Clean up data collators and datasets

examples/language-modeling/run_mlm.py

@@ -264,7 +264,15 @@ def main():
         def tokenize_function(examples):
             # Remove empty lines
             examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
-            return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
+            return tokenizer(
+                examples["text"],
+                padding=padding,
+                truncation=True,
+                max_length=data_args.max_seq_length,
+                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
+                # receives the `special_tokens_mask`.
+                return_special_tokens_mask=True,
+            )
 
         tokenized_datasets = datasets.map(
             tokenize_function,
@@ -275,8 +283,10 @@ def tokenize_function(examples):
         )
     else:
         # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
+        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
+        # efficient when it receives the `special_tokens_mask`.
         def tokenize_function(examples):
-            return tokenizer(examples[text_column_name])
+            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
 
         tokenized_datasets = datasets.map(
             tokenize_function,

src/transformers/__init__.py

@@ -281,7 +281,6 @@
     from .data.data_collator import (
         DataCollator,
         DataCollatorForLanguageModeling,
-        DataCollatorForNextSentencePrediction,
         DataCollatorForPermutationLanguageModeling,
         DataCollatorForSOP,
         DataCollatorForTokenClassification,

src/transformers/data/data_collator.py

@@ -1,4 +1,5 @@
 import random
+import warnings
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
 
@@ -175,72 +176,111 @@ def __call__(self, features):
         return batch
 
 
+def _collate_batch(examples, tokenizer):
+    """Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
+    # Tensorize if necessary.
+    if isinstance(examples[0], (list, tuple)):
+        examples = [torch.tensor(e, dtype=torch.long) for e in examples]
+
+    # Check if padding is necessary.
+    length_of_first = examples[0].size(0)
+    are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
+    if are_tensors_same_length:
+        return torch.stack(examples, dim=0)
+
+    # If yes, check if we have a `pad_token`.
+    if tokenizer._pad_token is None:
+        raise ValueError(
+            "You are attempting to pad samples but the tokenizer you are using"
+            f" ({tokenizer.__class__.__name__}) does not have one."
+        )
+
+    # Creating the full tensor and filling it with our data.
+    max_length = max(x.size(0) for x in examples)
+    result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)
+    for i, example in enumerate(examples):
+        if tokenizer.padding_side == "right":
+            result[i, : example.shape[0]] = example
+        else:
+            result[i, -example.shape[0] :] = example
+    return result
+
+
 @dataclass
 class DataCollatorForLanguageModeling:
     """
-    Data collator used for language modeling.
+    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
+    are not all of the same length.
 
-    - collates batches of tensors, honoring their tokenizer's pad_token
-    - preprocesses batches for masked language modeling
+    Args:
+        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
+            The tokenizer used for encoding the data.
+        mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
+            inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
+            non-masked tokens and the value to predict for the masked token.
+        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
+            The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
+
+    .. note::
+
+        For best performance, this data collator should be used with a dataset having items that are dictionaries or
+        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
+        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
+        argument :obj:`return_special_tokens_mask=True`.
     """
 
     tokenizer: PreTrainedTokenizerBase
     mlm: bool = True
     mlm_probability: float = 0.15
 
