minimal fixes to run DataCollatorForWholeWordMask with return_tensors…

…="np" and return_tensors="tf" (huggingface#13891)

* minimal fixes to run DataCollatorForWholeWordMask with return_tensors="np" and return_tensors="tf"

* more consinstent implementation for numpy_mask_tokens

src/transformers/data/data_collator.py

@@ -883,14 +883,14 @@ def tf_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict
         inputs, labels = self.tf_mask_tokens(batch_input, batch_mask)
         return {"input_ids": inputs, "labels": labels}
 
-    def np_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
+    def numpy_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
         if isinstance(examples[0], (dict, BatchEncoding)):
             input_ids = [e["input_ids"] for e in examples]
         else:
             input_ids = examples
             examples = [{"input_ids": e} for e in examples]
 
-        batch_input = _tf_collate_batch(input_ids, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
+        batch_input = _numpy_collate_batch(input_ids, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
 
         mask_labels = []
         for e in examples:
@@ -1009,15 +1009,15 @@ def tf_mask_tokens(self, inputs: Any, mask_labels: Any) -> Tuple[Any, Any]:
             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()
+        labels = tf.identity(inputs)
         # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
 
         masked_indices = tf.cast(mask_labels, tf.bool)
 
         special_tokens_mask = [
-            self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
+            self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels
         ]
-        masked_indices = masked_indices & ~tf.convert_to_tensor(special_tokens_mask, dtype=tf.bool)
+        masked_indices = masked_indices & ~tf.cast(special_tokens_mask, dtype=tf.bool)
         if self.tokenizer._pad_token is not None:
             padding_mask = inputs == self.tokenizer.pad_token_id
             masked_indices = masked_indices & ~padding_mask
@@ -1073,9 +1073,7 @@ def numpy_mask_tokens(self, inputs: Any, mask_labels: Any) -> Tuple[Any, Any]:
         indices_random = (
             np.random.binomial(1, 0.5, size=labels.shape).astype(np.bool) & masked_indices & ~indices_replaced
         )
-        random_words = np.random.randint(
-            low=0, high=len(self.tokenizer), size=np.count_nonzero(indices_random), dtype=np.int64
-        )
+        random_words = np.random.randint(low=0, high=len(self.tokenizer), size=labels.shape, dtype=np.int64)
         inputs[indices_random] = random_words[indices_random]
 
         # The rest of the time (10% of the time) we keep the masked input tokens unchanged

tests/test_data_collator.py

@@ -24,6 +24,7 @@
     DataCollatorForLanguageModeling,
     DataCollatorForPermutationLanguageModeling,
     DataCollatorForTokenClassification,
+    DataCollatorForWholeWordMask,
     DataCollatorWithPadding,
     default_data_collator,
     is_tf_available,
@@ -224,6 +225,16 @@ def test_data_collator_for_language_modeling(self):
         pad_features = [list(range(5)), list(range(10))]
         self._test_no_pad_and_pad(no_pad_features, pad_features)
 
+    def test_data_collator_for_whole_word_mask(self):
+        features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+
+        tokenizer = BertTokenizer(self.vocab_file)
+        data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="pt")
+        batch = data_collator(features)
+
+        self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
+        self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
+
     def test_plm(self):
         tokenizer = BertTokenizer(self.vocab_file)
         no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
@@ -488,6 +499,16 @@ def test_data_collator_for_language_modeling(self):
         pad_features = [list(range(5)), list(range(10))]
         self._test_no_pad_and_pad(no_pad_features, pad_features)
 
+    def test_data_collator_for_whole_word_mask(self):
+        features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+
+        tokenizer = BertTokenizer(self.vocab_file)
+        data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="tf")
+        batch = data_collator(features)
+
+        self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
+        self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
+
     def test_plm(self):
         tokenizer = BertTokenizer(self.vocab_file)
         no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
@@ -750,6 +771,16 @@ def test_data_collator_for_language_modeling(self):
         pad_features = [list(range(5)), list(range(10))]
         self._test_no_pad_and_pad(no_pad_features, pad_features)
 
+    def test_data_collator_for_whole_word_mask(self):
+        features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
+
+        tokenizer = BertTokenizer(self.vocab_file)
+        data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="np")
+        batch = data_collator(features)
+
+        self.assertEqual(batch["input_ids"].shape, (2, 10))
+        self.assertEqual(batch["labels"].shape, (2, 10))
+
     def test_plm(self):
         tokenizer = BertTokenizer(self.vocab_file)
         no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]