[Frontend] Consolidate tokenizer init code (#26276)

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

tests/test_inputs.py

@@ -7,7 +7,6 @@ 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.transformers_utils.tokenizer import init_tokenizer_from_configs
 
 pytestmark = pytest.mark.cpu_test
 
@@ -107,8 +106,7 @@ def test_zip_enc_dec_prompts(mm_processor_kwargs, expected_mm_kwargs):
 )
 def test_preprocessor_text_no_mm_inputs(model_id, prompt):
     model_config = ModelConfig(model=model_id)
-    tokenizer = init_tokenizer_from_configs(model_config)
-    input_preprocessor = InputPreprocessor(model_config, tokenizer)
+    input_preprocessor = InputPreprocessor(model_config)
 
     with pytest.raises(ValueError, match="does not support multimodal inputs"):
         input_preprocessor.preprocess(prompt)
@@ -129,8 +127,8 @@ def test_preprocessor_text_no_mm_inputs(model_id, prompt):
 )
 def test_preprocessor_always_mm_code_path(model_id, prompt):
     model_config = ModelConfig(model=model_id)
-    tokenizer = init_tokenizer_from_configs(model_config)
-    input_preprocessor = InputPreprocessor(model_config, tokenizer)
+    input_preprocessor = InputPreprocessor(model_config)
+    tokenizer = input_preprocessor.tokenizer
 
     # HF processor adds sep token
     sep_token_id = tokenizer.vocab[tokenizer.sep_token]

tests/v1/engine/test_processor_multi_modal_uuids.py

@@ -65,9 +65,7 @@ def _mk_processor(
         device_config=DeviceConfig(device="cpu"),
     )
 
-    # Pass tokenizer=None; InputPreprocessor handles None when
-    # skip_tokenizer_init is True.
-    return Processor(vllm_config, tokenizer=None)  # type: ignore[arg-type]
+    return Processor(vllm_config)
 
 
 def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):

vllm/entrypoints/llm.py

@@ -74,7 +74,6 @@ from vllm.transformers_utils.tokenizer import (
     AnyTokenizer,
     MistralTokenizer,
     get_cached_tokenizer,
-    init_tokenizer_from_configs,
 )
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import Counter, Device, as_iter, is_list_of
@@ -367,11 +366,8 @@ class LLM:
     def _get_processor(self) -> Processor:
         if not hasattr(self, "_processor"):
             vllm_config = self.llm_engine.vllm_config
-            if self.model_config.skip_tokenizer_init:
-                tokenizer = None
-            else:
-                tokenizer = init_tokenizer_from_configs(self.model_config)
-            self._processor = Processor(vllm_config, tokenizer)
+            self._processor = Processor(vllm_config)
+
         return self._processor
 
     def get_default_sampling_params(self) -> SamplingParams:

vllm/entrypoints/openai/serving_engine.py

@@ -16,7 +16,6 @@ from starlette.datastructures import Headers
 from typing_extensions import TypeIs
 
 from vllm.entrypoints.utils import _validate_truncation_size
-from vllm.transformers_utils.tokenizer import init_tokenizer_from_configs
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.processor import Processor
 
@@ -272,11 +271,8 @@ class OpenAIServing:
     async def _get_processor(self) -> Processor:
         if not hasattr(self, "_processor"):
             vllm_config = await self.engine_client.get_vllm_config()
-            if self.model_config.skip_tokenizer_init:
-                tokenizer = None
-            else:
-                tokenizer = init_tokenizer_from_configs(self.model_config)
-            self._processor = Processor(vllm_config, tokenizer)
+            self._processor = Processor(vllm_config)
+
         return self._processor
 
     def _get_renderer(self, tokenizer: Optional[AnyTokenizer]) -> BaseRenderer:

vllm/inputs/preprocess.py

@@ -17,7 +17,8 @@ from vllm.multimodal.inputs import (
     MultiModalUUIDDict,
 )
 from vllm.multimodal.processing import BaseMultiModalProcessor
-from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_configs
+from vllm.utils.jsontree import json_iter_leaves
 
 from .data import (
     DecoderOnlyInputs,
@@ -44,17 +45,20 @@ class InputPreprocessor:
     def __init__(
         self,
         model_config: ModelConfig,
-        tokenizer: Optional[AnyTokenizer],
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
         mm_processor_cache: Optional[BaseMultiModalProcessorCache] = None,
     ) -> None:
         super().__init__()
 
         self.model_config = model_config
-        self.tokenizer = tokenizer
         self.mm_registry = mm_registry
         self.mm_processor_cache = mm_processor_cache
 
+        if model_config.skip_tokenizer_init:
+            self.tokenizer = None
+        else:
+            self.tokenizer = init_tokenizer_from_configs(model_config)
+
     def get_tokenizer(self) -> AnyTokenizer:
         if self.tokenizer is None:
             raise ValueError(
@@ -273,7 +277,10 @@ class InputPreprocessor:
         mm_hashes = mm_input["mm_hashes"]
 
