[`Tokenizer`] Fix slow and fast serialization (#26570)

* fix

* last attempt

* current work

* fix forward compatibility

* save all special tokens

* current state

* revert additional changes

* updates

* remove tokenizer.model

* add a test and the fix

* nit

* revert one more break

* fix typefield issue

* quality

* more tests

* fix fields for FC

* more nits?

* new additional changes

* how

* some updates

* simplify all

* more nits

* revert some things to original

* nice

* nits

* a small hack

* more nits

* ahhaha

* fixup

* update

* make test run on ci

* use subtesting

* update

* Update .circleci/create_circleci_config.py

* updates

* fixup

* nits

* replace typo

* fix the test

* nits

* update

* None max dif pls

* a partial fix

* had to revert one thing

* test the fast

* updates

* fixup

* and more nits

* more fixes

* update

* Oupsy 👁



* nits

* fix marian

* on our way to heaven

* Update src/transformers/models/t5/tokenization_t5.py
Co-authored-by: Lysandre Debut <hi@lysand.re>

* fixup

* Update src/transformers/tokenization_utils_fast.py
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>

* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>

* fix phobert

* skip some things, test more

* nits

* fixup

* fix deberta

* update

* update

* more updates

* skip one test

* more updates

* fix camembert

* can't test this one

* more good fixes

* kind of a major update

- seperate what is only done in fast in fast init and refactor
- add_token(AddedToken(..., speicla = True)) ignores it in fast
- better loading

* fixup

* more fixups

* fix pegasus and mpnet

* remove skipped tests

* fix phoneme tokenizer if self.verbose

* fix individual models

* update common tests

* update testing files

* all over again

* nits

* skip test for markup lm

* fixups

* fix order of addition in fast by sorting the added tokens decoder

* proper defaults for deberta

* correct default for fnet

* nits on add tokens, string initialized to special if special

* skip irrelevant herbert tests

* main fixes

* update test added_tokens_serialization

* the fix for bart like models and class instanciating

* update bart

* nit!

* update idefix test

* fix whisper!

* some fixup

* fixups

* revert some of the wrong chanegs

* fixup

* fixup

* skip marian

* skip the correct tests

* skip for tf and flax as well

---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>

.circleci/create_circleci_config.py

@@ -127,6 +127,7 @@ class CircleCIJob:
             },
         ]
         steps.extend([{"run": l} for l in self.install_steps])
+        steps.extend([{"run": "pip install pytest-subtests"}])
         steps.append(
             {
                 "save_cache": {

src/transformers/convert_slow_tokenizer.py

@@ -1168,9 +1168,9 @@ class LlamaConverter(SpmConverter):
             )
             tokenizer.add_special_tokens(
                 [
-                    AddedToken("<unk>"),
-                    AddedToken("<s>"),
-                    AddedToken("</s>"),
+                    AddedToken("<unk>", normalized=False, special=True),
+                    AddedToken("<s>", normalized=False, special=True),
+                    AddedToken("</s>", normalized=False, special=True),
                 ]
             )
         else:

src/transformers/models/bart/tokenization_bart.py

@@ -204,8 +204,6 @@ class BartTokenizer(PreTrainedTokenizer):
         pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
 
         # Mask token behave like a normal word, i.e. include the space before it
-        # TODO seems like both slow and fast actually don't strip left and right soooooooo yeah. See `test_embeded_special_tokens`
-        # Also this not only will strip the spaces but any punctuation
         mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
 
         with open(vocab_file, encoding="utf-8") as vocab_handle:

src/transformers/models/bart/tokenization_bart_fast.py

@@ -170,7 +170,12 @@ class BartTokenizerFast(PreTrainedTokenizerFast):
         trim_offsets=True,
         **kwargs,
     ):
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+        # we have to specify that this tokens is special otherwise adding it will reset the normalized flag to `False` in `add_special_tokens`
+        mask_token = (
+            AddedToken(mask_token, lstrip=True, normalized=True, special=True)
+            if isinstance(mask_token, str)
+            else mask_token
+        )
         super().__init__(
             vocab_file,
             merges_file,

src/transformers/models/barthez/tokenization_barthez.py

@@ -136,8 +136,8 @@ class BarthezTokenizer(PreTrainedTokenizer):
         sp_model_kwargs: Optional[Dict[str, Any]] = None,
         **kwargs,
     ) -> None:
-        # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+        # Mask token behave like a normal word, i.e. include the space before it. Will have normalized=False by default this way
+        mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
 