+    def __post_init__(self):
+        if self.mlm and self.tokenizer.mask_token is None:
+            raise ValueError(
+                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
+                "You should pass `mlm=False`."
+            )
+
     def __call__(
         self, examples: List[Union[List[int], torch.Tensor, Dict[str, torch.Tensor]]]
     ) -> Dict[str, torch.Tensor]:
+        # Handle dict or lists with proper padding and conversion to tensor.
         if isinstance(examples[0], (dict, BatchEncoding)):
-            examples = [e["input_ids"] for e in examples]
-        batch = self._tensorize_batch(examples)
+            batch = self.tokenizer.pad(examples, return_tensors="pt")
+        else:
+            batch = {"input_ids": _collate_batch(examples, self.tokenizer)}
+
+        # If special token mask has been preprocessed, pop it from the dict.
+        special_tokens_mask = batch.pop("special_tokens_mask", None)
         if self.mlm:
-            inputs, labels = self.mask_tokens(batch)
-            return {"input_ids": inputs, "labels": labels}
+            batch["input_ids"], batch["labels"] = self.mask_tokens(
+                batch["input_ids"], special_tokens_mask=special_tokens_mask
+            )
         else:
-            labels = batch.clone().detach()
+            labels = batch["input_ids"].clone().detach()
             if self.tokenizer.pad_token_id is not None:
                 labels[labels == self.tokenizer.pad_token_id] = -100
-            return {"input_ids": batch, "labels": labels}
-
-    def _tensorize_batch(
-        self, examples: List[Union[List[int], torch.Tensor, Dict[str, torch.Tensor]]]
-    ) -> torch.Tensor:
-        # In order to accept both lists of lists and lists of Tensors
-        if isinstance(examples[0], (list, tuple)):
-            examples = [torch.tensor(e, dtype=torch.long) for e in examples]
-        length_of_first = examples[0].size(0)
-        are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
-        if are_tensors_same_length:
-            return torch.stack(examples, dim=0)
-        else:
-            if self.tokenizer._pad_token is None:
-                raise ValueError(
-                    "You are attempting to pad samples but the tokenizer you are using"
-                    f" ({self.tokenizer.__class__.__name__}) does not have one."
-                )
-            return pad_sequence(examples, batch_first=True, padding_value=self.tokenizer.pad_token_id)
+            batch["labels"] = labels
+        return batch
 
-    def mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    def mask_tokens(
+        self, inputs: torch.Tensor, special_tokens_mask: Optional[torch.Tensor] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
         """
-
-        if self.tokenizer.mask_token is None:
-            raise ValueError(
-                "This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
-            )
-
         labels = inputs.clone()
-        # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
+        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
         probability_matrix = torch.full(labels.shape, self.mlm_probability)
-        special_tokens_mask = [
-            self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
-        ]
-        probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
-        if self.tokenizer._pad_token is not None:
-            padding_mask = labels.eq(self.tokenizer.pad_token_id)
-            probability_matrix.masked_fill_(padding_mask, value=0.0)
+        if special_tokens_mask is None:
+            special_tokens_mask = [
+                self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
+            ]
+            special_tokens_mask = torch.tensor(special_tokens_mask, dtype=torch.bool)
+        else:
+            special_tokens_mask = special_tokens_mask.bool()
+
+        probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
         masked_indices = torch.bernoulli(probability_matrix).bool()
         labels[~masked_indices] = -100  # We only compute loss on masked tokens
 
@@ -385,9 +425,16 @@ class DataCollatorForSOP(DataCollatorForLanguageModeling):
     - preprocesses batches for both masked language modeling and sentence order prediction
     """
 
+    def __init__(self, *args, **kwargs):
+        warnings.warn(
+            "DataCollatorForSOP is deprecated and will be removed in a future version, you can now use "
+            "DataCollatorForLanguageModeling instead.",
+            FutureWarning,
+        )
+
     def __call__(self, examples: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
         input_ids = [example["input_ids"] for example in examples]
-        input_ids = self._tensorize_batch(input_ids)
+        input_ids = _collate_batch(input_ids, self.tokenizer)
         input_ids, labels, attention_mask = self.mask_tokens(input_ids)
 
         token_type_ids = [example["token_type_ids"] for example in examples]
@@ -582,136 +629,3 @@ def mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor,
             ) & masked_indices[i]
 
