[Renderer] Consolidate factory methods (#38218)

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

tests/entrypoints/openai/chat_completion/test_chat_error.py

@@ -16,7 +16,7 @@ from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.renderers.hf import HfRenderer
-from vllm.tokenizers.registry import tokenizer_args_from_config
+from vllm.tokenizers.registry import cached_tokenizer_from_config
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"
@@ -72,11 +72,9 @@ class MockVllmConfig:
 
 
 def _build_renderer(model_config: MockModelConfig):
-    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
-
-    return HfRenderer.from_config(
+    return HfRenderer(
         MockVllmConfig(model_config, parallel_config=MockParallelConfig()),
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+        cached_tokenizer_from_config(model_config),
     )
 
 

tests/entrypoints/openai/chat_completion/test_serving_chat.py

@@ -41,7 +41,7 @@ from vllm.renderers.hf import HfRenderer
 from vllm.renderers.mistral import MistralRenderer
 from vllm.tokenizers import get_tokenizer
 from vllm.tokenizers.mistral import MistralTokenizer
-from vllm.tokenizers.registry import tokenizer_args_from_config
+from vllm.tokenizers.registry import cached_tokenizer_from_config
 from vllm.tool_parsers import ToolParserManager
 from vllm.v1.engine.async_llm import AsyncLLM
 
@@ -553,11 +553,9 @@ class MockVllmConfig:
 
 
 def _build_renderer(model_config: MockModelConfig):
-    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
-
-    return HfRenderer.from_config(
+    return HfRenderer(
         MockVllmConfig(model_config, parallel_config=MockParallelConfig()),
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+        cached_tokenizer_from_config(model_config),
     )
 
 

tests/entrypoints/openai/completion/test_completion_error.py

@@ -16,7 +16,7 @@ from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.renderers.hf import HfRenderer
-from vllm.tokenizers.registry import tokenizer_args_from_config
+from vllm.tokenizers.registry import cached_tokenizer_from_config
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"
@@ -93,11 +93,9 @@ def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
 
 
 def _build_renderer(model_config: MockModelConfig):
-    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
-
-    return HfRenderer.from_config(
+    return HfRenderer(
         MockVllmConfig(model_config, parallel_config=MockParallelConfig()),
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+        cached_tokenizer_from_config(model_config),
     )
 
 

tests/entrypoints/openai/completion/test_lora_resolvers.py

@@ -18,7 +18,7 @@ from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.lora.request import LoRARequest
 from vllm.lora.resolver import LoRAResolver, LoRAResolverRegistry
 from vllm.renderers.hf import HfRenderer
-from vllm.tokenizers.registry import tokenizer_args_from_config
+from vllm.tokenizers.registry import cached_tokenizer_from_config
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"
@@ -101,11 +101,9 @@ def register_mock_resolver():
 
 
 def _build_renderer(model_config: MockModelConfig):
-    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
-
-    return HfRenderer.from_config(
+    return HfRenderer(
         MockVllmConfig(model_config, parallel_config=MockParallelConfig()),
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+        cached_tokenizer_from_config(model_config),
     )
 
 

tests/renderers/test_completions.py

@@ -15,7 +15,6 @@ from vllm.inputs import SingletonPrompt
 from vllm.renderers import TokenizeParams
 from vllm.renderers.hf import HfRenderer
 from vllm.renderers.inputs.preprocess import parse_model_prompt, prompt_to_seq
-from vllm.tokenizers.registry import tokenizer_args_from_config
 
 MODEL_NAME = "openai-community/gpt2"
 
@@ -81,8 +80,6 @@ def _build_renderer(
     truncation_side: str = "left",
     max_chars_per_token: int = 1,
 ):
-    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
-
     renderer = HfRenderer(
         MockVllmConfig(model_config, parallel_config=MockParallelConfig()),
         tokenizer=(

tests/renderers/test_process_multi_modal_uuids.py

@@ -8,7 +8,7 @@ from vllm.assets.video import VideoAsset
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.multimodal.parse import parse_mm_uuids
 from vllm.renderers.hf import HfRenderer
-from vllm.tokenizers.registry import tokenizer_args_from_config
+from vllm.tokenizers.registry import cached_tokenizer_from_config
 
 cherry_pil_image = ImageAsset("cherry_blossom").pil_image
 stop_pil_image = ImageAsset("stop_sign").pil_image
@@ -29,11 +29,9 @@ def _build_renderer(
         cache_config=CacheConfig(enable_prefix_caching=enable_prefix_caching),
     )
 
