Support reading tiktoken tokenizer.model file (#31656)

* use existing TikTokenConverter to read tiktoken tokenizer.model file

* del test file

* create titktoken integration file

* adding tiktoken llama test

* ALTNATIVE IMPLEMENTATION: supports llama 405B

* fix one char

* remove redundant line

* small fix

* rm unused import

* flag for converting from tiktokeng

* remove unneeded file

* ruff

* remove llamatiktokenconverter, stick to general converter

* tiktoken support v2

* update test

* remove stale changes

* udpate doc

* protect import

* use is_protobuf_available

* add templateprocessor in tiktokenconverter

* reverting templateprocessor from tiktoken support

* update test

* add require_tiktoken

* dev-ci

* trigger build

* trigger build again

* dev-ci

* [build-ci-image] tiktoken

* dev-ci

* dev-ci

* dev-ci

* dev-ci

* change tiktoken file name

* feedback review

* feedback rev

* applying feedback, removing tiktoken converters

* conform test

* adding docs for review

* add doc file for review

* add doc file for review

* add doc file for review

* support loading model without config.json file

* Revert "support loading model without config.json file"

This reverts commit 2753602e51c34cef2f184eb11f36d2ad1b02babb.

* remove dev var

* updating docs

* safely import protobuf

* fix protobuf import error

* fix protobuf import error

* trying isort to fix ruff error

* fix ruff error

* try to fix ruff again

* try to fix ruff again

* try to fix ruff again

* doc table of contents

* add fix for consistency.dockerfile torchaudio

* ruff

* applying feedback

* minor typo

* merging with push-ci-image

* clean up imports

* revert dockerfile consistency

docker/torch-light.dockerfile

@@ -7,5 +7,5 @@ ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
+RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken]"
 RUN pip uninstall -y transformers

docs/source/en/_toctree.yml

@@ -145,6 +145,8 @@
     title: Troubleshoot
   - local: gguf
     title: Interoperability with GGUF files
+  - local: tiktoken
+    title: Interoperability with TikToken files
   title: Developer guides
 - sections:
   - local: quantization/overview

docs/source/en/tiktoken.md

@@ -0,0 +1,38 @@
+<!--Copyright 2024 The HuggingFace Team. 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.
+``
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Tiktoken and interaction with Transformers
+
+Support for tiktoken model files is seamlessly integrated in 🤗 transformers when loading models 
+`from_pretrained` with a `tokenizer.model` tiktoken file on the Hub, which is automatically converted into our 
+[fast tokenizer](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast). 
+
+### Known models that were released with a `tiktoken.model`:
+	- gpt2
+	- llama3
+
+## Example usage
+ 
+In order to load `tiktoken` files in `transformers`, ensure that the `tokenizer.model` file is a tiktoken file and it 
+will automatically be loaded when loading `from_pretrained`. Here is how one would load a tokenizer and a model, which 
+ can be loaded from the exact same file:
+
+```py
+from transformers import AutoTokenizer
+
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id, subfolder="original") 
+```

setup.py

@@ -99,6 +99,7 @@ _deps = [
     "accelerate>=0.26.0",
     "av==9.2.0",  # Latest version of PyAV (10.0.0) has issues with audio stream.
     "beautifulsoup4",
+    "blobfile",
     "codecarbon==1.2.0",
     "cookiecutter==1.7.3",
     "dataclasses",
@@ -177,6 +178,7 @@ _deps = [
     "tensorflow-probability<0.24",
     "tf2onnx",
     "timeout-decorator",
+    "tiktoken",
     "timm<=0.9.16",
     "tokenizers>=0.19,<0.20",
     "torch",
@@ -311,6 +313,7 @@ extras["codecarbon"] = deps_list("codecarbon")
 extras["video"] = deps_list("decord", "av")
 
 extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
+extras["tiktoken"] = deps_list("tiktoken", "blobfile")
 extras["testing"] = (
     deps_list(
         "pytest",

src/transformers/convert_slow_tokenizer.py

@@ -26,10 +26,13 @@ from packaging import version
 from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
 from tokenizers.models import BPE, Unigram, WordPiece
 
-from .utils import is_protobuf_available, requires_backends
+from .utils import is_protobuf_available, logging, requires_backends
 from .utils.import_utils import PROTOBUF_IMPORT_ERROR
 
