[Refactor] Make Renderer an abstract class (#33479)

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

vllm/engine/protocol.py

@@ -11,7 +11,7 @@ from vllm.lora.request import LoRARequest
 from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.plugins.io_processors import IOProcessor
 from vllm.pooling_params import PoolingParams
-from vllm.renderers import RendererLike
+from vllm.renderers import BaseRenderer
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.v1.engine import EngineCoreRequest
@@ -28,7 +28,7 @@ class EngineClient(ABC):
 
     @property
     @abstractmethod
-    def renderer(self) -> RendererLike: ...
+    def renderer(self) -> BaseRenderer: ...
 
     @property
     @abstractmethod

vllm/renderers/__init__.py

@@ -2,11 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from .params import ChatParams, TokenizeParams, merge_kwargs
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 from .registry import RendererRegistry, renderer_from_config
 
 __all__ = [
-    "RendererLike",
+    "BaseRenderer",
     "RendererRegistry",
     "renderer_from_config",
     "ChatParams",

vllm/renderers/deepseek_v32.py

@@ -15,18 +15,18 @@ from vllm.tokenizers import cached_get_tokenizer
 from vllm.tokenizers.deepseek_v32 import DeepseekV32Tokenizer
 
 from .params import ChatParams
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 
 logger = init_logger(__name__)
 
 
-class DeepseekV32Renderer(RendererLike):
+class DeepseekV32Renderer(BaseRenderer):
     @classmethod
     def from_config(
         cls,
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
-    ) -> "RendererLike":
+    ) -> "BaseRenderer":
         return cls(config, tokenizer_kwargs)
 
     def __init__(
@@ -34,9 +34,7 @@ class DeepseekV32Renderer(RendererLike):
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
     ) -> None:
-        super().__init__()
-
-        self.config = config
+        super().__init__(config)
 
         if config.skip_tokenizer_init:
             tokenizer = None

vllm/renderers/grok2.py

@@ -15,18 +15,18 @@ from vllm.tokenizers import cached_get_tokenizer
 from vllm.tokenizers.grok2 import Grok2Tokenizer
 
 from .params import ChatParams
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 
 logger = init_logger(__name__)
 
 
-class Grok2Renderer(RendererLike):
+class Grok2Renderer(BaseRenderer):
     @classmethod
     def from_config(
         cls,
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
-    ) -> "RendererLike":
+    ) -> "BaseRenderer":
         return cls(config, tokenizer_kwargs)
 
     def __init__(
@@ -34,9 +34,7 @@ class Grok2Renderer(RendererLike):
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
     ) -> None:
-        super().__init__()
-
-        self.config = config
+        super().__init__(config)
 
         if config.skip_tokenizer_init:
             tokenizer = None

vllm/renderers/hf.py

@@ -34,7 +34,7 @@ from vllm.transformers_utils.processor import cached_get_processor
 from vllm.utils.func_utils import supports_kw
 
 from .params import ChatParams
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 
 if TYPE_CHECKING:
     from vllm.multimodal.inputs import MultiModalDataDict, MultiModalUUIDDict
@@ -584,13 +584,13 @@ def replace_vision_chunk_video_placeholder(
     return prompt_raw
 
 
-class HfRenderer(RendererLike):
+class HfRenderer(BaseRenderer):
     @classmethod
     def from_config(
         cls,
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
-    ) -> "RendererLike":
+    ) -> "BaseRenderer":
         return cls(config, tokenizer_kwargs)
 
     def __init__(
@@ -598,9 +598,8 @@ class HfRenderer(RendererLike):
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
     ) -> None:
-        super().__init__()
+        super().__init__(config)
 
-        self.config = config
         self.use_unified_vision_chunk = getattr(
             config.hf_config, "use_unified_vision_chunk", False
         )

vllm/renderers/mistral.py

@@ -17,7 +17,7 @@ from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.utils.async_utils import make_async
 
 from .params import ChatParams
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 
 logger = init_logger(__name__)
 
@@ -49,13 +49,13 @@ def safe_apply_chat_template(
         raise ValueError(str(e)) from e
 
 
-class MistralRenderer(RendererLike):
+class MistralRenderer(BaseRenderer):
     @classmethod
     def from_config(
         cls,
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
-    ) -> "RendererLike":
+    ) -> "BaseRenderer":
         return cls(config, tokenizer_kwargs)
 
     def __init__(
@@ -63,9 +63,7 @@ class MistralRenderer(RendererLike):
         config: ModelConfig,
         tokenizer_kwargs: dict[str, Any],
     ) -> None:
-        super().__init__()
-
-        self.config = config
+        super().__init__(config)
 
         if config.skip_tokenizer_init:
             tokenizer = None

vllm/renderers/protocol.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
-from typing import TYPE_CHECKING, Any, Protocol
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any
 
 from vllm.inputs import EmbedsPrompt, TextPrompt, TokensPrompt
 from vllm.tokenizers import TokenizerLike
@@ -19,19 +20,26 @@ if TYPE_CHECKING:
     )
 
 
-class RendererLike(Protocol):
-    config: "ModelConfig"
-    _async_tokenizer: AsyncMicrobatchTokenizer
-
+class BaseRenderer(ABC):
     @classmethod
+    @abstractmethod
     def from_config(
         cls,
         config: "ModelConfig",
         tokenizer_kwargs: dict[str, Any],
-    ) -> "RendererLike":
+    ) -> "BaseRenderer":
         raise NotImplementedError
 
