[Refactor] TokenizerRegistry only uses lazy imports (#30609)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/test_inputs.py

@@ -7,7 +7,7 @@ from vllm.config import ModelConfig
 from vllm.inputs import zip_enc_dec_prompts
 from vllm.inputs.parse import parse_raw_prompts
 from vllm.inputs.preprocess import InputPreprocessor
-from vllm.tokenizers import init_tokenizer_from_config
+from vllm.tokenizers import cached_tokenizer_from_config
 
 pytestmark = pytest.mark.cpu_test
 
@@ -108,7 +108,7 @@ def test_zip_enc_dec_prompts(mm_processor_kwargs, expected_mm_kwargs):
 )
 def test_preprocessor_always_mm_code_path(model_id, prompt):
     model_config = ModelConfig(model=model_id)
-    tokenizer = init_tokenizer_from_config(model_config)
+    tokenizer = cached_tokenizer_from_config(model_config)
     input_preprocessor = InputPreprocessor(model_config, tokenizer)
 
     # HF processor adds sep token

tests/tokenizers_/test_basic.py

@@ -3,38 +3,39 @@
 from typing import _get_protocol_attrs  # type: ignore
 
 import pytest
-from transformers import PreTrainedTokenizerBase
+from transformers import (
+    PreTrainedTokenizer,
+    PreTrainedTokenizerBase,
+    PreTrainedTokenizerFast,
+)
 
 from vllm.tokenizers import TokenizerLike, get_tokenizer
+from vllm.tokenizers.mistral import MistralTokenizer
 
 
 def _get_missing_attrs(obj: object, target: type):
     return [k for k in _get_protocol_attrs(target) if not hasattr(obj, k)]
 
 
+def _assert_tokenizer_like(tokenizer: object):
+    missing_attrs = _get_missing_attrs(tokenizer, TokenizerLike)
+    assert not missing_attrs, f"Missing attrs: {missing_attrs}"
+
+
 def test_tokenizer_like_protocol():
-    assert not (
+    tokenizer = get_tokenizer("gpt2", use_fast=False)
-        missing_attrs := _get_missing_attrs(
+    assert isinstance(tokenizer, PreTrainedTokenizer)
-            get_tokenizer("gpt2", use_fast=False),
+    _assert_tokenizer_like(tokenizer)
-            TokenizerLike,
-        )
-    ), f"Missing attrs: {missing_attrs}"
 
-    assert not (
+    tokenizer = get_tokenizer("gpt2", use_fast=True)
-        missing_attrs := _get_missing_attrs(
+    assert isinstance(tokenizer, PreTrainedTokenizerFast)
-            get_tokenizer("gpt2", use_fast=True),
+    _assert_tokenizer_like(tokenizer)
-            TokenizerLike,
-        )
-    ), f"Missing attrs: {missing_attrs}"
 
-    assert not (
+    tokenizer = get_tokenizer(
-        missing_attrs := _get_missing_attrs(
+        "mistralai/Mistral-7B-Instruct-v0.3", tokenizer_mode="mistral"
-            get_tokenizer(
+    )
-                "mistralai/Mistral-7B-Instruct-v0.3", tokenizer_mode="mistral"
+    assert isinstance(tokenizer, MistralTokenizer)
-            ),
+    _assert_tokenizer_like(tokenizer)
-            TokenizerLike,
-        )
-    ), f"Missing attrs: {missing_attrs}"
 
 
 @pytest.mark.parametrize("tokenizer_name", ["facebook/opt-125m", "gpt2"])

tests/tokenizers_/test_registry.py

@@ -2,7 +2,14 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from pathlib import Path
 
-from vllm.tokenizers import TokenizerLike, TokenizerRegistry, get_tokenizer
+import pytest
+
+from vllm.tokenizers import TokenizerLike
+from vllm.tokenizers.registry import (
+    TokenizerRegistry,
+    get_tokenizer,
+    resolve_tokenizer_args,
+)
 
 
 class TestTokenizer(TokenizerLike):
@@ -40,10 +47,22 @@ class TestTokenizer(TokenizerLike):
         return True
 
