(original_val.tolist(), batch_val.tolist())
+                else:
+                    self.assertEqual(original_val, batch_val)
+
+    def _validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+        self, collator, base_data, input_key, input_datatype, label_key, label_datatype, ignore_label=False
+    ):
+        # using the arguments to recreate the features with their respective (potentially new) datatypes
+        features_original = [
+            {label_key: label_datatype(sample[label_key]), input_key: input_datatype(sample[input_key])}
+            for sample in base_data
+        ]
+        features_batch = [
+            {label_key: label_datatype(sample[label_key]), input_key: input_datatype(sample[input_key])}
+            for sample in base_data
+        ]
+
+        # some collators do not use labels, or sometimes we want to check if the collator with labels can handle such cases
+        if ignore_label:
+            for original, batch in zip(features_original, features_batch):
+                original.pop(label_key)
+                batch.pop(label_key)
+
+        self._validate_original_data_against_collated_data(
+            collator=collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_default_collator_immutability(self):
+        features_base_single_label = [{"label": i, "inputs": (0, 1, 2, 3, 4, 5)} for i in range(4)]
+        features_base_multiple_labels = [{"label": (0, 1, 2), "inputs": (0, 1, 2, 3, 4, 5)} for i in range(4)]
+
+        for datatype_input, datatype_label in [
+            (list, int),
+            (list, float),
+            (np.array, int),
+            (np.array, torch.tensor),
+            (list, self._turn_to_none),
+        ]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=default_data_collator,
+                base_data=features_base_single_label,
+                input_key="inputs",
+                input_datatype=datatype_input,
+                label_key="label",
+                label_datatype=datatype_label,
+            )
+
+        for datatype_input, datatype_label in [(list, list), (list, self._turn_to_none)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=default_data_collator,
+                base_data=features_base_multiple_labels,
+                input_key="inputs",
+                input_datatype=datatype_input,
+                label_key="label",
+                label_datatype=datatype_label,
+            )
+
+        features_base_single_label_alt = [{"input_ids": (0, 1, 2, 3, 4), "label": float(i)} for i in range(4)]
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=default_data_collator,
+            base_data=features_base_single_label_alt,
+            input_key="input_ids",
+            input_datatype=list,
+            label_key="label",
+            label_datatype=float,
+        )
+
+    def test_with_padding_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
+        features_batch = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
+
+        data_collator = DataCollatorWithPadding(tokenizer, padding="max_length", max_length=10)
+        self._validate_original_data_against_collated_data(
+            collator=data_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+        self._validate_original_data_against_collated_data(
+            collator=data_collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_for_token_classification_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": (0, 1, 2), "labels": (0, 1, 2)},
+            {"input_ids": (0, 1, 2, 3, 4, 5), "labels": (0, 1, 2, 3, 4, 5)},
+        ]
+        token_classification_collators = [
+            DataCollatorForTokenClassification(tokenizer),
+            DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10),
+            DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8),
+            DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1),
+        ]
+
+        for datatype_input, datatype_label in [(list, list), (torch.tensor, torch.tensor)]:
+            for collator in token_classification_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                )
+
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=token_classification_collators[-1],
+            base_data=features_base,
+            input_key="input_ids",
+            input_datatype=datatype_input,
+            label_key="labels",
+            label_datatype=datatype_label,
+            ignore_label=True,
+        )
+
+    def test_seq2seq_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+            {"input_ids": list(range(6)), "labels": list(range(6))},
+        ]
+        seq2seq_collators = [
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST),
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.MAX_LENGTH, max_length=7),
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, pad_to_multiple_of=8),
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, label_pad_token_id=-1),
+        ]
+
+        for datatype_input, datatype_label in [(list, list), (torch.tensor, torch.tensor)]:
+            for collator in seq2seq_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                )
+