-    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
-
-    return HfRenderer.from_config(
+    return HfRenderer(
         vllm_config,
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+        cached_tokenizer_from_config(model_config),
     )
 
 

vllm/config/model.py

@@ -542,7 +542,9 @@ class ModelConfig:
 
         # Set default tokenizer modes based on model architecture
         if self.tokenizer_mode == "auto":
-            if arch == "Grok1ForCausalLM":
+            if self.model_impl == "terratorch":
+                self.tokenizer_mode = "terratorch"
+            elif arch == "Grok1ForCausalLM":
                 self.tokenizer_mode = "grok2"
             elif arch == "MoonshotKimiaForCausalLM":
                 self.tokenizer_mode = "kimi_audio"

vllm/renderers/base.py

@@ -69,15 +69,6 @@ _T = TypeVar("_T", bound=TokenizerLike, default=TokenizerLike)
 
 
 class BaseRenderer(ABC, Generic[_T]):
-    @classmethod
-    @abstractmethod
-    def from_config(
-        cls,
-        config: "VllmConfig",
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "BaseRenderer":
-        raise NotImplementedError
-
     def __init__(self, config: "VllmConfig", tokenizer: _T | None) -> None:
         super().__init__()
 

vllm/renderers/deepseek_v32.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
 
-from vllm.config import VllmConfig
 from vllm.entrypoints.chat_utils import (
     ChatCompletionMessageParam,
     ConversationMessage,
@@ -10,7 +8,6 @@ from vllm.entrypoints.chat_utils import (
     parse_chat_messages_async,
 )
 from vllm.logger import init_logger
-from vllm.tokenizers import cached_get_tokenizer
 from vllm.tokenizers.deepseek_v32 import DeepseekV32Tokenizer
 
 from .base import BaseRenderer
@@ -22,23 +19,6 @@ logger = init_logger(__name__)
 
 
 class DeepseekV32Renderer(BaseRenderer[DeepseekV32Tokenizer]):
-    @classmethod
-    def from_config(  # type: ignore[override]
-        cls,
-        config: VllmConfig,
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "DeepseekV32Renderer":
-        model_config = config.model_config
-        if model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            tokenizer = cached_get_tokenizer(
-                tokenizer_cls=DeepseekV32Tokenizer,
-                **tokenizer_kwargs,
-            )
-
-        return cls(config, tokenizer)
-
     def render_messages(
         self,
         messages: list[ChatCompletionMessageParam],

vllm/renderers/grok2.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
 
-from vllm.config import VllmConfig
 from vllm.entrypoints.chat_utils import (
     ChatCompletionMessageParam,
     ConversationMessage,
@@ -10,7 +8,6 @@ from vllm.entrypoints.chat_utils import (
     parse_chat_messages_async,
 )
 from vllm.logger import init_logger
-from vllm.tokenizers import cached_get_tokenizer
 from vllm.tokenizers.grok2 import Grok2Tokenizer
 
 from .base import BaseRenderer
@@ -22,23 +19,6 @@ logger = init_logger(__name__)
 
 
 class Grok2Renderer(BaseRenderer[Grok2Tokenizer]):
-    @classmethod
-    def from_config(  # type: ignore[override]
-        cls,
-        config: VllmConfig,
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "Grok2Renderer":
-        model_config = config.model_config
-        if model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            tokenizer = cached_get_tokenizer(
-                tokenizer_cls=Grok2Tokenizer,
-                **tokenizer_kwargs,
-            )
-
-        return cls(config, tokenizer)
-
     def render_messages(
         self,
         messages: list[ChatCompletionMessageParam],

