added code to raise value error for bert tokenizer for covert_tokens_to_indices

pytorch_pretrained_bert/tokenization.py

@@ -36,6 +36,15 @@ PRETRAINED_VOCAB_ARCHIVE_MAP = {
     'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
     'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
 }
+PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
+    'bert-base-uncased': 512,
+    'bert-large-uncased': 512,
+    'bert-base-cased': 512,
+    'bert-large-cased': 512,
+    'bert-base-multilingual-uncased': 512,
+    'bert-base-multilingual-cased': 512,
+    'bert-base-chinese': 512,
+}
 VOCAB_NAME = 'vocab.txt'
 
 
@@ -65,7 +74,8 @@ def whitespace_tokenize(text):
 
 class BertTokenizer(object):
     """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
-    def __init__(self, vocab_file, do_lower_case=True):
+
+    def __init__(self, vocab_file, do_lower_case=True, max_len=None):
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
@@ -75,6 +85,7 @@ class BertTokenizer(object):
             [(ids, tok) for tok, ids in self.vocab.items()])
         self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+        self.max_len = max_len if max_len is not None else int(1e12)
 
     def tokenize(self, text):
         split_tokens = []
@@ -88,6 +99,12 @@ class BertTokenizer(object):
         ids = []
         for token in tokens:
             ids.append(self.vocab[token])
+        if len(ids) > self.max_len:
+            raise ValueError(
+                "Token indices sequence length is longer than the specified maximum "
+                " sequence length for this BERT model ({} > {}). Running this"
+                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
+            )
         return ids
 
     def convert_ids_to_tokens(self, ids):
@@ -126,6 +143,11 @@ class BertTokenizer(object):
         else:
             logger.info("loading vocabulary file {} from cache at {}".format(
                 vocab_file, resolved_vocab_file))
+        if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
+            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
+            # than the number of positional embeddings
+            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
+            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
         # Instantiate tokenizer.
         tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
         return tokenizer

tests/tokenization_test.py

@@ -44,6 +44,24 @@ class TokenizationTest(unittest.TestCase):
         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
 
+    def test_full_tokenizer_raises_error_for_long_sequences(self):
+        vocab_tokens = [
+            "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+            "##ing", ","
+        ]
+        with open("/tmp/bert_tokenizer_test.txt", "w") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+            vocab_file = vocab_writer.name
+
+        tokenizer = BertTokenizer(vocab_file, max_len=10)
+        os.remove(vocab_file)
+        tokens = tokenizer.tokenize(u"the cat sat on the mat in the summer time")
+        indices = tokenizer.convert_tokens_to_ids(tokens)
+        self.assertListEqual(indices, [0 for _ in range(10)])
+
+        tokens = tokenizer.tokenize(u"the cat sat on the mat in the summer time .")
+        self.assertRaises(ValueError, tokenizer.convert_tokens_to_ids, tokens)
+
     def test_chinese(self):
         tokenizer = BasicTokenizer()