+    def __init__(self, config: "ModelConfig") -> None:
+        super().__init__()
+
+        self.config = config
+
+        # Lazy initialization since offline LLM doesn't use async
+        self._async_tokenizer: AsyncMicrobatchTokenizer | None = None
+
     @property
+    @abstractmethod
     def tokenizer(self) -> TokenizerLike | None:
         raise NotImplementedError
 
@@ -43,8 +51,7 @@ class RendererLike(Protocol):
         return tokenizer
 
     def get_async_tokenizer(self) -> AsyncMicrobatchTokenizer:
-        # Lazy initialization since offline LLM doesn't use async
-        if not hasattr(self, "_async_tokenizer"):
+        if self._async_tokenizer is None:
             self._async_tokenizer = AsyncMicrobatchTokenizer(self.get_tokenizer())
 
         return self._async_tokenizer
@@ -104,6 +111,7 @@ class RendererLike(Protocol):
     ) -> list[TextPrompt | TokensPrompt | EmbedsPrompt]:
         return self.render_completions(prompt_input, prompt_embeds)
 
+    @abstractmethod
     def render_messages(
         self,
         messages: list["ChatCompletionMessageParam"],

vllm/renderers/registry.py

@@ -7,7 +7,7 @@ from vllm.logger import init_logger
 from vllm.tokenizers.registry import tokenizer_args_from_config
 from vllm.utils.import_utils import resolve_obj_by_qualname
 
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig
@@ -43,7 +43,7 @@ class RendererRegistry:
 
         return None
 
-    def load_renderer_cls(self, renderer_mode: str) -> type[RendererLike]:
+    def load_renderer_cls(self, renderer_mode: str) -> type[BaseRenderer]:
         if renderer_mode not in self.renderers:
             raise ValueError(f"No renderer registered for {renderer_mode=!r}.")
 
@@ -57,7 +57,7 @@ class RendererRegistry:
         renderer_mode: str,
         config: "ModelConfig",
         tokenizer_kwargs: dict[str, Any],
-    ) -> RendererLike:
+    ) -> BaseRenderer:
         renderer_cls = self.load_renderer_cls(renderer_mode)
         return renderer_cls.from_config(config, tokenizer_kwargs)
 

vllm/renderers/terratorch.py

@@ -14,24 +14,22 @@ from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
 
 from .params import ChatParams
-from .protocol import RendererLike
+from .protocol import BaseRenderer
 
 logger = init_logger(__name__)
 
 
-class TerratorchRenderer(RendererLike):
+class TerratorchRenderer(BaseRenderer):
     @classmethod
     def from_config(
         cls,
         config: "ModelConfig",
         tokenizer_kwargs: dict[str, Any],
-    ) -> "RendererLike":
+    ) -> "BaseRenderer":
         return cls(config)
 
     def __init__(self, config: ModelConfig) -> None:
-        super().__init__()
-
-        self.config = config
+        super().__init__(config)
 
         if not config.skip_tokenizer_init:
             raise ValueError("Terratorch renderer requires `skip_tokenizer_init=True`")

vllm/v1/engine/async_llm.py

@@ -24,7 +24,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.outputs import STREAM_FINISHED, PoolingRequestOutput, RequestOutput
 from vllm.plugins.io_processors import get_io_processor
 from vllm.pooling_params import PoolingParams
-from vllm.renderers import RendererLike, merge_kwargs
+from vllm.renderers import BaseRenderer, merge_kwargs
 from vllm.sampling_params import RequestOutputKind, SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.tokenizers import TokenizerLike
@@ -844,7 +844,7 @@ class AsyncLLM(EngineClient):
         return self.input_processor.get_tokenizer()
 
     @property
-    def renderer(self) -> RendererLike:
+    def renderer(self) -> BaseRenderer:
         return self.input_processor.renderer
 
     async def is_tracing_enabled(self) -> bool:

vllm/v1/engine/input_processor.py

@@ -29,7 +29,7 @@ from vllm.multimodal.parse import ModalityDataItems, MultiModalDataItems
 from vllm.multimodal.processing.context import set_request_id
 from vllm.multimodal.utils import argsort_mm_positions
 from vllm.pooling_params import PoolingParams
-from vllm.renderers import RendererLike
+from vllm.renderers import BaseRenderer
 from vllm.sampling_params import _SAMPLING_EPS, SamplingParams
 from vllm.tokenizers import TokenizerLike
 from vllm.tokenizers.mistral import MistralTokenizer
@@ -96,7 +96,7 @@ class InputProcessor:
         return self.input_preprocessor.get_tokenizer()
 
     @property
-    def renderer(self) -> RendererLike:
+    def renderer(self) -> BaseRenderer:
         return self.input_preprocessor.renderer
 
     def _validate_logprobs(

vllm/v1/engine/llm_engine.py

@@ -21,7 +21,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.plugins.io_processors import get_io_processor
 from vllm.pooling_params import PoolingParams
-from vllm.renderers import RendererLike
+from vllm.renderers import BaseRenderer
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.tokenizers import TokenizerLike
@@ -367,7 +367,7 @@ class LLMEngine:
         return self.input_processor.get_tokenizer()
 
     @property
-    def renderer(self) -> RendererLike:
+    def renderer(self) -> BaseRenderer:
         return self.input_processor.renderer
 
     def do_log_stats(self) -> None: