[Chore] Use tokenizer.encode and tokenizer.decode directly (#29851)

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

tests/models/multimodal/processing/test_common.py

@@ -22,8 +22,11 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict
 from vllm.multimodal.cache import MultiModalProcessorOnlyCache
 from vllm.multimodal.inputs import MultiModalInputs
 from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
-from vllm.tokenizers import MistralTokenizer, cached_tokenizer_from_config
-from vllm.transformers_utils.tokenizer import encode_tokens
+from vllm.tokenizers import (
+    MistralTokenizer,
+    TokenizerLike,
+    cached_tokenizer_from_config,
+)
 
 from ....multimodal.utils import random_audio, random_image, random_video
 from ...registry import (
@@ -151,7 +154,7 @@ def get_text_token_prompts(
     mm_data: MultiModalDataDict,
 ):
     dummy_inputs = processor.dummy_inputs
-    tokenizer = processor.info.get_tokenizer()
+    tokenizer: TokenizerLike = processor.info.get_tokenizer()
     model_config = processor.info.ctx.model_config
 
     model_type = model_config.hf_config.model_type
@@ -188,10 +191,9 @@ def get_text_token_prompts(
         assert isinstance(inputs.prompt, str)
 
         text_prompt = inputs.prompt
-        token_prompt = encode_tokens(
-            tokenizer,
+        token_prompt = tokenizer.encode(
             text_prompt,
-            add_special_tokens=_ADD_SPECIAL_TOKENS_OVERRIDES.get(model_type),
+            add_special_tokens=_ADD_SPECIAL_TOKENS_OVERRIDES.get(model_type, True),
         )
 
     return text_prompt, token_prompt

tests/models/multimodal/processing/test_llama4.py

@@ -5,7 +5,6 @@
 import pytest
 
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.transformers_utils.tokenizer import encode_tokens
 
 from ....conftest import ImageTestAssets
 from ...utils import build_model_context
@@ -48,7 +47,7 @@ def test_processor_override(
         ]
     }
     if tokenized_prompt:
-        prompt = encode_tokens(tokenizer, prompt)
+        prompt = tokenizer.encode(prompt)
 
     processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
     mm_data = processed_inputs["mm_kwargs"].get_data()

vllm/entrypoints/openai/speech_to_text.py

@@ -37,7 +37,7 @@ from vllm.inputs.data import PromptType
 from vllm.logger import init_logger
 from vllm.model_executor.models import SupportsTranscription
 from vllm.outputs import RequestOutput
-from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.tokenizers import get_tokenizer
 from vllm.utils.import_utils import PlaceholderModule
 
 try:

vllm/entrypoints/renderer.py

@@ -33,7 +33,7 @@ class RenderConfig:
     `0` yields an empty list (and skips embeds).
     `-1` maps to `model_config.max_model_len`."""
 
-    add_special_tokens: bool | None = True
+    add_special_tokens: bool = True
     """Whether to add model-specific special tokens during tokenization."""
 
     cache_salt: str | None = None
@@ -315,7 +315,7 @@ class CompletionRenderer(BaseRenderer):
         text: str,
         max_length: int | None,
         truncate_prompt_tokens: int | None,
-        add_special_tokens: bool | None,
+        add_special_tokens: bool,
         cache_salt: str | None,
     ) -> EngineTokensPrompt:
         """Tokenize text input asynchronously."""

vllm/entrypoints/score_utils.py

@@ -19,7 +19,7 @@ from vllm.inputs import TokensPrompt
 from vllm.model_executor.models.interfaces import supports_score_template
 from vllm.multimodal.inputs import MultiModalDataDict
 from vllm.outputs import PoolingRequestOutput
-from vllm.transformers_utils.tokenizer import TokenizerLike
+from vllm.tokenizers import TokenizerLike
 
 ScoreContentPartParam: TypeAlias = (
     ChatCompletionContentPartImageParam | ChatCompletionContentPartImageEmbedsParam

vllm/model_executor/models/nano_nemotron_vl.py

@@ -75,7 +75,6 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
 from vllm.transformers_utils.configs.radio import RadioConfig
-from vllm.transformers_utils.tokenizer import encode_tokens
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .utils import _merge_multimodal_embeddings
@@ -454,14 +453,12 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
 
         # Pre-tokenize special tokens for video processing
         # to avoid repeated tokenization
-        self._img_start_token_ids = encode_tokens(
-            tokenizer, IMG_START, add_special_tokens=False
+        self._img_start_token_ids = tokenizer.encode(
+            IMG_START, add_special_tokens=False
         )
-        self._img_end_token_ids = encode_tokens(
-            tokenizer, IMG_END, add_special_tokens=False
-        )
-        self._img_context_token_ids = encode_tokens(
-            tokenizer, IMG_CONTEXT, add_special_tokens=False
+        self._img_end_token_ids = tokenizer.encode(IMG_END, add_special_tokens=False)
+        self._img_context_token_ids = tokenizer.encode(
+            IMG_CONTEXT, add_special_tokens=False
         )
 