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 

src/transformers/models/bertweet/tokenization_bertweet.py

@@ -149,10 +149,10 @@ class BertweetTokenizer(PreTrainedTokenizer):
         self.merges_file = merges_file
 
         self.encoder = {}
-        self.encoder[bos_token] = 0
-        self.encoder[pad_token] = 1
-        self.encoder[eos_token] = 2
-        self.encoder[unk_token] = 3
+        self.encoder[str(bos_token)] = 0
+        self.encoder[str(pad_token)] = 1
+        self.encoder[str(eos_token)] = 2
+        self.encoder[str(unk_token)] = 3
 
         self.add_from_file(vocab_file)
 

src/transformers/models/camembert/tokenization_camembert.py

@@ -89,7 +89,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
         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.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `['<s>NOTUSED', '</s>NOTUSED']`):
+        additional_special_tokens (`List[str]`, *optional*, defaults to `['<s>NOTUSED', '</s>NOTUSED', '<unk>NOTUSED']`):
             Additional special tokens used by the tokenizer.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
@@ -127,12 +127,16 @@ class CamembertTokenizer(PreTrainedTokenizer):
         unk_token="<unk>",
         pad_token="<pad>",
         mask_token="<mask>",
-        additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED"],
+        additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED", "<unk>NOTUSED"],
         sp_model_kwargs: Optional[Dict[str, Any]] = None,
         **kwargs,
     ) -> None:
         # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+        mask_token = (
+            AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False, special=True)
+            if isinstance(mask_token, str)
+            else mask_token
+        )
 
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 
@@ -144,11 +148,11 @@ class CamembertTokenizer(PreTrainedTokenizer):
         # sentencepiece vocabulary (this is the case for <s> and </s> and <unk>).
         # In this case it is recommended to properly set the tokens by hand.
         self._added_tokens_decoder = {
-            0: AddedToken("<s>NOTUSED"),
-            1: AddedToken(pad_token),
-            2: AddedToken("</s>NOTUSED"),
-            3: AddedToken(unk_token),
-            4: AddedToken("<unk>NOTUSED"),
+            0: AddedToken("<s>NOTUSED", special=True),
+            1: AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token,
+            2: AddedToken("</s>NOTUSED", special=True),
+            3: AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token,
+            4: AddedToken("<unk>NOTUSED", special=True),
         }
 
         self.fairseq_offset = 4  # 3 tokens are newly added, but the offset starts from 4

src/transformers/models/camembert/tokenization_camembert_fast.py

@@ -119,12 +119,11 @@ class CamembertTokenizerFast(PreTrainedTokenizerFast):
         unk_token="<unk>",
         pad_token="<pad>",
         mask_token="<mask>",
-        additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED"],
+        additional_special_tokens=["<s>NOTUSED", "</s>NOTUSED", "<unk>NOTUSED"],
         **kwargs,
     ):
-        # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
-
+        # Mask token behave like a normal word, i.e. include the space before it. Will have normalized = False
+        mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
         super().__init__(
             vocab_file,
             tokenizer_file=tokenizer_file,

src/transformers/models/codegen/tokenization_codegen.py

@@ -163,10 +163,10 @@ class CodeGenTokenizer(PreTrainedTokenizer):
         add_bos_token=False,
         **kwargs,
     ):
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
         self.add_bos_token = add_bos_token
 
         with open(vocab_file, encoding="utf-8") as vocab_handle:

src/transformers/models/deberta/tokenization_deberta.py

@@ -192,12 +192,12 @@ class DebertaTokenizer(PreTrainedTokenizer):
         add_bos_token=False,
         **kwargs,
     ):
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
-        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
+        sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
+        cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
+        unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
 