vllm/renderers/hf.py

@@ -27,8 +27,7 @@ from vllm.entrypoints.chat_utils import (
 )
 from vllm.inputs import MultiModalDataDict, MultiModalUUIDDict
 from vllm.logger import init_logger
-from vllm.tokenizers import cached_get_tokenizer
-from vllm.tokenizers.hf import CachedHfTokenizer, HfTokenizer
+from vllm.tokenizers.hf import HfTokenizer
 from vllm.transformers_utils.chat_templates import get_chat_template_fallback_path
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.utils.func_utils import supports_kw
@@ -604,26 +603,6 @@ def replace_vision_chunk_video_placeholder(
 
 
 class HfRenderer(BaseRenderer[HfTokenizer]):
-    @classmethod
-    def from_config(  # type: ignore[override]
-        cls,
-        config: VllmConfig,
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "HfRenderer":
-        model_config = config.model_config
-        if model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            tokenizer = cast(
-                HfTokenizer,
-                cached_get_tokenizer(
-                    tokenizer_cls=CachedHfTokenizer,  # type: ignore[type-abstract]
-                    **tokenizer_kwargs,
-                ),
-            )
-
-        return cls(config, tokenizer)
-
     def __init__(
         self,
         config: VllmConfig,

vllm/renderers/kimi_audio.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any, cast
-
-from vllm.config import VllmConfig
-from vllm.tokenizers.kimi_audio import KimiAudioTokenizer
-from vllm.tokenizers.registry import get_tokenizer
-
-from .hf import HfRenderer, HfTokenizer
-
-
-class KimiAudioRenderer(HfRenderer):
-    """Renderer for Kimi-Audio models.
-
-    This renderer uses HfRenderer internally with a custom TikToken tokenizer.
-    """
-
-    @classmethod
-    def from_config(  # type: ignore[override]
-        cls,
-        config: VllmConfig,
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "HfRenderer":
-        """Create an HfRenderer instance for Kimi-Audio models."""
-        model_config = config.model_config
-        if model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            # Extract tokenizer_name from kwargs (already processed by
-            # tokenizer_args_from_config for ModelScope/GGUF/etc)
-            tokenizer_name = tokenizer_kwargs.pop(
-                "tokenizer_name", model_config.tokenizer
-            )
-            # Remove tokenizer_cls from kwargs to avoid duplicate argument
-            tokenizer_kwargs = {
-                k: v for k, v in tokenizer_kwargs.items() if k != "tokenizer_cls"
-            }
-            # Use get_tokenizer directly instead of cached_get_tokenizer
-            # (KimiAudioTokenizer doesn't work with get_cached_tokenizer)
-            tokenizer = cast(
-                HfTokenizer,
-                get_tokenizer(
-                    tokenizer_name,
-                    tokenizer_cls=KimiAudioTokenizer,  # type: ignore[arg-type]
-                    **tokenizer_kwargs,
-                ),
-            )
-
-        return HfRenderer(config, tokenizer)

vllm/renderers/mistral.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any
 
 from vllm.config import VllmConfig
 from vllm.entrypoints.chat_utils import (
@@ -11,7 +10,6 @@ from vllm.entrypoints.chat_utils import (
     parse_chat_messages_async,
 )
 from vllm.logger import init_logger
-from vllm.tokenizers import cached_get_tokenizer
 from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.utils.async_utils import make_async
 
@@ -51,23 +49,6 @@ def safe_apply_chat_template(
 
 
 class MistralRenderer(BaseRenderer[MistralTokenizer]):
-    @classmethod
-    def from_config(  # type: ignore[override]
-        cls,
-        config: VllmConfig,
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "MistralRenderer":
-        model_config = config.model_config
-        if model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            tokenizer = cached_get_tokenizer(
-                tokenizer_cls=MistralTokenizer,
-                **tokenizer_kwargs,
-            )
-
-        return cls(config, tokenizer)
-
     def __init__(
         self,
         config: VllmConfig,

