Roberta tokenization + fixed tests (py3 + py2).

pytorch_transformers/tests/modeling_roberta_test.py

@@ -157,42 +157,6 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
             inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict
 
-        def test_inference_masked_lm(self):
-            model = RobertaForMaskedLM.from_pretrained('roberta-base')
-
-            input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
-            output = model(input_ids)[0]
-            expected_shape = torch.Size((1, 11, 50265))
-            self.assertEqual(
-                output.shape,
-                expected_shape
-            )
-            # compare the actual values for a slice.
-            expected_slice = torch.Tensor(
-                [[[33.8843, -4.3107, 22.7779],
-                  [4.6533, -2.8099, 13.6252],
-                  [1.8222, -3.6898, 8.8600]]]
-            )
-            self.assertTrue(
-                torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
-            )
-
-        # @pytest.mark.slow
-        def test_inference_no_head(self):
-            model = RobertaModel.from_pretrained('roberta-base')
-
-            input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
-            output = model(input_ids)[0]
-            # compare the actual values for a slice.
-            expected_slice = torch.Tensor(
-                [[[-0.0231, 0.0782, 0.0074],
-                  [-0.1854, 0.0539, -0.0174],
-                  [0.0548, 0.0799, 0.1687]]]
-            )
-            self.assertTrue(
-                torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
-            )
-
     def setUp(self):
         self.model_tester = RobertaModelTest.RobertaModelTester(self)
         self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
@@ -220,7 +184,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
 
 class RobertaModelIntegrationTest(unittest.TestCase):
 
-    # @pytest.mark.slow
+    @pytest.mark.slow
     def test_inference_masked_lm(self):
         model = RobertaForMaskedLM.from_pretrained('roberta-base')
         
@@ -241,7 +205,7 @@ class RobertaModelIntegrationTest(unittest.TestCase):
             torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
         )
 
-    # @pytest.mark.slow
+    @pytest.mark.slow
     def test_inference_no_head(self):
         model = RobertaModel.from_pretrained('roberta-base')
         

pytorch_transformers/tests/tokenization_roberta_test.py

@@ -18,8 +18,7 @@ import os
 import json
 import unittest
 
-from pytorch_transformers.tokenization_roberta import RobertaTokenizer, DICT_FILES_NAMES
-from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer, VOCAB_FILES_NAMES
+from pytorch_transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES
 from .tokenization_tests_commons import CommonTestCases
 
 
@@ -45,8 +44,7 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
             fp.write("\n".join(merges))
 
     def get_tokenizer(self):
-        bpe_tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
-        return RobertaTokenizer.from_pretrained("roberta-base", bpe_tokenizer=bpe_tokenizer)
+        return RobertaTokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
 
     def get_input_output_texts(self):
         input_text = u"lower newer"
@@ -54,15 +52,14 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
         return input_text, output_text
 
     def test_full_tokenizer(self):
-        tokenizer = self.get_tokenizer()
+        tokenizer = RobertaTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
         text = "lower"
         bpe_tokens = ["low", "er"]
         tokens = tokenizer.tokenize(text)
         self.assertListEqual(tokens, bpe_tokens)
 
         input_tokens = tokens + [tokenizer.unk_token]
-        input_bpe_tokens = [0, 4, 12, 176, 2]
-        tokenizer.convert_tokens_to_ids(input_tokens)
+        input_bpe_tokens = [13, 12, 17]
         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
 

pytorch_transformers/tokenization_roberta.py

@@ -12,229 +12,182 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Tokenization classes for RoBERTa."""
+"""Tokenization classes for OpenAI GPT."""
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
+import sys
 import json
 import logging
-import re
-from io import open
-import six
 import os
+import regex as re
+from io import open
 
+from .tokenization_gpt2 import bytes_to_unicode, get_pairs
 from .tokenization_utils import PreTrainedTokenizer
-from .tokenization_gpt2 import GPT2Tokenizer
+
+try:
+    from functools import lru_cache
+except ImportError:
+    # Just a dummy decorator to get the checks to run on python2
+    # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
+    def lru_cache():
+        return lambda func: func
 
 logger = logging.getLogger(__name__)
 