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=seq2seq_collators[-1],
+            base_data=features_base,
+            input_key="input_ids",
+            input_datatype=datatype_input,
+            label_key="labels",
+            label_datatype=datatype_label,
+            ignore_label=True,
+        )
+
+        features_base_no_pad = [
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+        ]
+        seq2seq_no_padding_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.DO_NOT_PAD)
+        for datatype_input, datatype_label in [(list, list), (torch.tensor, torch.tensor)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=seq2seq_no_padding_collator,
+                base_data=features_base_no_pad,
+                input_key="input_ids",
+                input_datatype=datatype_input,
+                label_key="labels",
+                label_datatype=datatype_label,
+            )
+
+    def test_language_modelling_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base_no_pad = [
+            {"input_ids": tuple(range(10)), "labels": (1,)},
+            {"input_ids": tuple(range(10)), "labels": (1,)},
+        ]
+        features_base_pad = [
+            {"input_ids": tuple(range(5)), "labels": (1,)},
+            {"input_ids": tuple(range(5)), "labels": (1,)},
+        ]
+        lm_collators = [
+            DataCollatorForLanguageModeling(tokenizer, mlm=False),
+            DataCollatorForLanguageModeling(tokenizer, mlm=False, pad_to_multiple_of=8),
+            DataCollatorForLanguageModeling(tokenizer),
+            DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8),
+        ]
+
+        for datatype_input, datatype_label in [(list, list), (torch.tensor, torch.tensor)]:
+            for collator in lm_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base_no_pad,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                    ignore_label=True,
+                )
+
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base_pad,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                    ignore_label=True,
+                )
+
+    def test_whole_world_masking_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": list(range(10)), "labels": (1,)},
+            {"input_ids": list(range(10)), "labels": (1,)},
+        ]
+        whole_word_masking_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="pt")
+
+        for datatype_input, datatype_label in [(list, list), (np.array, np.array)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=whole_word_masking_collator,
+                base_data=features_base,
+                input_key="input_ids",
+                input_datatype=datatype_input,
+                label_key="labels",
+                label_datatype=datatype_label,
+                ignore_label=True,
+            )
+
+    def test_permutation_language_modelling_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        plm_collator = DataCollatorForPermutationLanguageModeling(tokenizer)
+
+        no_pad_features_original = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+        no_pad_features_batch = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+        self._validate_original_data_against_collated_data(
+            collator=plm_collator, original_data=no_pad_features_original, batch_data=no_pad_features_batch
+        )
+
+        pad_features_original = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
+        pad_features_batch = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
+        self._validate_original_data_against_collated_data(
+            collator=plm_collator, original_data=pad_features_original, batch_data=pad_features_batch
+        )
+
+    def test_next_sentence_prediction_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [
+            {"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
+            for i in range(2)
+        ]
+        features_batch = [
+            {"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
+            for i in range(2)
+        ]
+
+        nsp_collator = DataCollatorForLanguageModeling(tokenizer)
+        self._validate_original_data_against_collated_data(
+            collator=nsp_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        nsp_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
+        self._validate_original_data_against_collated_data(
+            collator=nsp_collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_sentence_order_prediction_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [
+            {
+                "input_ids": torch.tensor([0, 1, 2, 3, 4]),
+                "token_type_ids": torch.tensor([0, 1, 2, 3, 4]),
+                "sentence_order_label": i,
+            }
+            for i in range(2)
+        ]
+        features_batch = [
+            {
+                "input_ids": torch.tensor([0, 1, 2, 3, 4]),
+                "token_type_ids": torch.tensor([0, 1, 2, 3, 4]),
+                "sentence_order_label": i,
+            }
+            for i in range(2)
+        ]
+
+        sop_collator = DataCollatorForLanguageModeling(tokenizer)
+        self._validate_original_data_against_collated_data(