vllm/renderers/qwen_vl.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
-
-from vllm.config import VllmConfig
-from vllm.tokenizers import cached_get_tokenizer
-from vllm.tokenizers.qwen_vl import QwenVLTokenizer
-
-from .hf import HfRenderer
-
-
-class QwenVLRenderer(HfRenderer):
-    @classmethod
-    def from_config(  # type: ignore[override]
-        cls,
-        config: VllmConfig,
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "HfRenderer":
-        model_config = config.model_config
-        if model_config.skip_tokenizer_init:
-            tokenizer = None
-        else:
-            tokenizer = cached_get_tokenizer(
-                tokenizer_cls=QwenVLTokenizer,
-                **tokenizer_kwargs,
-            )
-
-        return HfRenderer(config, tokenizer)

vllm/renderers/registry.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 from vllm.logger import init_logger
-from vllm.tokenizers.registry import tokenizer_args_from_config
+from vllm.tokenizers import TokenizerLike
+from vllm.tokenizers.registry import (
+    cached_tokenizer_from_config,
+    tokenizer_args_from_config,
+)
 from vllm.utils.import_utils import resolve_obj_by_qualname
 
 from .base import BaseRenderer
@@ -19,9 +23,9 @@ _VLLM_RENDERERS = {
     "deepseek_v32": ("deepseek_v32", "DeepseekV32Renderer"),
     "hf": ("hf", "HfRenderer"),
     "grok2": ("grok2", "Grok2Renderer"),
-    "kimi_audio": ("kimi_audio", "KimiAudioRenderer"),
+    "kimi_audio": ("hf", "HfRenderer"),
     "mistral": ("mistral", "MistralRenderer"),
-    "qwen_vl": ("qwen_vl", "QwenVLRenderer"),
+    "qwen_vl": ("hf", "HfRenderer"),
     "terratorch": ("terratorch", "TerratorchRenderer"),
 }
 
@@ -58,10 +62,10 @@ class RendererRegistry:
         self,
         renderer_mode: str,
         config: "VllmConfig",
-        tokenizer_kwargs: dict[str, Any],
+        tokenizer: TokenizerLike | None,
     ) -> BaseRenderer:
         renderer_cls = self.load_renderer_cls(renderer_mode)
-        return renderer_cls.from_config(config, tokenizer_kwargs)
+        return renderer_cls(config, tokenizer)
 
 
 RENDERER_REGISTRY = RendererRegistry(
@@ -76,20 +80,7 @@ RENDERER_REGISTRY = RendererRegistry(
 def renderer_from_config(config: "VllmConfig", **kwargs):
     model_config = config.model_config
 
-    tokenizer_mode, tokenizer_name, args, kwargs = tokenizer_args_from_config(
-        model_config, **kwargs
-    )
-
-    if (
-        model_config.tokenizer_mode == "auto"
-        and model_config.model_impl == "terratorch"
-    ):
-        renderer_mode = "terratorch"
-    else:
-        renderer_mode = tokenizer_mode
+    tokenizer = cached_tokenizer_from_config(model_config, **kwargs)
+    renderer_mode, *_ = tokenizer_args_from_config(model_config, **kwargs)
 
-    return RENDERER_REGISTRY.load_renderer(
-        renderer_mode,
-        config,
-        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
-    )
+    return RENDERER_REGISTRY.load_renderer(renderer_mode, config, tokenizer)

vllm/renderers/terratorch.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
 
-from vllm.config import VllmConfig
 from vllm.entrypoints.chat_utils import (
     ChatCompletionMessageParam,
     ConversationMessage,
@@ -20,18 +18,6 @@ logger = init_logger(__name__)
 
 
 class TerratorchRenderer(BaseRenderer):
-    @classmethod
-    def from_config(
-        cls,
-        config: VllmConfig,  # type: ignore[override]
-        tokenizer_kwargs: dict[str, Any],
-    ) -> "TerratorchRenderer":
-        model_config = config.model_config
-        if not model_config.skip_tokenizer_init:
-            raise ValueError("Terratorch renderer requires `skip_tokenizer_init=True`")
-
-        return cls(config, None)
-
     def render_messages(
         self,
         messages: list[ChatCompletionMessageParam],