 
+@pytest.mark.parametrize("runner_type", ["generate", "pooling"])
+def test_resolve_tokenizer_args_idempotent(runner_type):
+    tokenizer_mode, tokenizer_name, args, kwargs = resolve_tokenizer_args(
+        "facebook/opt-125m",
+        runner_type=runner_type,
+    )
+
+    assert (tokenizer_mode, tokenizer_name, args, kwargs) == resolve_tokenizer_args(
+        tokenizer_name, *args, **kwargs
+    )
+
+
 def test_customized_tokenizer():
     TokenizerRegistry.register("test_tokenizer", __name__, TestTokenizer.__name__)
 
-    tokenizer = TokenizerRegistry.get_tokenizer("test_tokenizer", "abc")
+    tokenizer = TokenizerRegistry.load_tokenizer("test_tokenizer", "abc")
     assert isinstance(tokenizer, TestTokenizer)
     assert tokenizer.path_or_repo_id == "abc"
     assert tokenizer.bos_token_id == 0

vllm/entrypoints/chat_utils.py

@@ -50,7 +50,6 @@ from vllm.model_executor.models import SupportsMultiModal
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict, MultiModalUUIDDict
 from vllm.multimodal.utils import MEDIA_CONNECTOR_REGISTRY, MediaConnector
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.transformers_utils.chat_templates import get_chat_template_fallback_path
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.utils import random_uuid
@@ -60,6 +59,8 @@ from vllm.utils.import_utils import LazyLoader
 
 if TYPE_CHECKING:
     import torch
+
+    from vllm.tokenizers.mistral import MistralTokenizer
 else:
     torch = LazyLoader("torch", globals(), "torch")
 
@@ -1832,7 +1833,7 @@ def apply_hf_chat_template(
 
 
 def apply_mistral_chat_template(
-    tokenizer: MistralTokenizer,
+    tokenizer: "MistralTokenizer",
     messages: list[ChatCompletionMessageParam],
     chat_template: str | None,
     tools: list[dict[str, Any]] | None,

vllm/tokenizers/__init__.py

@@ -1,9 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from .deepseekv32 import DeepseekV32Tokenizer
-from .hf import HfTokenizer
-from .mistral import MistralTokenizer
 from .protocol import TokenizerLike
 from .registry import (
     TokenizerRegistry,
@@ -15,12 +12,9 @@ from .registry import (
 
 __all__ = [
     "TokenizerLike",
-    "HfTokenizer",
-    "MistralTokenizer",
     "TokenizerRegistry",
     "cached_get_tokenizer",
     "get_tokenizer",
     "cached_tokenizer_from_config",
     "init_tokenizer_from_config",
-    "DeepseekV32Tokenizer",
 ]

vllm/tokenizers/deepseekv32.py

@@ -2,24 +2,18 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from pathlib import Path
+from typing import Any
 
 from transformers import BatchEncoding
 
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+
 from .deepseek_v32_encoding import encode_messages
-from .hf import HfTokenizer, TokenizerLike
+from .hf import CachedHfTokenizer
-from .registry import TokenizerRegistry
+from .protocol import TokenizerLike
 
 
-@TokenizerRegistry.register("deepseek_v32")
+class DeepseekV32Tokenizer(CachedHfTokenizer):
-class DeepseekV32Tokenizer(HfTokenizer):
-    def __init__(self, tokenizer: TokenizerLike):
-        self.tokenizer = tokenizer
-        self.name_or_path = (
-            tokenizer.name_or_path if hasattr(tokenizer, "name_or_path") else ""
-        )
-        self._added_vocab = self.tokenizer.get_added_vocab()
-        self._added_vocab_size = len(self._added_vocab)
-
     @classmethod
     def from_pretrained(
         cls,
@@ -40,7 +34,21 @@ class DeepseekV32Tokenizer(HfTokenizer):
         )
         return DeepseekV32Tokenizer(tokenizer)
 