+            collator=sop_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        sop_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
+        self._validate_original_data_against_collated_data(
+            collator=sop_collator, original_data=features_original, batch_data=features_batch
+        )
+
+
 @require_tf
 class TFDataCollatorIntegrationTest(unittest.TestCase):
     def setUp(self):
@@ -794,6 +1118,338 @@ class TFDataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["sentence_order_label"].shape.as_list(), [2])
 
 
+@require_tf
+class TFDataCollatorImmutabilityTest(unittest.TestCase):
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
+        self.vocab_file = os.path.join(self.tmpdirname, "vocab.txt")
+        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def _turn_to_none(self, item):
+        """used to convert `item` to `None` type"""
+        return None
+
+    def _validate_original_data_against_collated_data(self, collator, original_data, batch_data):
+        # we only care about side effects, the results are tested elsewhere
+        collator(batch_data)
+
+        # we go through every item and convert to `primitive` datatypes if necessary
+        # then compares for equivalence for the original data and the data that has been passed through the collator
+        for original, batch in zip(original_data, batch_data):
+            for original_val, batch_val in zip(original.values(), batch.values()):
+                if isinstance(original_val, np.ndarray):
+                    self.assertEqual(original_val.tolist(), batch_val.tolist())
+                elif isinstance(original_val, tf.Tensor):
+                    self.assertEqual(original_val.numpy().tolist(), batch_val.numpy().tolist())
+                else:
+                    self.assertEqual(original_val, batch_val)
+
+    def _validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+        self, collator, base_data, input_key, input_datatype, label_key, label_datatype, ignore_label=False
+    ):
+        # using the arguments to recreate the features with their respective (potentially new) datatypes
+        features_original = [
+            {label_key: label_datatype(sample[label_key]), input_key: input_datatype(sample[input_key])}
+            for sample in base_data
+        ]
+        features_batch = [
+            {label_key: label_datatype(sample[label_key]), input_key: input_datatype(sample[input_key])}
+            for sample in base_data
+        ]
+
+        # some collators do not use labels, or sometimes we want to check if the collator with labels can handle such cases
+        if ignore_label:
+            for original, batch in zip(features_original, features_batch):
+                original.pop(label_key)
+                batch.pop(label_key)
+
+        self._validate_original_data_against_collated_data(
+            collator=collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_default_collator_immutability(self):
+        features_base_single_label = [{"label": i, "inputs": (0, 1, 2, 3, 4, 5)} for i in range(4)]
+        features_base_multiple_labels = [{"label": (0, 1, 2), "inputs": (0, 1, 2, 3, 4, 5)} for i in range(4)]
+
+        for datatype_input, datatype_label in [
+            (list, int),
+            (list, float),
+            (np.array, int),
+            (np.array, tf.constant),
+            (list, self._turn_to_none),
+        ]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=lambda x: default_data_collator(x, return_tensors="tf"),
+                base_data=features_base_single_label,
+                input_key="inputs",
+                input_datatype=datatype_input,
+                label_key="label",
+                label_datatype=datatype_label,
+            )
+
+        for datatype_input, datatype_label in [(list, list), (list, self._turn_to_none)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=lambda x: default_data_collator(x, return_tensors="tf"),
+                base_data=features_base_multiple_labels,
+                input_key="inputs",
+                input_datatype=datatype_input,
+                label_key="label",
+                label_datatype=datatype_label,
+            )
+
+        features_base_single_label_alt = [{"input_ids": (0, 1, 2, 3, 4), "label": float(i)} for i in range(4)]
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=lambda x: default_data_collator(x, return_tensors="tf"),
+            base_data=features_base_single_label_alt,
+            input_key="input_ids",
+            input_datatype=list,
+            label_key="label",
+            label_datatype=float,
+        )
+
+    def test_with_padding_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
+        features_batch = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
+
+        data_collator = DataCollatorWithPadding(tokenizer, padding="max_length", max_length=10, return_tensors="tf")
+        self._validate_original_data_against_collated_data(
+            collator=data_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
+        self._validate_original_data_against_collated_data(
+            collator=data_collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_for_token_classification_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": (0, 1, 2), "labels": (0, 1, 2)},
+            {"input_ids": (0, 1, 2, 3, 4, 5), "labels": (0, 1, 2, 3, 4, 5)},
+        ]
+        token_classification_collators = [
+            DataCollatorForTokenClassification(tokenizer, return_tensors="tf"),
+            DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10, return_tensors="tf"),
+            DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8, return_tensors="tf"),
+            DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1, return_tensors="tf"),
+        ]
+
+        for datatype_input, datatype_label in [(list, list)]:
+            for collator in token_classification_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                )
+
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=token_classification_collators[-1],
+            base_data=features_base,
+            input_key="input_ids",
+            input_datatype=datatype_input,
+            label_key="labels",
+            label_datatype=datatype_label,
+            ignore_label=True,
+        )
+
+    def test_seq2seq_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+            {"input_ids": list(range(6)), "labels": list(range(6))},
+        ]
+        seq2seq_collators = [
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, return_tensors="tf"),
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.MAX_LENGTH, max_length=7, return_tensors="tf"),
+            DataCollatorForSeq2Seq(
+                tokenizer, padding=PaddingStrategy.LONGEST, pad_to_multiple_of=8, return_tensors="tf"
+            ),
+            DataCollatorForSeq2Seq(
+                tokenizer, padding=PaddingStrategy.LONGEST, label_pad_token_id=-1, return_tensors="tf"
+            ),
+        ]
+
+        for datatype_input, datatype_label in [(list, list)]:
+            for collator in seq2seq_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                )
+
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=seq2seq_collators[-1],
+            base_data=features_base,
+            input_key="input_ids",
+            input_datatype=datatype_input,
+            label_key="labels",
+            label_datatype=datatype_label,
+            ignore_label=True,
+        )
+
+        features_base_no_pad = [
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+        ]
+        seq2seq_no_padding_collator = DataCollatorForSeq2Seq(
+            tokenizer, padding=PaddingStrategy.DO_NOT_PAD, return_tensors="tf"
+        )
+        for datatype_input, datatype_label in [(list, list)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=seq2seq_no_padding_collator,
+                base_data=features_base_no_pad,
+                input_key="input_ids",
+                input_datatype=datatype_input,
+                label_key="labels",
+                label_datatype=datatype_label,
+            )
+
+    def test_language_modelling_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base_no_pad = [
+            {"input_ids": tuple(range(10)), "labels": (1,)},
+            {"input_ids": tuple(range(10)), "labels": (1,)},
+        ]
+        features_base_pad = [
+            {"input_ids": tuple(range(5)), "labels": (1,)},
+            {"input_ids": tuple(range(5)), "labels": (1,)},
+        ]
+        lm_collators = [
+            DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="tf"),
+            DataCollatorForLanguageModeling(tokenizer, mlm=False, pad_to_multiple_of=8, return_tensors="tf"),
+            DataCollatorForLanguageModeling(tokenizer, return_tensors="tf"),
+            DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="tf"),
+        ]
+
+        for datatype_input, datatype_label in [(list, list)]:
+            for collator in lm_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base_no_pad,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                    ignore_label=True,
+                )
+
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base_pad,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                    ignore_label=True,
+                )
+
+    def test_whole_world_masking_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": list(range(10)), "labels": (1,)},
+            {"input_ids": list(range(10)), "labels": (1,)},
+        ]
+        whole_word_masking_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="tf")
+
+        for datatype_input, datatype_label in [(list, list), (np.array, np.array)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=whole_word_masking_collator,
+                base_data=features_base,
+                input_key="input_ids",
+                input_datatype=datatype_input,
+                label_key="labels",
+                label_datatype=datatype_label,
+                ignore_label=True,
+            )
+
+    def test_permutation_language_modelling_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        plm_collator = DataCollatorForPermutationLanguageModeling(tokenizer, return_tensors="tf")
+
+        no_pad_features_original = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+        no_pad_features_batch = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+        self._validate_original_data_against_collated_data(
+            collator=plm_collator, original_data=no_pad_features_original, batch_data=no_pad_features_batch
+        )
+
+        pad_features_original = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
+        pad_features_batch = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
+        self._validate_original_data_against_collated_data(
+            collator=plm_collator, original_data=pad_features_original, batch_data=pad_features_batch
+        )
+
+    def test_next_sentence_prediction_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [
+            {"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
+            for i in range(2)
+        ]
+        features_batch = [
+            {"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
+            for i in range(2)
+        ]
+
+        nsp_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="tf")
+        self._validate_original_data_against_collated_data(
+            collator=nsp_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        nsp_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
+        self._validate_original_data_against_collated_data(
+            collator=nsp_collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_sentence_order_prediction_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [
+            {
+                "input_ids": tf.convert_to_tensor([0, 1, 2, 3, 4]),
+                "token_type_ids": tf.convert_to_tensor([0, 1, 2, 3, 4]),
+                "sentence_order_label": i,
+            }
+            for i in range(2)
+        ]
+        features_batch = [
+            {
+                "input_ids": tf.convert_to_tensor([0, 1, 2, 3, 4]),
+                "token_type_ids": tf.convert_to_tensor([0, 1, 2, 3, 4]),
+                "sentence_order_label": i,
+            }
+            for i in range(2)
+        ]
+
+        sop_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="tf")
+        self._validate_original_data_against_collated_data(
+            collator=sop_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        sop_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
+        self._validate_original_data_against_collated_data(
+            collator=sop_collator, original_data=features_original, batch_data=features_batch
+        )
+
+
 class NumpyDataCollatorIntegrationTest(unittest.TestCase):
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
@@ -1137,3 +1793,332 @@ class NumpyDataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["token_type_ids"].shape, (2, 8))
         self.assertEqual(batch["labels"].shape, (2, 8))
         self.assertEqual(batch["sentence_order_label"].shape, (2,))
+
+
+class NumpyDataCollatorImmutabilityTest(unittest.TestCase):
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
+        self.vocab_file = os.path.join(self.tmpdirname, "vocab.txt")
+        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def _turn_to_none(self, item):
+        """used to convert `item` to `None` type"""
+        return None
+
+    def _validate_original_data_against_collated_data(self, collator, original_data, batch_data):
+        # we only care about side effects, the results are tested elsewhere
+        collator(batch_data)
+
+        # we go through every item and convert to `primitive` datatypes if necessary
+        # then compares for equivalence for the original data and the data that has been passed through the collator
+        for original, batch in zip(original_data, batch_data):
+            for original_val, batch_val in zip(original.values(), batch.values()):
+                if isinstance(original_val, np.ndarray):
+                    self.assertEqual(original_val.tolist(), batch_val.tolist())
+                else:
+                    self.assertEqual(original_val, batch_val)
+
+    def _validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+        self, collator, base_data, input_key, input_datatype, label_key, label_datatype, ignore_label=False
+    ):
+        # using the arguments to recreate the features with their respective (potentially new) datatypes
+        features_original = [
+            {label_key: label_datatype(sample[label_key]), input_key: input_datatype(sample[input_key])}
+            for sample in base_data
+        ]
+        features_batch = [
+            {label_key: label_datatype(sample[label_key]), input_key: input_datatype(sample[input_key])}
+            for sample in base_data
+        ]
+
+        # some collators do not use labels, or sometimes we want to check if the collator with labels can handle such cases
+        if ignore_label:
+            for original, batch in zip(features_original, features_batch):
+                original.pop(label_key)
+                batch.pop(label_key)
+
+        self._validate_original_data_against_collated_data(
+            collator=collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_default_collator_immutability(self):
+        features_base_single_label = [{"label": i, "inputs": (0, 1, 2, 3, 4, 5)} for i in range(4)]
