#32184 save total_vocab_size (#32240)

* save total_vocab_size = vocab_size + user added tokens to speed up operation

* updating length when added_tokens_decoder is set

* add test len(tokenizer)

src/transformers/tokenization_utils.py

@@ -480,6 +480,7 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
 
             self._added_tokens_decoder[index] = AddedToken(token) if isinstance(token, str) else token
             self._added_tokens_encoder[str(token)] = index
+        self._update_total_vocab_size()
 
     def get_added_vocab(self) -> Dict[str, int]:
         """
@@ -494,10 +495,17 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
 
     def __len__(self):
         """
-        Size of the full vocabulary with the added tokens. Counts the `keys` and not the `values` because otherwise if
-        there is a hole in the vocab, we will add tokenizers at a wrong index.
+        Size of the full vocabulary with the added tokens.
         """
-        return len(set(self.get_vocab().keys()))
+        return self.total_vocab_size
+
+    def _update_total_vocab_size(self):
+        """
+        Update the size of the full vocabulary with the added tokens. Counts the `keys` and not the `values` because
+        otherwise if there is a hole in the vocab, we will add tokenizers at a wrong index. This operation is slow and
+        is only updated when adding tokens.
+        """
+        self.total_vocab_size = len(self.get_vocab())
 
     def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
         """
@@ -574,6 +582,7 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
                 logger.info(f"Adding {token} to the vocabulary")
 
         self._update_trie()
+        self._update_total_vocab_size()
         return added_tokens
 
     def _update_trie(self, unique_no_split_tokens: Optional[str] = []):

tests/tokenization/test_tokenization_utils.py

@@ -284,3 +284,15 @@ class TokenizerUtilsTest(unittest.TestCase):
         with tempfile.TemporaryDirectory() as tmpdirname:
             bert_tokenizer.save(os.path.join(tmpdirname, "tokenizer.json"))
             PreTrainedTokenizerFast(tokenizer_file=os.path.join(tmpdirname, "tokenizer.json"))
+
+    def test_len_tokenizer(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizerFast]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("bert-base-uncased")
+                added_tokens_size = len(tokenizer.added_tokens_decoder)
+                self.assertEqual(len(tokenizer), tokenizer.vocab_size)
+
+                tokenizer.add_tokens(["<test_token>"])
+                self.assertEqual(len(tokenizer), tokenizer.vocab_size + 1)
+                self.assertEqual(len(tokenizer.added_tokens_decoder), added_tokens_size + 1)
+                self.assertEqual(len(tokenizer.added_tokens_encoder), added_tokens_size + 1)