-    def apply_chat_template(self, messages, tools=None, **kwargs):
+    def __init__(self, tokenizer: TokenizerLike) -> None:
+        super().__init__()
+
+        self.tokenizer = tokenizer
+        self.name_or_path = getattr(tokenizer, "name_or_path", "")
+
+        self._added_vocab = self.tokenizer.get_added_vocab()
+        self._added_vocab_size = len(self._added_vocab)
+
+    def apply_chat_template(
+        self,
+        messages: list["ChatCompletionMessageParam"],
+        tools: list[dict[str, Any]] | None = None,
+        **kwargs,
+    ) -> str | list[int]:
         thinking = kwargs.get("thinking", False)
         thinking_mode = "thinking"
         if not thinking:
@@ -49,13 +57,24 @@ class DeepseekV32Tokenizer(HfTokenizer):
         messages = conversation.copy()
         if tools is not None and len(tools) > 0:
             messages.insert(0, {"role": "system"})
-            messages[0]["tools"] = tools
+            messages[0]["tools"] = tools  # type: ignore[typeddict-unknown-key]
 
         # Historical reasoning content is dropped when a new user message is introduced
         drop_thinking = messages[-1]["role"] == "user"
 
         encode_config = dict(thinking_mode=thinking_mode, drop_thinking=drop_thinking)
         prompt_str = encode_messages(messages, **encode_config)  # type: ignore
+
+        if kwargs.get("tokenize", True):
+            tokenizer_kwargs = {
+                k: kwargs[k] for k in ("truncation", "max_length") if k in kwargs
+            }
+            return self.encode(
+                prompt_str,
+                add_special_tokens=False,
+                **tokenizer_kwargs,
+            )
+
         return prompt_str
 
     def num_special_tokens_to_add(self) -> int:

vllm/tokenizers/hf.py

@@ -3,22 +3,18 @@
 import contextlib
 import copy
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import TypeAlias
 
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from vllm.transformers_utils.config import get_sentence_transformer_tokenizer_config
 
 from .protocol import TokenizerLike
-from .registry import TokenizerRegistry
 
-if TYPE_CHECKING:
+HfTokenizer: TypeAlias = PreTrainedTokenizer | PreTrainedTokenizerFast
-    from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 
 
-def get_cached_tokenizer(
+def get_cached_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
-    tokenizer: "PreTrainedTokenizer | PreTrainedTokenizerFast",
-) -> TokenizerLike:
     """
     By default, transformers will recompute multiple tokenizer properties
     each time they are called, leading to a significant slowdown.
@@ -65,11 +61,10 @@ def get_cached_tokenizer(
     CachedTokenizer.__name__ = f"Cached{tokenizer.__class__.__name__}"
 
     cached_tokenizer.__class__ = CachedTokenizer
-    return cached_tokenizer  # type: ignore
+    return cached_tokenizer
 
 
-@TokenizerRegistry.register("hf")
+class CachedHfTokenizer(TokenizerLike):
-class HfTokenizer(TokenizerLike):
     @classmethod
     def from_pretrained(
         cls,
@@ -79,7 +74,7 @@ class HfTokenizer(TokenizerLike):
         revision: str | None = None,
         download_dir: str | None = None,
         **kwargs,
-    ) -> "TokenizerLike":
+    ) -> HfTokenizer:
         try:
             tokenizer = AutoTokenizer.from_pretrained(
                 path_or_repo_id,

vllm/tokenizers/mistral.py

@@ -3,10 +3,11 @@
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, cast
 
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 from vllm.logger import init_logger
 
 from .protocol import TokenizerLike
-from .registry import TokenizerRegistry
 
 if TYPE_CHECKING:
     from mistral_common.protocol.instruct.request import (
@@ -15,9 +16,6 @@ if TYPE_CHECKING:
     from mistral_common.tokens.tokenizers.tekken import Tekkenizer
     from transformers import BatchEncoding
 
