[Tokenizer] Add BertTokenizerFast, support register new tokenizer (#9353

)

* Add , support register tokenizer, fix some typo

* add more tests

* CustomTokenizerFast2->CustomTokenizerFastWithoutSlow

* lint

paddlenlp/transformers/auto/tokenizer.py

@@ -24,6 +24,7 @@
 from ...utils.import_utils import import_module
 from ...utils.log import logger
 from ..configuration_utils import PretrainedConfig
+from ..tokenizer_utils import PretrainedTokenizer
 from ..tokenizer_utils_base import TOKENIZER_CONFIG_FILE
 from ..tokenizer_utils_fast import PretrainedTokenizerFast
 from .configuration import (
@@ -45,7 +46,13 @@
         [
             ("albert", (("AlbertChineseTokenizer", "AlbertEnglishTokenizer"), None)),
             ("bart", "BartTokenizer"),
-            ("bert", "BertTokenizer"),
+            (
+                "bert",
+                (
+                    "BertTokenizer",
+                    "BertTokenizerFast" if is_tokenizers_available() else None,
+                ),
+            ),
             ("blenderbot", "BlenderbotTokenizer"),
             ("bloom", "BloomTokenizer"),
             ("clip", "CLIPTokenizer"),
@@ -459,3 +466,46 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
             "- or a correct model-identifier of community-contributed pretrained models,\n"
             "- or the correct path to a directory containing relevant tokenizer files.\n"
         )
+
+    def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None, exist_ok=False):
+        """
+        Register a new tokenizer in this mapping.
+
+
+        Args:
+            config_class ([`PretrainedConfig`]):
+                The configuration corresponding to the model to register.
+            slow_tokenizer_class ([`PretrainedTokenizer`], *optional*):
+                The slow tokenizer to register.
+            fast_tokenizer_class ([`PretrainedTokenizerFast`], *optional*):
+                The fast tokenizer to register.
+        """
+        if slow_tokenizer_class is None and fast_tokenizer_class is None:
+            raise ValueError("You need to pass either a `slow_tokenizer_class` or a `fast_tokenizer_class")
+        if slow_tokenizer_class is not None and issubclass(slow_tokenizer_class, PretrainedTokenizerFast):
+            raise ValueError("You passed a fast tokenizer in the `slow_tokenizer_class`.")
+        if fast_tokenizer_class is not None and issubclass(fast_tokenizer_class, PretrainedTokenizer):
+            raise ValueError("You passed a slow tokenizer in the `fast_tokenizer_class`.")
+
+        if (
+            slow_tokenizer_class is not None
+            and fast_tokenizer_class is not None
+            and issubclass(fast_tokenizer_class, PretrainedTokenizerFast)
+            and fast_tokenizer_class.slow_tokenizer_class != slow_tokenizer_class
+        ):
+            raise ValueError(
+                "The fast tokenizer class you are passing has a `slow_tokenizer_class` attribute that is not "
+                "consistent with the slow tokenizer class you passed (fast tokenizer has "
+                f"{fast_tokenizer_class.slow_tokenizer_class} and you passed {slow_tokenizer_class}. Fix one of those "
+                "so they match!"
+            )
+
+        # Avoid resetting a set slow/fast tokenizer if we are passing just the other ones.
+        if config_class in TOKENIZER_MAPPING._extra_content:
+            existing_slow, existing_fast = TOKENIZER_MAPPING[config_class]
+            if slow_tokenizer_class is None:
+                slow_tokenizer_class = existing_slow
+            if fast_tokenizer_class is None:
+                fast_tokenizer_class = existing_fast
+
+        TOKENIZER_MAPPING.register(config_class, (slow_tokenizer_class, fast_tokenizer_class), exist_ok=exist_ok)

paddlenlp/transformers/bert/tokenizer_fast.py

@@ -0,0 +1,164 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import json
+from typing import List, Optional, Tuple
+
+from tokenizers import normalizers
+
+from ..tokenizer_utils_fast import PretrainedTokenizerFast
+from .tokenizer import BertTokenizer
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+
+class BertTokenizerFast(PretrainedTokenizerFast):
+    r"""
+
+    This tokenizer inherits from [`PretrainedTokenizerFast`] which contains most of the main methods. Users should
+    refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            File containing the vocabulary.
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        clean_text (`bool`, *optional*, defaults to `True`):
+            Whether or not to clean the text before tokenization by removing any control characters and replacing all
+            whitespaces by the classic one.
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese.
+        strip_accents (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+        wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
+            The prefix for subwords.
+    """
+
+    resource_files_names = VOCAB_FILES_NAMES
+    slow_tokenizer_class = BertTokenizer
+
+    def __init__(
+        self,
+        vocab_file=None,
+        tokenizer_file=None,
+        do_lower_case=True,
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        tokenize_chinese_chars=True,
+        strip_accents=None,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_file,
+            tokenizer_file=tokenizer_file,
+            do_lower_case=do_lower_case,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            tokenize_chinese_chars=tokenize_chinese_chars,
+            strip_accents=strip_accents,
+            **kwargs,
+        )
+
+        normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+        if (
+            normalizer_state.get("lowercase", do_lower_case) != do_lower_case
+            or normalizer_state.get("strip_accents", strip_accents) != strip_accents
+            or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
+        ):
+            normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
+            normalizer_state["lowercase"] = do_lower_case
+            normalizer_state["strip_accents"] = strip_accents
+            normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
+            self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
+
+        self.do_lower_case = do_lower_case
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+        if token_ids_1 is not None:
+            output += token_ids_1 + [self.sep_token_id]
+
+        return output
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
+        pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+        return tuple(files)