-DICT_FILES_NAMES = {
-    'dict_file': 'dict.txt',
+VOCAB_FILES_NAMES = {
+    'vocab_file': 'vocab.json',
+    'merges_file': 'merges.txt',
 }
 
-PRETRAINED_DICT_FILES_MAP = {
-    'dict_file':
+PRETRAINED_VOCAB_FILES_MAP = {
+    'vocab_file':
+    {
+        'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-vocab.json",
+        'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json",
+        'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-vocab.json",
+    },
+    'merges_file':
     {
-            'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
-            'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
-            'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
+        'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-merges.txt",
+        'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt",
+        'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-merges.txt",
     },
 }
 
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    'roberta-base': 512,
-    'roberta-large': 512,
-    'roberta-large-mnli': 512,
+    'roberta-base': 1024,
+    'roberta-large': 1024,
+    'roberta-large-mnli': 1024,
 }
 
-SPACE_NORMALIZER = re.compile(r"\s+")
-
-def tokenize_line(line):
-    line = SPACE_NORMALIZER.sub(" ", line)
-    line = line.strip()
-    return line.split()
-
-
-class Dictionary(object):
-    """
-    A mapping from symbols to consecutive integers
-
-    From Facebook's fairseq.
-    """
-
-    def __init__(
-        self,
-        pad='<pad>',
-        eos='</s>',
-        unk='<unk>',
-        bos='<s>',
-        extra_special_symbols=None,
-    ):
-        self.unk_word, self.pad_word, self.eos_word = unk, pad, eos
-        self.symbols = []
-        self.count = []
-        self.indices = {}
-        self.bos_index = self.add_symbol(bos)
-        self.pad_index = self.add_symbol(pad)
-        self.eos_index = self.add_symbol(eos)
-        self.unk_index = self.add_symbol(unk)
-        if extra_special_symbols:
-            for s in extra_special_symbols:
-                self.add_symbol(s)
-        self.nspecial = len(self.symbols)
-
-    def __getitem__(self, idx):
-        if idx < len(self.symbols):
-            return self.symbols[idx]
-        return self.unk_word
-
-    def index(self, sym):
-        """Returns the index of the specified symbol"""
-        assert isinstance(sym, str)
-        if sym in self.indices:
-            return self.indices[sym]
-        return self.unk_index
-
-    def add_symbol(self, word, n=1):
-        """Adds a word to the dictionary"""
-        if word in self.indices:
-            idx = self.indices[word]
-            self.count[idx] = self.count[idx] + n
-            return idx
-        else:
-            idx = len(self.symbols)
-            self.indices[word] = idx
-            self.symbols.append(word)
-            self.count.append(n)
-            return idx
-
-    @classmethod
-    def load(cls, f, ignore_utf_errors=False):
-        """Loads the dictionary from a text file with the format:
-
-        ```
-        <symbol0> <count0>
-        <symbol1> <count1>
-        ...
-        ```
-        """
-        d = cls()
-        d.add_from_file(f, ignore_utf_errors)
-        return d
-
-    def add_from_file(self, f, ignore_utf_errors=False):
-        """
-        Loads a pre-existing dictionary from a text file and adds its symbols
-        to this instance.
-        """
-        if isinstance(f, six.string_types):
-            try:
-                if not ignore_utf_errors:
-                    with open(f, 'r', encoding='utf-8') as fd:
-                        self.add_from_file(fd)
-                else:
-                    with open(f, 'r', encoding='utf-8', errors='ignore') as fd:
-                        self.add_from_file(fd)
-            except FileNotFoundError as fnfe:
-                raise fnfe
-            except UnicodeError:
-                raise Exception("Incorrect encoding detected in {}, please "
-                                "rebuild the dataset".format(f))
-            return
-
-        lines = f.read().splitlines()
-        for line in lines:
-            idx = line.rfind(' ')
-            if idx == -1:
-                raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
-            word = line[:idx]
-            count = int(line[idx + 1:])
-            self.indices[word] = len(self.symbols)
-            self.symbols.append(word)
-            self.count.append(count)
-
-    def encode_line(self, line, line_tokenizer=tokenize_line, add_if_not_exist=True,
-                    consumer=None, append_eos=True, reverse_order=False):
-        words = line_tokenizer(line)
-        if reverse_order:
-            words = list(reversed(words))
-        nwords = len(words)
-        ids = [0] * (nwords + 1 if append_eos else nwords)
-
-        for i, word in enumerate(words):
-            if add_if_not_exist:
-                idx = self.add_symbol(word)
-            else:
-                idx = self.index(word)
-            if consumer is not None:
-                consumer(word, idx)
-            ids[i] = idx
-        if append_eos:
-            ids[nwords] = self.eos_index
-        return ids
-
 