-    from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
-    from vllm.entrypoints.openai.protocol import ChatCompletionRequest
-
     try:
         # Transformers v5
         from transformers.tokenization_mistral_common import MistralCommonBackend
@@ -201,7 +199,6 @@ def _tekken_token_to_id(tokenizer: "Tekkenizer", t: str | bytes) -> int:
         return tokenizer.unk_id
 
 
-@TokenizerRegistry.register("mistral")
 class MistralTokenizer(TokenizerLike):
     @classmethod
     def from_pretrained(

vllm/tokenizers/protocol.py

@@ -97,7 +97,7 @@ class TokenizerLike(Protocol):
         messages: list["ChatCompletionMessageParam"],
         tools: list[dict[str, Any]] | None = None,
         **kwargs,
-    ) -> list[int]:
+    ) -> str | list[int]:
         raise NotImplementedError
 
     def convert_tokens_to_string(self, tokens: list[str]) -> str:

vllm/tokenizers/registry.py

@@ -1,13 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import importlib.util
-from collections.abc import Callable
+from dataclasses import dataclass, field
 from functools import lru_cache
 from pathlib import Path
-from typing import TYPE_CHECKING, TypeVar, overload
+from typing import TYPE_CHECKING
 
 import huggingface_hub
-from typing_extensions import assert_never
+from typing_extensions import TypeVar, assert_never, deprecated
 
 import vllm.envs as envs
 from vllm.logger import init_logger
@@ -24,46 +24,25 @@ from vllm.utils.import_utils import resolve_obj_by_qualname
 from .protocol import TokenizerLike
 
 if TYPE_CHECKING:
-    from vllm.config import ModelConfig
+    from vllm.config.model import ModelConfig, RunnerType
 
 logger = init_logger(__name__)
 
-_T = TypeVar("_T", bound=type[TokenizerLike])
+
+_VLLM_TOKENIZERS = {
+    "deepseekv32": ("deepseekv32", "DeepseekV32Tokenizer"),
+    "hf": ("hf", "CachedHfTokenizer"),
+    "mistral": ("mistral", "MistralTokenizer"),
+}
 
 
-class TokenizerRegistry:
+@dataclass
-    # Tokenizer name -> tokenizer_cls or (tokenizer module, tokenizer class)
+class _TokenizerRegistry:
-    REGISTRY: dict[str, type[TokenizerLike] | tuple[str, str]] = {}
+    # Tokenizer mode ->  (tokenizer module, tokenizer class)
+    tokenizers: dict[str, tuple[str, str]] = field(default_factory=dict)
 
-    # In-tree tokenizers
+    def register(self, tokenizer_mode: str, module: str, class_name: str) -> None:
-    @staticmethod
+        if tokenizer_mode in self.tokenizers:
-    @overload
-    def register(tokenizer_mode: str) -> Callable[[_T], _T]: ...
-
-    # OOT tokenizers
-    @staticmethod
-    @overload
-    def register(tokenizer_mode: str, module: str, class_name: str) -> None: ...
-
-    @staticmethod
-    def register(
-        tokenizer_mode: str,
-        module: str | None = None,
-        class_name: str | None = None,
-    ) -> Callable[[_T], _T] | None:
-        # In-tree tokenizers
-        if module is None or class_name is None:
-
-            def wrapper(tokenizer_cls: _T) -> _T:
-                assert tokenizer_mode not in TokenizerRegistry.REGISTRY
-                TokenizerRegistry.REGISTRY[tokenizer_mode] = tokenizer_cls
-
-                return tokenizer_cls
-
-            return wrapper
-
-        # OOT tokenizers
-        if tokenizer_mode in TokenizerRegistry.REGISTRY:
             logger.warning(
                 "%s.%s is already registered for tokenizer_mode=%r. "
                 "It is overwritten by the new one.",
@@ -72,36 +51,42 @@ class TokenizerRegistry:
                 tokenizer_mode,
             )
 