 
+logger = logging.get_logger(__name__)
+
+
 def import_protobuf(error_message=""):
     if is_protobuf_available():
         import google.protobuf
@@ -1451,12 +1454,15 @@ class TikTokenConverter:
         vocab_file=None,
         pattern=r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""",
         add_prefix_space=False,
+        additional_special_tokens=None,
         *args,
+        **kwargs,
     ):
         super().__init__(*args)
         self.vocab_file = vocab_file
         self.pattern = pattern
         self.add_prefix_space = add_prefix_space
+        self.additional_special_tokens = additional_special_tokens
 
     def extract_vocab_merges_from_model(self, tiktoken_url: str):
         try:
@@ -1505,7 +1511,10 @@ class TikTokenConverter:
             ]
         )
         tokenizer.decoder = decoders.ByteLevel()
+        tokenizer.add_special_tokens(self.additional_special_tokens)
+
         tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
+
         return tokenizer
 
 
@@ -1569,7 +1578,7 @@ SLOW_TO_FAST_CONVERTERS = {
 }
 
 
-def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer:
+def convert_slow_tokenizer(transformer_tokenizer, from_tiktoken=False) -> Tokenizer:
     """
     Utilities to convert a slow tokenizer instance in a fast tokenizer instance.
 
@@ -1577,6 +1586,8 @@ def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer:
         transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]):
             Instance of a slow tokenizer to convert in the backend tokenizer for
             [`~tokenization_utils_base.PreTrainedTokenizerFast`].
+       from_tiktoken (bool, optional): Whether to use the `tiktoken` library to convert the tokenizer instead of sentencepiece.
+            Defaults to False.
 
     Return:
         A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a
@@ -1584,14 +1595,20 @@ def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer:
     """
 
     tokenizer_class_name = transformer_tokenizer.__class__.__name__
-
-    if tokenizer_class_name not in SLOW_TO_FAST_CONVERTERS:
-        raise ValueError(
-            f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance."
-            " No converter was found. Currently available slow->fast convertors:"
-            f" {list(SLOW_TO_FAST_CONVERTERS.keys())}"
-        )
-
+    if tokenizer_class_name in SLOW_TO_FAST_CONVERTERS and not from_tiktoken:
         converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name]
-
         return converter_class(transformer_tokenizer).converted()
+
+    else:
+        try:
+            logger.info("Converting from Tiktoken")
+            return TikTokenConverter(
+                vocab_file=transformer_tokenizer.vocab_file,
+                additional_special_tokens=transformer_tokenizer.additional_special_tokens,
+            ).converted()
+        except Exception:
+            raise ValueError(
+                f"Converting from Tiktoken failed, if a converter for SentencePiece is available, provide a model path "
+                f"with a SentencePiece tokenizer.model file."
+                f"Currently available slow->fast convertors: {list(SLOW_TO_FAST_CONVERTERS.keys())}"
+            )

src/transformers/dependency_versions_table.py

@@ -6,6 +6,7 @@ deps = {
     "accelerate": "accelerate>=0.26.0",
     "av": "av==9.2.0",
     "beautifulsoup4": "beautifulsoup4",
+    "blobfile": "blobfile",
     "codecarbon": "codecarbon==1.2.0",
     "cookiecutter": "cookiecutter==1.7.3",
     "dataclasses": "dataclasses",
@@ -82,6 +83,7 @@ deps = {
     "tensorflow-probability": "tensorflow-probability<0.24",
     "tf2onnx": "tf2onnx",
     "timeout-decorator": "timeout-decorator",
+    "tiktoken": "tiktoken",
     "timm": "timm<=0.9.16",
     "tokenizers": "tokenizers>=0.19,<0.20",
     "torch": "torch",

src/transformers/testing_utils.py

@@ -114,6 +114,7 @@ from .utils import (
     is_tensorflow_text_available,
     is_tf2onnx_available,
     is_tf_available,
+    is_tiktoken_available,
     is_timm_available,
     is_tokenizers_available,
     is_torch_available,
@@ -1228,6 +1229,13 @@ def require_cython(test_case):
     return unittest.skipUnless(is_cython_available(), "test requires cython")(test_case)
 
 
+def require_tiktoken(test_case):
+    """
+    Decorator marking a test that requires TikToken. These tests are skipped when TikToken isn't installed.
+    """
+    return unittest.skipUnless(is_tiktoken_available(), "test requires TikToken")(test_case)
+
+
 def get_gpu_count():
     """
     Return the number of available gpus (regardless of whether torch, tf or jax is used)

src/transformers/tokenization_utils_base.py

@@ -53,6 +53,7 @@ from .utils import (
     is_mlx_available,
     is_numpy_array,
     is_offline_mode,
+    is_protobuf_available,
     is_remote_url,
     is_tf_available,
     is_tf_tensor,
@@ -65,6 +66,7 @@ from .utils import (
     to_py_obj,
 )
 from .utils.chat_template_utils import _compile_jinja_template, _render_with_assistant_indices
+from .utils.import_utils import PROTOBUF_IMPORT_ERROR
 
 
 if TYPE_CHECKING:
@@ -75,6 +77,16 @@ if TYPE_CHECKING:
     if is_flax_available():
         import jax.numpy as jnp  # noqa: F401
 
+
+def import_protobuf_decode_error(error_message=""):
+    if is_protobuf_available():
+        from google.protobuf.message import DecodeError
+
+        return DecodeError
+    else:
+        raise ImportError(PROTOBUF_IMPORT_ERROR.format(error_message))
+
+
 if is_tokenizers_available():
     from tokenizers import AddedToken
     from tokenizers import Encoding as EncodingFast
@@ -2434,6 +2446,19 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         # Instantiate the tokenizer.
         try:
             tokenizer = cls(*init_inputs, **init_kwargs)
+        except import_protobuf_decode_error():
+            logger.info(
+                "Unable to load tokenizer model from SPM, loading from TikToken will be attempted instead."
+                "(Google protobuf error: Tried to load SPM model with non-SPM vocab file).",
+            )
+            return False
+        except RuntimeError as e:
+            if "sentencepiece_processor.cc" in str(e):
+                logger.info(
+                    "Unable to load tokenizer model from SPM, loading from TikToken will be attempted instead."
+                    "(SentencePiece RuntimeError: Tried to load SPM model with non-SPM vocab file).",
+                )
+            return False
         except OSError:
             raise OSError(
                 "Unable to load vocabulary from file. "

src/transformers/tokenization_utils_fast.py

@@ -54,6 +54,7 @@ logger = logging.get_logger(__name__)
 TOKENIZER_FILE = "tokenizer.json"
 SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
 TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
+TIKTOKEN_VOCAB_FILE = "tokenizer.model"
 
 # Slow tokenizers have an additional added tokens files
 ADDED_TOKENS_FILE = "added_tokens.json"
@@ -74,7 +75,7 @@ MODEL_TO_TRAINER_MAPPING = {
     "WordPiece": WordPieceTrainer,
 }
 
-VOCAB_FILES_NAMES = {"tokenizer_file": TOKENIZER_FILE}
+VOCAB_FILES_NAMES = {"tokenizer_file": TOKENIZER_FILE, "vocab_file": TIKTOKEN_VOCAB_FILE}
 
 
 @add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
@@ -113,7 +114,7 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
         elif fast_tokenizer_file is not None and not from_slow:
             # We have a serialization from tokenizers which let us directly build the backend
             fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
-        elif slow_tokenizer is not None:
+        elif slow_tokenizer:
             # We need to convert a slow tokenizer to build the backend
             fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
         elif gguf_file is not None:
@@ -123,22 +124,26 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
             tokenizer_dict = gguf_param["tokenizer"]
             tokenizer_config = gguf_param["tokenizer_config"]
             fast_tokenizer, additional_kwargs = convert_gguf_tokenizer(architecture, tokenizer_dict)
-
             kwargs.update(tokenizer_config)
             if len(additional_kwargs) > 0:
                 kwargs.update(additional_kwargs)
-
-        elif self.slow_tokenizer_class is not None:
+        elif self.slow_tokenizer_class is not None and slow_tokenizer is not False:
             # We need to create and convert a slow tokenizer to build the backend
             slow_tokenizer = self.slow_tokenizer_class(*args, **kwargs)
             fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
+        elif not slow_tokenizer:
+            # We tried loading a slow_tokenizer with spm and failed, try to load with tiktoken
+            self.vocab_file = kwargs.get("vocab_file", None)
+            self.additional_special_tokens = kwargs.get("additional_special_tokens", [])
+            fast_tokenizer = convert_slow_tokenizer(self, from_tiktoken=True)
+            slow_tokenizer = None
         else:
             raise ValueError(
                 "Couldn't instantiate the backend tokenizer from one of: \n"
                 "(1) a `tokenizers` library serialization file, \n"
                 "(2) a slow tokenizer instance to convert or \n"
                 "(3) an equivalent slow tokenizer class to instantiate and convert. \n"
-                "You need to have sentencepiece installed to convert a slow tokenizer to a fast one."
+                "You need to have sentencepiece or tiktoken installed to convert a slow tokenizer to a fast one."
             )
 
         self._tokenizer = fast_tokenizer

src/transformers/utils/__init__.py

@@ -188,6 +188,7 @@ from .import_utils import (
     is_tensorflow_text_available,
     is_tf2onnx_available,
     is_tf_available,
+    is_tiktoken_available,
     is_timm_available,
     is_tokenizers_available,
     is_torch_available,

src/transformers/utils/import_utils.py

@@ -179,6 +179,8 @@ _torchdistx_available = _is_package_available("torchdistx")
 _torchvision_available = _is_package_available("torchvision")
 _mlx_available = _is_package_available("mlx")
 _hqq_available = _is_package_available("hqq")
+_tiktoken_available = _is_package_available("tiktoken")
+_blobfile_available = _is_package_available("blobfile")
 _liger_kernel_available = _is_package_available("liger_kernel")
 