         # Validate that all mm items have a string as their hash
-        if not contains_only_strings(mm_hashes):
+        contains_only_strings = all(
+            isinstance(leaf, str) for leaf in json_iter_leaves(mm_hashes)
+        )
+        if not contains_only_strings:
             raise ValueError(
                 f"mm_hashes must contain only strings, got: {mm_hashes}. "
                 "This is likely due to an incorrect custom implementation of "
@@ -693,15 +700,3 @@ class InputPreprocessor:
     def clear_cache(self) -> None:
         if self.mm_processor_cache is not None:
             self.mm_processor_cache.clear_cache()
-
-
-# Helper function to validate that a nested dictionary contains
-# only strings or list of strings as the leaf values.
-def contains_only_strings(obj: object):
-    if isinstance(obj, str):
-        return True
-    if isinstance(obj, list):
-        return all(isinstance(x, str) for x in obj)
-    if isinstance(obj, dict):
-        return all(contains_only_strings(v) for v in obj.values())
-    return False

vllm/v1/engine/async_llm.py

@@ -28,7 +28,7 @@ from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.tracing import init_tracer
 from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
-from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_configs
+from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import Device, as_list, cancel_task_threadsafe, cdiv, deprecate_kwargs
 from vllm.v1.engine import EngineCoreRequest
@@ -104,20 +104,8 @@ class AsyncLLM(EngineClient):
                 "logger list; enabling logging without default stat loggers"
             )
 
-        if self.model_config.skip_tokenizer_init:
-            self.tokenizer = None
-        else:
-            # Tokenizer (+ ensure liveness if running in another process).
-            self.tokenizer = init_tokenizer_from_configs(
-                model_config=vllm_config.model_config
-            )
-
         # Processor (converts Inputs --> EngineCoreRequests).
-        self.processor = Processor(
-            vllm_config=vllm_config,
-            tokenizer=self.tokenizer,
-            mm_registry=mm_registry,
-        )
+        self.processor = Processor(vllm_config, mm_registry=mm_registry)
 
         # OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
         self.output_processor = OutputProcessor(
@@ -257,6 +245,10 @@ class AsyncLLM(EngineClient):
 
         cancel_task_threadsafe(getattr(self, "output_handler", None))
 
+    @property
+    def tokenizer(self) -> Optional[AnyTokenizer]:
+        return self.processor.tokenizer
+
     async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
         return await self.engine_core.get_supported_tasks_async()
 

vllm/v1/engine/llm_engine.py

@@ -23,7 +23,7 @@ from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.tracing import init_tracer
-from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_configs
+from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import Device
 from vllm.v1.engine import EngineCoreRequest
@@ -95,18 +95,8 @@ class LLMEngine:
             self.dp_group = None
         self.should_execute_dummy_batch = False
 
-        if self.model_config.skip_tokenizer_init:
-            self.tokenizer = None
-        else:
-            # Tokenizer (+ ensure liveness if running in another process).
-            self.tokenizer = init_tokenizer_from_configs(
-                model_config=vllm_config.model_config
-            )
-
         # Processor (convert Inputs --> EngineCoreRequests)
-        self.processor = Processor(
-            vllm_config=vllm_config, tokenizer=self.tokenizer, mm_registry=mm_registry
-        )
+        self.processor = Processor(vllm_config, mm_registry=mm_registry)
 
         # OutputProcessor (convert EngineCoreOutputs --> RequestOutput).
         self.output_processor = OutputProcessor(
@@ -214,6 +204,14 @@ class LLMEngine:
     def validate_outputs(cls, outputs, output_type):
         return outputs
 
+    @property
+    def tokenizer(self) -> Optional[AnyTokenizer]:
+        return self.processor.tokenizer
+
+    @tokenizer.setter
+    def tokenizer(self, tokenizer: Optional[AnyTokenizer]) -> None:
+        self.processor.tokenizer = tokenizer
+
     def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
         return self.engine_core.get_supported_tasks()
 

vllm/v1/engine/processor.py

@@ -37,15 +37,13 @@ class Processor:
     def __init__(
         self,
         vllm_config: VllmConfig,
-        tokenizer: AnyTokenizer,
         mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
-    ):
+    ) -> None:
         self.vllm_config = vllm_config
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.lora_config = vllm_config.lora_config
         self.structured_outputs_config = vllm_config.structured_outputs_config
-        self.tokenizer = tokenizer
 
         self.generation_config_fields = self.model_config.try_get_generation_config()
 
@@ -54,11 +52,18 @@ class Processor:
 
         self.input_preprocessor = InputPreprocessor(
             self.model_config,
-            self.tokenizer,
             mm_registry,
             mm_processor_cache=self.mm_processor_cache,
         )
 
+    @property
+    def tokenizer(self) -> Optional[AnyTokenizer]:
+        return self.input_preprocessor.tokenizer
+
+    @tokenizer.setter
+    def tokenizer(self, tokenizer: Optional[AnyTokenizer]) -> None:
+        self.input_preprocessor.tokenizer = tokenizer
+
     def _validate_logprobs(
         self,
         params: SamplingParams,
@@ -511,10 +516,8 @@ class Processor:
             else:
                 raise ValueError(f"The {prompt_type} prompt cannot be empty")
 
-        if self.model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            tokenizer = self.tokenizer
+        tokenizer = self.tokenizer
+        if tokenizer is not None:
             max_input_id = max(prompt_ids or [], default=0)
 
             # NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while