OpenAI GPT Tokenizer can fallback on using BERT BasicTokenizer

pytorch_pretrained_bert/tokenization_openai.py

@@ -26,6 +26,7 @@ from io import open
 from tqdm import tqdm
 
 from .file_utils import cached_path
+from .tokenization import BasicTokenizer
 
 logger = logging.getLogger(__name__)
 
@@ -72,8 +73,9 @@ class OpenAIGPTTokenizer(object):
     """
     BPE tokenizer. Peculiarities:
         - lower case all inputs
-        - uses SpaCy tokenizer
-        - special tokens: additional symbols (ex: "__classify__") to add to a vocabulary.
+        - uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not.
+        - argument special_tokens and function set_special_tokens:
+            can be used to add additional symbols (ex: "__classify__") to a vocabulary.
     """
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
@@ -122,12 +124,15 @@ class OpenAIGPTTokenizer(object):
         try:
             import ftfy
             import spacy
+            self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
+            self.fix_text = ftfy.fix_text
         except ImportError:
-            raise ImportError("Please install ftfy and spacy to use OpenAI GPT tokenizer.")
+            logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
+            self.nlp = BasicTokenizer(do_lower_case=True,
+                                      never_split=special_tokens if special_tokens is not None else [])
+            self.fix_text = None
 
         self.max_len = max_len if max_len is not None else int(1e12)
-        self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
-        self.fix_text = ftfy.fix_text
         self.encoder = json.load(open(vocab_file, encoding="utf-8"))
         self.decoder = {v:k for k,v in self.encoder.items()}
         merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
@@ -150,6 +155,9 @@ class OpenAIGPTTokenizer(object):
             return
         self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
         self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
+        if self.fix_text is None:
+            # Using BERT's BasicTokenizer: we can update the tokenizer
+            self.nlp.never_split = special_tokens
         logger.info("Special tokens {}".format(self.special_tokens))
 
     def bpe(self, token):
@@ -198,6 +206,13 @@ class OpenAIGPTTokenizer(object):
     def tokenize(self, text):
         """ Tokenize a string. """
         split_tokens = []
+        if self.fix_text is None:
+            # Using BERT's BasicTokenizer
+            text = self.nlp.tokenize(text)
+            for token in text:
+                split_tokens.extend([t for t in self.bpe(token).split(' ')])
+        else:
+            # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
             text = self.nlp(text_standardize(self.fix_text(text)))
             for token in text:
                 split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
@@ -219,8 +234,8 @@ class OpenAIGPTTokenizer(object):
         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)
+                " sequence length for this OpenAI GPT model ({} > {}). Running this"
+                " sequence through the model will result in indexing errors".format(len(ids), self.max_len)
             )
         return ids