 
@@ -1171,6 +1173,10 @@ def is_mlx_available():
     return _mlx_available
 
 
+def is_tiktoken_available():
+    return _tiktoken_available and _blobfile_available
+
+
 def is_liger_kernel_available():
     if not _liger_kernel_available:
         return False

tests/models/llama/test_tokenization_llama.py

@@ -25,15 +25,19 @@ from huggingface_hub import hf_hub_download
 from transformers import (
     SPIECE_UNDERLINE,
     AddedToken,
+    AutoTokenizer,
     LlamaTokenizer,
     LlamaTokenizerFast,
+    PreTrainedTokenizerFast,
 )
 from transformers.convert_slow_tokenizer import convert_slow_tokenizer
 from transformers.testing_utils import (
     get_tests_dir,
     nested_simplify,
     require_jinja,
+    require_read_token,
     require_sentencepiece,
+    require_tiktoken,
     require_tokenizers,
     require_torch,
     slow,
@@ -832,3 +836,66 @@ class CommonSpmIntegrationTests(unittest.TestCase):
         self.assertEqual(input_ids, [284, 1, 156])
         tokens = self.tokenizer.tokenize("No <s> ▁He")
         self.assertEqual(tokens, ["▁No", "<s>", "▁He"])  # spaces are eaten by rstrip / lstrip
+
+
+@require_tiktoken
+@require_read_token
+class TikTokenIntegrationTests(unittest.TestCase):
+    """
+    A class that regroups important test to make sure that we properly handle the special tokens.
+    """
+
+    def test_tiktoken_llama(self):
+        model_path = "hf-internal-testing/Llama3-Instruct-Internal"
+        test_text = "This is a test sentence."
+        test_tokens = [128000, 2028, 374, 264, 1296, 11914, 13, 128001]
+        num_reserved_special_tokens = 256
+        special_tokens = [
+            "<|begin_of_text|>",
+            "<|end_of_text|>",
+            "<|reserved_special_token_0|>",
+            "<|reserved_special_token_1|>",
+            "<|reserved_special_token_2|>",
+            "<|reserved_special_token_3|>",
+            "<|start_header_id|>",
+            "<|end_header_id|>",
+            "<|reserved_special_token_4|>",
+            "<|eot_id|>",
+            "<|python_tag|>",  # end of turn
+        ] + [f"<|reserved_special_token_{i}|>" for i in range(5, num_reserved_special_tokens - 5)]
+
+        tiktoken_tokenizer = PreTrainedTokenizerFast.from_pretrained(
+            model_path,
+            additional_special_tokens=special_tokens,
+            bos_token="<|begin_of_text|>",
+            eos_token="<|end_of_text|>",
+        )
+        tokens = tiktoken_tokenizer.tokenize("<|begin_of_text|> " + test_text)
+        self.assertEqual(tokens[0], "<|begin_of_text|>")
+
+        tiktoken_tokenizer = AutoTokenizer.from_pretrained(
+            model_path, legacy=False, additional_special_tokens=special_tokens, add_bos_token=True, add_eos_token=True
+        )
+        self.assertTrue(isinstance(tiktoken_tokenizer, PreTrainedTokenizerFast))
+
+        tokens = tiktoken_tokenizer.encode(test_text, add_special_tokens=True)
+        self.assertEqual(tokens, test_tokens)
+
+        tmpdirname = tempfile.mkdtemp()
+        tiktoken_tokenizer.save_pretrained(tmpdirname)
+        tokenizer_reload = AutoTokenizer.from_pretrained(tmpdirname)
+
+        self.assertTrue(isinstance(tokenizer_reload, PreTrainedTokenizerFast))
+        tokens = tokenizer_reload.encode(test_text, add_special_tokens=True)
+        self.assertEqual(tokens, test_tokens)
+        shutil.rmtree(tmpdirname)
+
+        tiktoken_tokenizer = AutoTokenizer.from_pretrained(
+            model_path,
+            additional_special_tokens=special_tokens,
+            from_slow=True,
+            add_bos_token=True,
+            add_eos_token=True,
+        )
+        tokens = tiktoken_tokenizer.encode(test_text, add_special_tokens=True)
+        self.assertEqual(tokens, test_tokens)