+        features_base_multiple_labels = [{"label": (0, 1, 2), "inputs": (0, 1, 2, 3, 4, 5)} for i in range(4)]
+
+        for datatype_input, datatype_label in [
+            (list, int),
+            (list, float),
+            (np.array, int),
+            (np.array, np.array),
+            (list, self._turn_to_none),
+        ]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=lambda x: default_data_collator(x, return_tensors="np"),
+                base_data=features_base_single_label,
+                input_key="inputs",
+                input_datatype=datatype_input,
+                label_key="label",
+                label_datatype=datatype_label,
+            )
+
+        for datatype_input, datatype_label in [(list, list), (list, self._turn_to_none)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=lambda x: default_data_collator(x, return_tensors="np"),
+                base_data=features_base_multiple_labels,
+                input_key="inputs",
+                input_datatype=datatype_input,
+                label_key="label",
+                label_datatype=datatype_label,
+            )
+
+        features_base_single_label_alt = [{"input_ids": (0, 1, 2, 3, 4), "label": float(i)} for i in range(4)]
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=lambda x: default_data_collator(x, return_tensors="np"),
+            base_data=features_base_single_label_alt,
+            input_key="input_ids",
+            input_datatype=list,
+            label_key="label",
+            label_datatype=float,
+        )
+
+    def test_with_padding_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
+        features_batch = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
+
+        data_collator = DataCollatorWithPadding(tokenizer, padding="max_length", max_length=10, return_tensors="np")
+        self._validate_original_data_against_collated_data(
+            collator=data_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8, return_tensors="np")
+        self._validate_original_data_against_collated_data(
+            collator=data_collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_for_token_classification_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": (0, 1, 2), "labels": (0, 1, 2)},
+            {"input_ids": (0, 1, 2, 3, 4, 5), "labels": (0, 1, 2, 3, 4, 5)},
+        ]
+        token_classification_collators = [
+            DataCollatorForTokenClassification(tokenizer, return_tensors="np"),
+            DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10, return_tensors="np"),
+            DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8, return_tensors="np"),
+            DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1, return_tensors="np"),
+        ]
+
+        for datatype_input, datatype_label in [(list, list)]:
+            for collator in token_classification_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                )
+
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=token_classification_collators[-1],
+            base_data=features_base,
+            input_key="input_ids",
+            input_datatype=datatype_input,
+            label_key="labels",
+            label_datatype=datatype_label,
+            ignore_label=True,
+        )
+
+    def test_seq2seq_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+            {"input_ids": list(range(6)), "labels": list(range(6))},
+        ]
+        seq2seq_collators = [
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, return_tensors="np"),
+            DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.MAX_LENGTH, max_length=7, return_tensors="np"),
+            DataCollatorForSeq2Seq(
+                tokenizer, padding=PaddingStrategy.LONGEST, pad_to_multiple_of=8, return_tensors="np"
+            ),
+            DataCollatorForSeq2Seq(
+                tokenizer, padding=PaddingStrategy.LONGEST, label_pad_token_id=-1, return_tensors="np"
+            ),
+        ]
+
+        for datatype_input, datatype_label in [(list, list)]:
+            for collator in seq2seq_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                )
+
+        self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+            collator=seq2seq_collators[-1],
+            base_data=features_base,
+            input_key="input_ids",
+            input_datatype=datatype_input,
+            label_key="labels",
+            label_datatype=datatype_label,
+            ignore_label=True,
+        )
+
+        features_base_no_pad = [
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+            {"input_ids": list(range(3)), "labels": list(range(3))},
+        ]
+        seq2seq_no_padding_collator = DataCollatorForSeq2Seq(
+            tokenizer, padding=PaddingStrategy.DO_NOT_PAD, return_tensors="np"
+        )
+        for datatype_input, datatype_label in [(list, list)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=seq2seq_no_padding_collator,
+                base_data=features_base_no_pad,
+                input_key="input_ids",
+                input_datatype=datatype_input,
+                label_key="labels",
