Fix GPT2 and RoBERTa tokenizer to beging with a space - update Roberta tokenizer

pytorch_transformers/modeling_gpt2.py

@@ -682,7 +682,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
         tokenizer.add_special_tokens({'cls_token': '[CLS]'})  # Add a [CLS] to the vocabulary (we should train it also!)
-        model.resize_token_embeddings(tokenizer.vocab_size + 1)
+        model.resize_token_embeddings(len(tokenizer))  # Update the model embeddings to the new vocabulary size (add a vector at the end)
         choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
         input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
         mc_token_ids = torch.tensor([input_ids.size(-1), input_ids.size(-1)]).unsqueeze(0)  # Batch size 1

pytorch_transformers/tokenization_gpt2.py

@@ -109,7 +109,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
                  bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
         super(GPT2Tokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
 
-        self.encoder = json.load(open(vocab_file))
+        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
         self.decoder = {v: k for k, v in self.encoder.items()}
         self.errors = errors  # how to handle errors in decoding
         self.byte_encoder = bytes_to_unicode()
@@ -169,6 +169,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
 
     def _tokenize(self, text):
         """ Tokenize a string. """
+        text = ' ' + text  # GPT-2 (and RoBERTa) tokenizers need at least one space to begin the sentence with.
         bpe_tokens = []
         for token in re.findall(self.pat, text):
             if sys.version_info[0] == 2:

pytorch_transformers/tokenization_roberta.py

@@ -23,8 +23,7 @@ import os
 import regex as re
 from io import open
 
-from .tokenization_gpt2 import bytes_to_unicode, get_pairs
+from .tokenization_gpt2 import GPT2Tokenizer
-from .tokenization_utils import PreTrainedTokenizer
 
 try:
     from functools import lru_cache
@@ -63,7 +62,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
 }
 
 
-class RobertaTokenizer(PreTrainedTokenizer):
+class RobertaTokenizer(GPT2Tokenizer):
     """
     RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities: Byte-level BPE
     """
@@ -77,89 +76,6 @@ class RobertaTokenizer(PreTrainedTokenizer):
                                                sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
                                                mask_token=mask_token, **kwargs)
 
-        self.encoder = json.load(open(vocab_file, encoding="utf-8"))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
-        bpe_merges = [tuple(merge.split()) for merge in bpe_data]
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-
-        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
-
-    @property
-    def vocab_size(self):
-        return len(self.encoder)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def _tokenize(self, text):
-        """ Tokenize a string. """
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            if sys.version_info[0] == 2:
-                token = ''.join(self.byte_encoder[ord(b)] for b in token)
-            else:
-                token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def _convert_token_to_id(self, token):
-        """ Converts a token (str/unicode) in an id using the vocab. """
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (string/unicode) using the vocab."""
-        return self.decoder.get(index)
-
-    def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
-        text = ''.join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
-        return text
-
     def add_special_tokens_single_sentence(self, token_ids):
         """
         Adds special tokens to a sequence for sequence classification tasks.
@@ -175,27 +91,3 @@ class RobertaTokenizer(PreTrainedTokenizer):
         sep = [self._convert_token_to_id(self.sep_token)]
         cls = [self._convert_token_to_id(self.cls_token)]
         return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    def save_vocabulary(self, save_directory):
-        """Save the tokenizer vocabulary and merge files to a directory."""
-        if not os.path.isdir(save_directory):
-            logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
-            return
-        vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
-        merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
-
-        with open(vocab_file, 'w', encoding='utf-8') as f:
-            f.write(json.dumps(self.encoder, ensure_ascii=False))
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write(u'#version: 0.2\n')
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
-                                   " Please check that the tokenizer is not corrupted!".format(merge_file))
-                    index = token_index
-                writer.write(' '.join(bpe_tokens) + u'\n')
-                index += 1
-
-        return vocab_file, merge_file