     @property
@@ -1179,14 +1176,12 @@ class NemotronH_Nano_VL_V2(
         # Pre-tokenize special tokens for video processing
         # to avoid repeated tokenization
         tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
-        self._img_start_token_ids = encode_tokens(
-            tokenizer, IMG_START, add_special_tokens=False
+        self._img_start_token_ids = tokenizer.encode(
+            IMG_START, add_special_tokens=False
         )
-        self._img_end_token_ids = encode_tokens(
-            tokenizer, IMG_END, add_special_tokens=False
-        )
-        self._img_context_token_ids = encode_tokens(
-            tokenizer, IMG_CONTEXT, add_special_tokens=False
+        self._img_end_token_ids = tokenizer.encode(IMG_END, add_special_tokens=False)
+        self._img_context_token_ids = tokenizer.encode(
+            IMG_CONTEXT, add_special_tokens=False
         )
 
     def pixel_shuffle(self, x, scale_factor=0.5):

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -88,7 +88,6 @@ from vllm.multimodal.processing import (
 )
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
-from vllm.transformers_utils.tokenizer import encode_tokens
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import (
@@ -591,7 +590,7 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
                     tokenization_kwargs=tokenization_kwargs,
                 )
             tokenizer = self.info.get_tokenizer()
-            prompt_ids = encode_tokens(tokenizer, prompt)
+            prompt_ids = tokenizer.encode(prompt)
         else:
             prompt_ids = self._apply_hf_processor_tokens_only(prompt)
 

vllm/multimodal/processing.py

@@ -25,7 +25,6 @@ from typing_extensions import TypeVar, assert_never
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
 from vllm.transformers_utils.processor import cached_processor_from_config
-from vllm.transformers_utils.tokenizer import decode_tokens, encode_tokens
 from vllm.utils.collection_utils import flatten_2d_lists, full_groupby
 from vllm.utils.func_utils import get_allowed_kwarg_only_overrides
 from vllm.utils.jsontree import JSONTree, json_map_leaves
@@ -80,9 +79,9 @@ def _cached_encode(
     tokenizer: TokenizerLike,
     text: str,
     *,
-    add_special_tokens: bool | None = None,
+    add_special_tokens: bool = True,
 ) -> list[int]:
-    return encode_tokens(tokenizer, text, add_special_tokens=add_special_tokens)
+    return tokenizer.encode(text, add_special_tokens=add_special_tokens)
 
 
 @lru_cache(maxsize=2048)
@@ -90,11 +89,9 @@ def _cached_decode(
     tokenizer: TokenizerLike,
     token_ids: tuple[int, ...],
     *,
-    skip_special_tokens: bool | None = None,
+    skip_special_tokens: bool = False,
 ) -> str:
-    return decode_tokens(
-        tokenizer, list(token_ids), skip_special_tokens=skip_special_tokens
-    )
+    return tokenizer.decode(list(token_ids), skip_special_tokens=skip_special_tokens)
 
 
 def _seq2text(
@@ -110,7 +107,7 @@ def _seq2text(
         raise ValueError("You cannot decode tokens when `skip_tokenizer_init=True`")
 
     if not use_cache:
-        return decode_tokens(tokenizer, seq)
+        return tokenizer.decode(seq)
 
     return _cached_decode(tokenizer, tuple(seq))
 
@@ -126,7 +123,7 @@ def _seq2tokens(
             raise ValueError("You cannot encode text when `skip_tokenizer_init=True`")
 
         if not use_cache:
-            return encode_tokens(tokenizer, seq, add_special_tokens=False)
+            return tokenizer.encode(seq, add_special_tokens=False)
 
         return _cached_encode(tokenizer, seq, add_special_tokens=False)
 
@@ -2198,8 +2195,8 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
         tokenizer = self.info.get_tokenizer()
         decoder_prompt_raw = self.create_decoder_prompt(prompt, mm_data)
         if isinstance(decoder_prompt_raw, str):
-            decoder_prompt_ids = encode_tokens(
-                tokenizer, decoder_prompt_raw, add_special_tokens=False
+            decoder_prompt_ids = tokenizer.encode(
+                decoder_prompt_raw, add_special_tokens=False
             )
         else:
             decoder_prompt_ids = decoder_prompt_raw

vllm/transformers_utils/tokenizer.py

@@ -4,6 +4,8 @@
 import warnings
 from typing import Any
 
+from typing_extensions import deprecated
+
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
 
@@ -73,6 +75,7 @@ def __getattr__(name: str):
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
+@deprecated("Will be removed in v0.13. Please use `tokenizer.decode()` instead.")
 def decode_tokens(
     tokenizer: TokenizerLike,
     token_ids: list[int],
@@ -94,6 +97,7 @@ def decode_tokens(
     return tokenizer.decode(token_ids, **kw_args)
 
 
+@deprecated("Will be removed in v0.13. Please use `tokenizer.encode()` instead.")
 def encode_tokens(
     tokenizer: TokenizerLike,
     text: str,