 class RobertaTokenizer(PreTrainedTokenizer):
     """
-    RoBERTa tokenizer. Peculiarities:
-        - GPT-2 tokenizer with a different integer mapping on top.
+    GPT-2 BPE tokenizer. Peculiarities:
+        - Byte-level BPE
     """
-    vocab_files_names = DICT_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_DICT_FILES_MAP
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
-    def __init__(self, dict_file, bpe_tokenizer=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>",
-                 unk_token="<unk>", **kwargs):
-        super(RobertaTokenizer, self).__init__(cls_token=bos_token, sep_token=eos_token, eos_token=eos_token,
-                                               unk_token=unk_token, **kwargs)
+    def __init__(self, vocab_file, merges_file, errors='replace', bos_token="<s>", eos_token="</s>", sep_token="</s>",
+                 cls_token="<s>", unk_token="<unk>", **kwargs):
+        super(RobertaTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token,
+                                               sep_token=sep_token, cls_token=cls_token, **kwargs)
 
-        self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") if bpe_tokenizer is None else bpe_tokenizer
-        self.dictionary = Dictionary.load(dict_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()
+        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.dictionary.indices)
+        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):
-        """ Use GPT-2 Tokenizer """
-        return self.gpt2_tokenizer._tokenize(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):
-        if self.dictionary.index(token) != 3:
-            return self.dictionary.index(token)
-        return self.dictionary.index(str(self.gpt2_tokenizer.convert_tokens_to_ids(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):
-        symbol = self.dictionary[index]
-        try:
-            idx = int(symbol)
-            return self.gpt2_tokenizer._convert_id_to_token(idx)
-        except ValueError:
-            return symbol
+        """Converts an index (integer) in a token (string/unicode) using the vocab."""
+        return self.decoder.get(index)
 
     def convert_tokens_to_string(self, tokens):
-        return self.gpt2_tokenizer.convert_tokens_to_string(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 convert_tokens_to_ids(self, tokens, no_sep_cls_tokens=False):
-        cls = [self._convert_token_to_id(self.cls_token)]
-        tokens = super().convert_tokens_to_ids(tokens)
-        sep = [self._convert_token_to_id(self.sep_token)]
-        return (cls + tokens + sep) if (isinstance(tokens, list) and not no_sep_cls_tokens) else tokens
+    def add_special_tokens_single_sentence(self, token_ids):
+        return [self._convert_token_to_id(self.cls_token)] + token_ids + [self._convert_token_to_id(self.sep_token)]
 
-    def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
-        return super().convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens)[1:-1]
+    def add_special_tokens_sentences_pair(self, *token_ids):
+        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
-        dict_file = os.path.join(save_directory, DICT_FILES_NAMES['dict_file'])
-
-        with open(dict_file, 'w', encoding='utf-8') as f:
-            for i in range(self.dictionary.nspecial, len(self.dictionary.count)):
-                f.write(f"{list(self.dictionary.indices.keys())[i]} {self.dictionary.count[i]}\n")
-
-        vocab_files = self.gpt2_tokenizer.save_pretrained(save_directory)
-
-        return vocab_files + (dict_file,)
+        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