         return inputs.long(), perm_mask, target_mapping, labels.long()
-
-
-@dataclass
-class DataCollatorForNextSentencePrediction:
-    """
-    Data collator used for next sentence prediction. - collates examples which contains pre-generated negative examples
-    - preprocesses batches for masked language modeling
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    mlm: bool = True
-    block_size: int = 512
-    short_seq_probability: float = 0.1
-    nsp_probability: float = 0.5
-    mlm_probability: float = 0.15
-
-    def __call__(self, examples: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
-        """
-        The input should contain negative examples, :class:`~transformers.DataCollatorForNextSentencePrediction` will
-        not generate any negative examples
-
-        Args:
-            examples (:obj:`List[Dict]`): Each dictionary should have the following keys:
-
-                  - ``tokens_a``: A sequence of tokens, which should appear before ``tokens_b`` in the text.
-                  - ``tokens_b``: A sequence of tokens, which should appear after ``tokens_a`` in the text.
-                  - ``is_random_next``: 1 if this pair is generated randomly, else 0.
-        """
-
-        tokens_a = [e["tokens_a"] for e in examples]
-        tokens_b = [e["tokens_b"] for e in examples]
-        nsp_labels = [1 if e["is_random_next"] else 0 for e in examples]
-
-        input_ids = []
-        segment_ids = []
-        attention_masks = []
-
-        assert len(tokens_a) == len(tokens_b)
-        for i in range(len(tokens_a)):
-            input_id, attention_mask, segment_id = self.create_features_from_example(tokens_a[i], tokens_b[i])
-            input_ids.append(input_id)
-            segment_ids.append(segment_id)
-            attention_masks.append(attention_mask)
-        if self.mlm:
-            input_ids, mlm_labels = self.mask_tokens(self._tensorize_batch(input_ids))
-        else:
-            input_ids = self._tensorize_batch(input_ids)
-
-        result = {
-            "input_ids": input_ids,
-            "attention_mask": self._tensorize_batch(attention_masks),
-            "token_type_ids": self._tensorize_batch(segment_ids),
-            "labels": mlm_labels if self.mlm else None,
-            "next_sentence_label": torch.tensor(nsp_labels),
-        }
-        return result
-
-    def _tensorize_batch(self, examples: List[torch.Tensor]) -> torch.Tensor:
-        length_of_first = examples[0].size(0)
-        are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
-        if are_tensors_same_length:
-            return torch.stack(examples, dim=0)
-        else:
-            if self.tokenizer._pad_token is None:
-                raise ValueError(
-                    "You are attempting to pad samples but the tokenizer you are using"
-                    f" ({self.tokenizer.__class__.__name__}) does not have one."
-                )
-            return pad_sequence(examples, batch_first=True, padding_value=self.tokenizer.pad_token_id)
-
-    def create_features_from_example(self, tokens_a, tokens_b):
-        """Creates examples for a single document."""
-
-        max_num_tokens = self.block_size - self.tokenizer.num_special_tokens_to_add(pair=True)
-
-        tokens_a, tokens_b, _ = self.tokenizer.truncate_sequences(
-            tokens_a,
-            tokens_b,
-            num_tokens_to_remove=len(tokens_a) + len(tokens_b) - max_num_tokens,
-            truncation_strategy="longest_first",
-        )
-
-        input_id = self.tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b)
-        attention_mask = [1] * len(input_id)
-        segment_id = self.tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b)
-        assert len(input_id) <= self.block_size
-
-        # pad
-        while len(input_id) < self.block_size:
-            input_id.append(0)
-            attention_mask.append(0)
-            segment_id.append(0)
-
-        input_id = torch.tensor(input_id)
-        attention_mask = torch.tensor(attention_mask)
-        segment_id = torch.tensor(segment_id)
-
-        return input_id, attention_mask, segment_id
-
-    def mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
-        """
-
-        if self.tokenizer.mask_token is None:
-            raise ValueError(
-                "This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
-            )
-
-        labels = inputs.clone()
-        # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
-        probability_matrix = torch.full(labels.shape, self.mlm_probability)
-        special_tokens_mask = [
-            self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
-        ]
-        probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
-        if self.tokenizer._pad_token is not None:
-            padding_mask = labels.eq(self.tokenizer.pad_token_id)
-            probability_matrix.masked_fill_(padding_mask, value=0.0)
-        masked_indices = torch.bernoulli(probability_matrix).bool()
-        labels[~masked_indices] = -100  # We only compute loss on masked tokens
-
-        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-        indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
-        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
-        # 10% of the time, we replace masked input tokens with random word
-        indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
-        random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
-        inputs[indices_random] = random_words[indices_random]
-
-        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-        return inputs, labels