         # Mask token behave like a normal word, i.e. include the space before it
         mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

src/transformers/models/deberta_v2/tokenization_deberta_v2.py

@@ -138,7 +138,7 @@ class DebertaV2Tokenizer(PreTrainedTokenizer):
         self._tokenizer = SPMTokenizer(
             vocab_file, None, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs
         )
-        unk_token = AddedToken(unk_token, normalized=True, lstrip=False, rstrip=False)
+        unk_token = AddedToken(unk_token, normalized=True, special=True) if isinstance(unk_token, str) else unk_token
         super().__init__(
             do_lower_case=do_lower_case,
             bos_token=bos_token,

src/transformers/models/fnet/tokenization_fnet.py

@@ -116,9 +116,10 @@ class FNetTokenizer(PreTrainedTokenizer):
     ) -> None:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
-        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
-        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+        mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
+        cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
+        sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
+        mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 
         self.do_lower_case = do_lower_case

src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py

@@ -20,7 +20,7 @@ import sys
 import unicodedata
 from typing import Dict, List, Optional, Tuple, Union
 
-from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
 from ...tokenization_utils_base import (
     BatchEncoding,
     EncodedInput,
@@ -244,6 +244,12 @@ class LayoutLMv2Tokenizer(PreTrainedTokenizer):
         additional_special_tokens: Optional[List[str]] = None,
         **kwargs,
     ):
+        sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
+        unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
+        cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
+        mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token
+
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"

src/transformers/models/layoutxlm/tokenization_layoutxlm.py

@@ -248,7 +248,7 @@ class LayoutXLMTokenizer(PreTrainedTokenizer):
         **kwargs,
     ) -> None:
         # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+        mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
 
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 

src/transformers/models/led/tokenization_led.py

@@ -197,8 +197,6 @@ class LEDTokenizer(PreTrainedTokenizer):
         pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
 
         # Mask token behave like a normal word, i.e. include the space before it
-        # TODO seems like both slow and fast actually don't strip left and right soooooooo yeah. See `test_embeded_special_tokens`
-        # Also this not only will strip the spaces but any punctuation
         mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
 
         with open(vocab_file, encoding="utf-8") as vocab_handle:

src/transformers/models/led/tokenization_led_fast.py

@@ -152,7 +152,12 @@ class LEDTokenizerFast(PreTrainedTokenizerFast):
         trim_offsets=True,
         **kwargs,
     ):
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+        # we have to specify that this tokens is special otherwise adding it will reset the normalized flag to `False` in `add_special_tokens`
+        mask_token = (
+            AddedToken(mask_token, lstrip=True, normalized=True, special=True)
+            if isinstance(mask_token, str)
+            else mask_token
+        )
         super().__init__(
             vocab_file,
             merges_file,

src/transformers/models/llama/tokenization_llama.py

@@ -155,10 +155,10 @@ class LlamaTokenizer(PreTrainedTokenizer):
         **kwargs,
     ):
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
 
         if legacy is None:
             logger.warning_once(

src/transformers/models/marian/tokenization_marian.py

@@ -148,9 +148,9 @@ class MarianTokenizer(PreTrainedTokenizer):
 
         self.separate_vocabs = separate_vocabs
         self.encoder = load_json(vocab)
-        if unk_token not in self.encoder:
+        if str(unk_token) not in self.encoder:
             raise KeyError("<unk> token must be in the vocab")
-        assert pad_token in self.encoder
+        assert str(pad_token) in self.encoder
 
         if separate_vocabs:
             self.target_encoder = load_json(target_vocab_file)

src/transformers/models/mbart/tokenization_mbart.py

@@ -97,7 +97,9 @@ class MBartTokenizer(PreTrainedTokenizer):
         **kwargs,
     ):
         # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+        mask_token = (
+            AddedToken(mask_token, lstrip=True, normalized=False) if isinstance(mask_token, str) else mask_token
+        )
 
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 

src/transformers/models/mbart50/tokenization_mbart50.py

@@ -132,7 +132,7 @@ class MBart50Tokenizer(PreTrainedTokenizer):
 
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 
-        kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
+        kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or []
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]