-        TokenizerRegistry.REGISTRY[tokenizer_mode] = (module, class_name)
+        self.tokenizers[tokenizer_mode] = (module, class_name)
 
         return None
 
-    @staticmethod
+    def load_tokenizer_cls(self, tokenizer_mode: str) -> type[TokenizerLike]:
-    def get_tokenizer(tokenizer_mode: str, *args, **kwargs) -> "TokenizerLike":
+        if tokenizer_mode not in self.tokenizers:
-        if tokenizer_mode not in TokenizerRegistry.REGISTRY:
             raise ValueError(f"No tokenizer registered for {tokenizer_mode=!r}.")
 
-        item = TokenizerRegistry.REGISTRY[tokenizer_mode]
+        module, class_name = self.tokenizers[tokenizer_mode]
-        if isinstance(item, type):
-            return item.from_pretrained(*args, **kwargs)
-
-        module, class_name = item
         logger.debug_once(f"Loading {class_name} for {tokenizer_mode=!r}")
 
-        class_ = resolve_obj_by_qualname(f"{module}.{class_name}")
+        return resolve_obj_by_qualname(f"{module}.{class_name}")
-        return class_.from_pretrained(*args, **kwargs)
+
+    def load_tokenizer(self, tokenizer_mode: str, *args, **kwargs) -> TokenizerLike:
+        tokenizer_cls = self.load_tokenizer_cls(tokenizer_mode)
+        return tokenizer_cls.from_pretrained(*args, **kwargs)
 
 
-def get_tokenizer(
+TokenizerRegistry = _TokenizerRegistry(
+    {
+        mode: (f"vllm.tokenizers.{mod_relname}", cls_name)
+        for mode, (mod_relname, cls_name) in _VLLM_TOKENIZERS.items()
+    }
+)
+
+
+def resolve_tokenizer_args(
     tokenizer_name: str | Path,
     *args,
+    runner_type: "RunnerType" = "generate",
     tokenizer_mode: str = "auto",
-    trust_remote_code: bool = False,
-    revision: str | None = None,
-    download_dir: str | None = None,
     **kwargs,
-) -> TokenizerLike:
+):
-    """Gets a tokenizer for the given model name via HuggingFace or ModelScope."""
+    revision: str | None = kwargs.get("revision")
+    download_dir: str | None = kwargs.get("download_dir")
+
     if envs.VLLM_USE_MODELSCOPE:
         # download model from ModelScope hub,
         # lazy import so that modelscope is not required for normal use.
@@ -125,16 +110,6 @@ def get_tokenizer(
                 )
                 tokenizer_name = tokenizer_path
 
-    if tokenizer_mode == "slow":
-        if kwargs.get("use_fast", False):
-            raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
-
-        tokenizer_mode = "hf"
-        kwargs["use_fast"] = False
-
-    if "truncation_side" not in kwargs:
-        kwargs["truncation_side"] = "left"
-
     # Separate model folder from file path for GGUF models
     if is_gguf(tokenizer_name):
         if check_gguf_file(tokenizer_name):
@@ -150,6 +125,21 @@ def get_tokenizer(
             )
             kwargs["gguf_file"] = gguf_file
 
+    if "truncation_side" not in kwargs:
+        if runner_type == "generate" or runner_type == "draft":
+            kwargs["truncation_side"] = "left"
+        elif runner_type == "pooling":
+            kwargs["truncation_side"] = "right"
+        else:
+            assert_never(runner_type)
+
+    if tokenizer_mode == "slow":
+        if kwargs.get("use_fast", False):
+            raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
+
+        tokenizer_mode = "hf"
+        kwargs["use_fast"] = False
+
     # Try to use official Mistral tokenizer if possible
     if tokenizer_mode == "auto" and importlib.util.find_spec("mistral_common"):
         allow_patterns = ["tekken.json", "tokenizer.model.v*"]
@@ -165,49 +155,70 @@ def get_tokenizer(
     if tokenizer_mode == "auto":
         tokenizer_mode = "hf"
 