paddlenlp/transformers/convert_slow_tokenizer.py

@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import OrderedDict
 from typing import Dict, List, Optional, Tuple
 
 import tokenizers
@@ -28,7 +29,7 @@
     pre_tokenizers,
     processors,
 )
-from tokenizers.models import BPE, Unigram
+from tokenizers.models import BPE, Unigram, WordPiece
 
 from paddlenlp.utils.import_utils import (
     is_protobuf_available,
@@ -330,6 +331,50 @@ def converted(self) -> Tokenizer:
         return tokenizer
 
 
+class BertConverter(Converter):
+    def converted(self) -> Tokenizer:
+        vocab = self.original_tokenizer.vocab
+        tokenizer = Tokenizer(
+            WordPiece(
+                OrderedDict([(vocab._idx_to_token[i], i) for i in range(len(vocab))]),
+                unk_token=str(self.original_tokenizer.unk_token),
+            )
+        )
+
+        tokenize_chinese_chars = False
+        strip_accents = False
+        do_lower_case = False
+        if hasattr(self.original_tokenizer, "basic_tokenizer"):
+            tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars
+            strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
+            do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
+
+        tokenizer.normalizer = normalizers.BertNormalizer(
+            clean_text=True,
+            handle_chinese_chars=tokenize_chinese_chars,
+            strip_accents=strip_accents,
+            lowercase=do_lower_case,
+        )
+        tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
+
+        cls = str(self.original_tokenizer.cls_token)
+        sep = str(self.original_tokenizer.sep_token)
+        cls_token_id = self.original_tokenizer.cls_token_id
+        sep_token_id = self.original_tokenizer.sep_token_id
+
+        tokenizer.post_processor = processors.TemplateProcessing(
+            single=f"{cls}:0 $A:0 {sep}:0",
+            pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
+            special_tokens=[
+                (cls, cls_token_id),
+                (sep, sep_token_id),
+            ],
+        )
+        tokenizer.decoder = decoders.WordPiece(prefix="##")
+
+        return tokenizer
+
+
 class LlamaConverter(SpmConverter):
     handle_byte_fallback = True
 
@@ -399,6 +444,7 @@ def pre_tokenizer(self, replacement, add_prefix_space):
 
 SLOW_TO_FAST_CONVERTERS = {
     "LlamaTokenizer": LlamaConverter,
+    "BertTokenizer": BertConverter,
 }
 
 

paddlenlp/transformers/tokenizer_utils_base.py

@@ -1851,6 +1851,9 @@ def convert_added_tokens(obj: Union[AddedToken, Any], add_type_field=True):
 
         # Add tokenizer class to the tokenizer config to be able to reload it with from_pretrained
         tokenizer_class = self.__class__.__name__
+        # Remove the Fast at the end unless we have a special `PreTrainedTokenizerFast`
+        if tokenizer_class.endswith("Fast") and tokenizer_class != "PreTrainedTokenizerFast":
+            tokenizer_class = tokenizer_class[:-4]
         tokenizer_config["tokenizer_class"] = tokenizer_class
 
         with io.open(tokenizer_config_file, "w", encoding="utf-8") as f:

paddlenlp/transformers/tokenizer_utils_fast.py

@@ -750,7 +750,7 @@ def train_new_from_iterator(
                 Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.
 
         Returns:
-            [`PreTrainedTokenizerFast`]: A new tokenizer of the same type as the original one, trained on
+            [`PretrainedTokenizerFast`]: A new tokenizer of the same type as the original one, trained on
             `text_iterator`.
 
         """

tests/transformers/auto/test_confiugration.py

@@ -23,9 +23,10 @@
 from paddlenlp.transformers import AutoConfig
 from paddlenlp.transformers.auto.configuration import CONFIG_MAPPING
 from paddlenlp.transformers.bert.configuration import BertConfig
-from paddlenlp.transformers.configuration_utils import PretrainedConfig
 from paddlenlp.utils.env import CONFIG_NAME
 
+from ...utils.test_module.custom_configuration import CustomConfig
+
 
 class AutoConfigTest(unittest.TestCase):
     def test_built_in_model_class_config(self):
@@ -90,13 +91,6 @@ def test_load_from_legacy_config(self):
             self.assertEqual(auto_config.hidden_size, number)
 
     def test_new_config_registration(self):
-        class CustomConfig(PretrainedConfig):
-            model_type = "custom"
-
-            def __init__(self, attribute=1, **kwargs):
-                self.attribute = attribute
-                super().__init__(**kwargs)
-
         try:
             AutoConfig.register("custom", CustomConfig)
             # Wrong model type will raise an error