+                label_datatype=datatype_label,
+            )
+
+    def test_language_modelling_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base_no_pad = [
+            {"input_ids": tuple(range(10)), "labels": (1,)},
+            {"input_ids": tuple(range(10)), "labels": (1,)},
+        ]
+        features_base_pad = [
+            {"input_ids": tuple(range(5)), "labels": (1,)},
+            {"input_ids": tuple(range(5)), "labels": (1,)},
+        ]
+        lm_collators = [
+            DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="np"),
+            DataCollatorForLanguageModeling(tokenizer, mlm=False, pad_to_multiple_of=8, return_tensors="np"),
+            DataCollatorForLanguageModeling(tokenizer, return_tensors="np"),
+            DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="np"),
+        ]
+
+        for datatype_input, datatype_label in [(list, list)]:
+            for collator in lm_collators:
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base_no_pad,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                    ignore_label=True,
+                )
+
+                self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                    collator=collator,
+                    base_data=features_base_pad,
+                    input_key="input_ids",
+                    input_datatype=datatype_input,
+                    label_key="labels",
+                    label_datatype=datatype_label,
+                    ignore_label=True,
+                )
+
+    def test_whole_world_masking_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_base = [
+            {"input_ids": list(range(10)), "labels": (1,)},
+            {"input_ids": list(range(10)), "labels": (1,)},
+        ]
+        whole_word_masking_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="np")
+
+        for datatype_input, datatype_label in [(list, list), (np.array, np.array)]:
+            self._validate_original_data_against_collated_data_on_specified_keys_and_datatypes(
+                collator=whole_word_masking_collator,
+                base_data=features_base,
+                input_key="input_ids",
+                input_datatype=datatype_input,
+                label_key="labels",
+                label_datatype=datatype_label,
+                ignore_label=True,
+            )
+
+    def test_permutation_language_modelling_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        plm_collator = DataCollatorForPermutationLanguageModeling(tokenizer, return_tensors="np")
+
+        no_pad_features_original = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+        no_pad_features_batch = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+        self._validate_original_data_against_collated_data(
+            collator=plm_collator, original_data=no_pad_features_original, batch_data=no_pad_features_batch
+        )
+
+        pad_features_original = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
+        pad_features_batch = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
+        self._validate_original_data_against_collated_data(
+            collator=plm_collator, original_data=pad_features_original, batch_data=pad_features_batch
+        )
+
+    def test_next_sentence_prediction_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [
+            {"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
+            for i in range(2)
+        ]
+        features_batch = [
+            {"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
+            for i in range(2)
+        ]
+
+        nsp_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="np")
+        self._validate_original_data_against_collated_data(
+            collator=nsp_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        nsp_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="np")
+        self._validate_original_data_against_collated_data(
+            collator=nsp_collator, original_data=features_original, batch_data=features_batch
+        )
+
+    def test_sentence_order_prediction_collator_immutability(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+
+        features_original = [
+            {
+                "input_ids": np.array([0, 1, 2, 3, 4]),
+                "token_type_ids": np.array([0, 1, 2, 3, 4]),
+                "sentence_order_label": i,
+            }
+            for i in range(2)
+        ]
+        features_batch = [
+            {
+                "input_ids": np.array([0, 1, 2, 3, 4]),
+                "token_type_ids": np.array([0, 1, 2, 3, 4]),
+                "sentence_order_label": i,
+            }
+            for i in range(2)
+        ]
+
+        sop_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="np")
+        self._validate_original_data_against_collated_data(
+            collator=sop_collator, original_data=features_original, batch_data=features_batch
+        )
+
+        sop_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="np")
+        self._validate_original_data_against_collated_data(
+            collator=sop_collator, original_data=features_original, batch_data=features_batch
+        )