-    tokenizer_args = (tokenizer_name, *args)
+    return tokenizer_mode, tokenizer_name, args, kwargs
-    tokenizer_kwargs = dict(
+
+
+cached_resolve_tokenizer_args = lru_cache(resolve_tokenizer_args)
+
+
+def tokenizer_args_from_config(config: "ModelConfig", **kwargs):
+    return cached_resolve_tokenizer_args(
+        config.tokenizer,
+        runner_type=config.runner_type,
+        tokenizer_mode=config.tokenizer_mode,
+        revision=config.tokenizer_revision,
+        trust_remote_code=config.trust_remote_code,
+        **kwargs,
+    )
+
+
+_T = TypeVar("_T", bound=TokenizerLike, default=TokenizerLike)
+
+
+def get_tokenizer(
+    tokenizer_name: str | Path,
+    *args,
+    tokenizer_cls: type[_T] = TokenizerLike,  # type: ignore[assignment]
+    trust_remote_code: bool = False,
+    revision: str | None = None,
+    download_dir: str | None = None,
+    **kwargs,
+) -> _T:
+    """Gets a tokenizer for the given model name via HuggingFace or ModelScope."""
+    tokenizer_mode, tokenizer_name, args, kwargs = cached_resolve_tokenizer_args(
+        tokenizer_name,
+        *args,
         trust_remote_code=trust_remote_code,
         revision=revision,
         download_dir=download_dir,
         **kwargs,
     )
 
-    if tokenizer_mode == "custom":
+    if tokenizer_cls == TokenizerLike:
-        logger.warning_once(
+        tokenizer_cls_ = TokenizerRegistry.load_tokenizer_cls(tokenizer_mode)
-            "TokenizerRegistry now uses `tokenizer_mode` as the registry key "
+    else:
-            "instead of `tokenizer_name`. "
+        tokenizer_cls_ = tokenizer_cls
-            "Please update the definition of `.from_pretrained` in "
-            "your custom tokenizer to accept `args=%s`, `kwargs=%s`. "
-            "Then, you can pass `tokenizer_mode=%r` instead of "
-            "`tokenizer_mode='custom'` when initializing vLLM.",
-            tokenizer_args,
-            str(tokenizer_kwargs),
-            tokenizer_name,
-        )
 
-        tokenizer_mode = str(tokenizer_name)
+    tokenizer = tokenizer_cls_.from_pretrained(tokenizer_name, *args, **kwargs)
-
-    tokenizer = TokenizerRegistry.get_tokenizer(
-        tokenizer_mode,
-        *tokenizer_args,
-        **tokenizer_kwargs,
-    )
     if not tokenizer.is_fast:
         logger.warning(
             "Using a slow tokenizer. This might cause a significant "
             "slowdown. Consider using a fast tokenizer instead."
         )
 
-    return tokenizer
+    return tokenizer  # type: ignore
 
 
 cached_get_tokenizer = lru_cache(get_tokenizer)
 
 
 def cached_tokenizer_from_config(model_config: "ModelConfig", **kwargs):
+    if model_config.skip_tokenizer_init:
+        return None
+
     return cached_get_tokenizer(
         model_config.tokenizer,
+        runner_type=model_config.runner_type,
         tokenizer_mode=model_config.tokenizer_mode,
         revision=model_config.tokenizer_revision,
         trust_remote_code=model_config.trust_remote_code,
@@ -215,19 +226,8 @@ def cached_tokenizer_from_config(model_config: "ModelConfig", **kwargs):
     )
 
