Data collator for token classification pads labels column when receiv…

…es pytorch tensors (#20244)

* token cls data_collator pads labels column

* remove walrus operator for code quality

* remove redundat space

* remove comment that was fixed

* PR comments fix

Co-authored-by: Alexander Markov <amarkov.me@gmail.com>

src/transformers/data/data_collator.py

@@ -305,30 +305,38 @@ def torch_call(self, features):
 
         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
+
+        no_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]
+
         batch = self.tokenizer.pad(
-            features,
+            no_labels_features,
             padding=self.padding,
             max_length=self.max_length,
             pad_to_multiple_of=self.pad_to_multiple_of,
-            # Conversion to tensors will fail if we have labels as they are not of the same length yet.
-            return_tensors="pt" if labels is None else None,
+            return_tensors="pt",
         )
 
         if labels is None:
             return batch
 
-        sequence_length = torch.tensor(batch["input_ids"]).shape[1]
+        sequence_length = batch["input_ids"].shape[1]
         padding_side = self.tokenizer.padding_side
+
+        def to_list(tensor_or_iterable):
+            if isinstance(tensor_or_iterable, torch.Tensor):
+                return tensor_or_iterable.tolist()
+            return list(tensor_or_iterable)
+
         if padding_side == "right":
             batch[label_name] = [
-                list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
+                to_list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
             ]
         else:
             batch[label_name] = [
-                [self.label_pad_token_id] * (sequence_length - len(label)) + list(label) for label in labels
+                [self.label_pad_token_id] * (sequence_length - len(label)) + to_list(label) for label in labels
             ]
 
-        batch = {k: torch.tensor(v, dtype=torch.int64) for k, v in batch.items()}
+        batch[label_name] = torch.tensor(batch[label_name], dtype=torch.int64)
         return batch
 
     def tf_call(self, features):

tests/trainer/test_data_collator.py

@@ -154,6 +154,51 @@ def test_data_collator_for_token_classification(self):
         self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
         self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3)
 
+        for feature in features:
+            feature.pop("labels")
+
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
+        self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
+
+    def test_data_collator_for_token_classification_works_with_pt_tensors(self):
+        tokenizer = BertTokenizer(self.vocab_file)
+        features = [
+            {"input_ids": torch.tensor([0, 1, 2]), "labels": torch.tensor([0, 1, 2])},
+            {"input_ids": torch.tensor([0, 1, 2, 3, 4, 5]), "labels": torch.tensor([0, 1, 2, 3, 4, 5])},
+        ]
+
+        data_collator = DataCollatorForTokenClassification(tokenizer)
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
+        self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
+        self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
+        self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-100] * 3)
+
+        data_collator = DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10)
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
+        self.assertEqual(batch["labels"].shape, torch.Size([2, 10]))
+
+        data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8)
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
+        self.assertEqual(batch["labels"].shape, torch.Size([2, 8]))
+
+        data_collator = DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1)
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
+        self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
+        self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
+        self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3)
+
+        for feature in features:
+            feature.pop("labels")
+
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
+        self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
+
     def _test_no_pad_and_pad(self, no_pad_features, pad_features):
         tokenizer = BertTokenizer(self.vocab_file)
         data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)