:zip: #2106 basic tokenizer.tokenize global speed improvement (3-8x) by simply...

:zip: #2106 basic tokenizer.tokenize global speed improvement (3-8x) by simply caching added_tokens in a Set

transformers/tokenization_utils.py

@@ -231,6 +231,7 @@ class PreTrainedTokenizer(object):
         
         # Added tokens
         self.added_tokens_encoder = {}
+        self.unique_added_tokens_encoder = set()
         self.added_tokens_decoder = {}
 
         # inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
@@ -550,6 +551,7 @@ class PreTrainedTokenizer(object):
         added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(to_add_tokens))
         added_tok_decoder = {v:k for k, v in added_tok_encoder.items()}
         self.added_tokens_encoder.update(added_tok_encoder)
+        self.unique_added_tokens_encoder = set(self.added_tokens_encoder.keys()).union(set(self.all_special_tokens))
         self.added_tokens_decoder.update(added_tok_decoder)
 
         return len(to_add_tokens)
@@ -627,6 +629,7 @@ class PreTrainedTokenizer(object):
 
         return added_tokens
 
+
     def tokenize(self, text, **kwargs):
         """ Converts a string in a sequence of tokens (string), using the tokenizer.
             Split in words for word-based vocabulary or sub-words for sub-word-based
@@ -681,18 +684,17 @@ class PreTrainedTokenizer(object):
             for tok in tok_list:
                 tokenized_text = []
                 for sub_text in text_list:
-                    if sub_text not in self.added_tokens_encoder \
-                            and sub_text not in all_special_tokens:
+                    if sub_text not in self.unique_added_tokens_encoder:
                         tokenized_text += split_on_token(tok, sub_text)
                     else:
                         tokenized_text += [sub_text]
                 text_list = tokenized_text
 
-            return list(itertools.chain.from_iterable((self._tokenize(token, **kwargs) if token not \
-                    in self.added_tokens_encoder and token not in all_special_tokens \
+            return list(itertools.chain.from_iterable((self._tokenize(token, **kwargs) \
+                    if token not in self.unique_added_tokens_encoder
                     else [token] for token in tokenized_text)))
 
-        added_tokens = list(self.added_tokens_encoder.keys()) + all_special_tokens
+        added_tokens = self.unique_added_tokens_encoder
         tokenized_text = split_on_tokens(added_tokens, text)
         return tokenized_text