 
+@deprecated(
+    "Renamed to `cached_tokenizer_from_config`. The old name will be removed in v0.14."
+)
 def init_tokenizer_from_config(model_config: "ModelConfig"):
-    runner_type = model_config.runner_type
+    return cached_tokenizer_from_config(model_config)
-    if runner_type == "generate" or runner_type == "draft":
-        truncation_side = "left"
-    elif runner_type == "pooling":
-        truncation_side = "right"
-    else:
-        assert_never(runner_type)
-
-    return get_tokenizer(
-        model_config.tokenizer,
-        tokenizer_mode=model_config.tokenizer_mode,
-        trust_remote_code=model_config.trust_remote_code,
-        revision=model_config.tokenizer_revision,
-        truncation_side=truncation_side,
-    )

vllm/transformers_utils/tokenizer.py

@@ -60,17 +60,17 @@ def __getattr__(name: str):
 
         return cached_tokenizer_from_config
     if name == "init_tokenizer_from_configs":
-        from vllm.tokenizers import init_tokenizer_from_config
+        from vllm.tokenizers import cached_tokenizer_from_config
 
         warnings.warn(
             "`vllm.transformers_utils.tokenizer.init_tokenizer_from_configs` "
-            "has been moved to `vllm.tokenizers.init_tokenizer_from_config`. "
+            "has been moved to `vllm.tokenizers.cached_tokenizer_from_config`. "
             "The old name will be removed in v0.14.",
             DeprecationWarning,
             stacklevel=2,
         )
 
-        return init_tokenizer_from_config
+        return cached_tokenizer_from_config
 
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 

vllm/v1/engine/async_llm.py

@@ -26,7 +26,7 @@ from vllm.plugins.io_processors import get_io_processor
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
-from vllm.tokenizers import TokenizerLike, init_tokenizer_from_config
+from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
 from vllm.tracing import init_tracer
 from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
 from vllm.usage.usage_lib import UsageContext
@@ -111,7 +111,7 @@ class AsyncLLM(EngineClient):
         if self.model_config.skip_tokenizer_init:
             tokenizer = None
         else:
-            tokenizer = init_tokenizer_from_config(self.model_config)
+            tokenizer = cached_tokenizer_from_config(self.model_config)
 
         self.input_processor = InputProcessor(self.vllm_config, tokenizer)
         self.io_processor = get_io_processor(

vllm/v1/engine/llm_engine.py

@@ -23,7 +23,7 @@ from vllm.plugins.io_processors import get_io_processor
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
-from vllm.tokenizers import TokenizerLike, init_tokenizer_from_config
+from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
 from vllm.tracing import init_tracer
 from vllm.usage.usage_lib import UsageContext
 from vllm.v1.engine import EngineCoreRequest
@@ -86,7 +86,7 @@ class LLMEngine:
         if self.model_config.skip_tokenizer_init:
             tokenizer = None
         else:
-            tokenizer = init_tokenizer_from_config(self.model_config)
+            tokenizer = cached_tokenizer_from_config(self.model_config)
 
         self.input_processor = InputProcessor(self.vllm_config, tokenizer)
         self.io_processor = get_io_processor(

vllm/v1/structured_output/__init__.py

@@ -7,7 +7,7 @@ from typing import TYPE_CHECKING
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParserManager
-from vllm.tokenizers import init_tokenizer_from_config
+from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.utils.import_utils import LazyLoader
 from vllm.v1.structured_output.backend_guidance import GuidanceBackend
 from vllm.v1.structured_output.backend_types import (
@@ -71,7 +71,7 @@ class StructuredOutputManager:
             # of CPUs.
             max_workers = max(1, (multiprocessing.cpu_count() + 1) // 2)
             self.executor = ThreadPoolExecutor(max_workers=max_workers)
-            self.tokenizer = init_tokenizer_from_config(
+            self.tokenizer = cached_tokenizer_from_config(
                 model_config=self.vllm_config.model_config
             )
             reasoning_parser = (