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[Refactor] Pass full VllmConfig to Renderer (#34485)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-02-13 14:48:38 +08:00
committed by GitHub
parent 1b4e8e53f8
commit 2f308214c0
18 changed files with 137 additions and 86 deletions
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7
tests/entrypoints/openai/test_chat_error.py
View File

@@ -59,11 +59,16 @@ class MockModelConfig:
return self.diff_sampling_param or {}
@dataclass
class MockVllmConfig:
model_config: MockModelConfig
def _build_renderer(model_config: MockModelConfig):
_, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
return HfRenderer(
model_config,
MockVllmConfig(model_config),
tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
)

7
tests/entrypoints/openai/test_completion_error.py
View File

@@ -58,6 +58,11 @@ class MockModelConfig:
return self.diff_sampling_param or {}
@dataclass
class MockVllmConfig:
model_config: MockModelConfig
def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
models = OpenAIServingModels(
engine_client=engine,
@@ -74,7 +79,7 @@ def _build_renderer(model_config: MockModelConfig):
_, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
return HfRenderer(
model_config,
MockVllmConfig(model_config),
tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
)

7
tests/entrypoints/openai/test_lora_resolvers.py
View File

@@ -57,6 +57,11 @@ class MockModelConfig:
return self.diff_sampling_param or {}
@dataclass
class MockVllmConfig:
model_config: MockModelConfig
class MockLoRAResolver(LoRAResolver):
async def resolve_lora(
self, base_model_name: str, lora_name: str
@@ -91,7 +96,7 @@ def _build_renderer(model_config: MockModelConfig):
_, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
return HfRenderer(
model_config,
MockVllmConfig(model_config),
tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
)

17
tests/entrypoints/openai/test_serving_chat.py
View File

@@ -534,11 +534,16 @@ class MockModelConfig:
return self.diff_sampling_param or {}
@dataclass
class MockVllmConfig:
model_config: MockModelConfig
def _build_renderer(model_config: MockModelConfig):
_, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
return HfRenderer(
model_config,
MockVllmConfig(model_config),
tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
)
@@ -749,7 +754,10 @@ async def test_serving_chat_mistral_token_ids_prompt_is_validated():
mock_engine.io_processor = MagicMock()
mock_tokenizer = MagicMock(spec=MistralTokenizer)
mock_renderer = MistralRenderer(mock_engine.model_config, tokenizer_kwargs={})
mock_renderer = MistralRenderer(
MockVllmConfig(mock_engine.model_config),
tokenizer_kwargs={},
)
mock_renderer._tokenizer = mock_tokenizer
# Force the Mistral chat template renderer to return token IDs.
# Choose a prompt length that is < max_model_len, but large enough that
@@ -788,7 +796,10 @@ async def test_serving_chat_mistral_token_ids_prompt_too_long_is_rejected():
mock_engine.io_processor = MagicMock()
mock_tokenizer = MagicMock(spec=MistralTokenizer)
mock_renderer = MistralRenderer(mock_engine.model_config, tokenizer_kwargs={})
mock_renderer = MistralRenderer(
MockVllmConfig(mock_engine.model_config),
tokenizer_kwargs={},
)
mock_renderer._tokenizer = mock_tokenizer
# prompt_token_ids length == max_model_len should be rejected for
# completion-like requests (ChatCompletionRequest).

55
tests/renderers/test_completions.py
View File

@@ -40,6 +40,11 @@ class MockModelConfig:
is_encoder_decoder: bool = False
@dataclass
class MockVllmConfig:
model_config: MockModelConfig
@dataclass
class DummyTokenizer:
truncation_side: str = "left"
@@ -72,7 +77,7 @@ def _build_renderer(
_, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
renderer = HfRenderer(
model_config,
MockVllmConfig(model_config),
tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
)
@@ -104,14 +109,14 @@ class TestValidatePrompt:
renderer = _build_renderer(MockModelConfig())
with pytest.raises(ValueError, match="at least one prompt"):
renderer.render_prompts(_preprocess_prompt(renderer.config, []))
renderer.render_prompts(_preprocess_prompt(renderer.model_config, []))
def test_invalid_type(self):
renderer = _build_renderer(MockModelConfig())
with pytest.raises(TypeError, match="should be a list of integers"):
renderer.render_prompts(
_preprocess_prompt(renderer.config, [[1, 2], ["foo", "bar"]]) # type: ignore[arg-type]
_preprocess_prompt(renderer.model_config, [[1, 2], ["foo", "bar"]]) # type: ignore[arg-type]
)
@@ -120,7 +125,9 @@ class TestRenderPrompt:
renderer = _build_renderer(MockModelConfig())
tokens = [101, 7592, 2088]
prompts = renderer.render_prompts(_preprocess_prompt(renderer.config, tokens))
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.model_config, tokens)
)
results = renderer.tokenize_prompts(
prompts,
TokenizeParams(max_total_tokens=100),
@@ -134,7 +141,7 @@ class TestRenderPrompt:
token_lists = [[101, 7592, 2088], [102, 1234, 5678, 9012], [103, 4567]]
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, token_lists)
_preprocess_prompt(renderer.model_config, token_lists)
)
results = renderer.tokenize_prompts(
prompts,
@@ -151,7 +158,7 @@ class TestRenderPrompt:
text_input = "x" * 10
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, text_input)
_preprocess_prompt(renderer.model_config, text_input)
)
results = renderer.tokenize_prompts(
prompts,
@@ -166,7 +173,7 @@ class TestRenderPrompt:
text_list_input = ["x" * 10, "x" * 12, "x" * 14]
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, text_list_input)
_preprocess_prompt(renderer.model_config, text_list_input)
)
results = renderer.tokenize_prompts(
prompts,
@@ -181,7 +188,7 @@ class TestRenderPrompt:
renderer = _build_renderer(MockModelConfig())
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, "x" * 200)
_preprocess_prompt(renderer.model_config, "x" * 200)
)
results = renderer.tokenize_prompts(
prompts,
@@ -195,7 +202,7 @@ class TestRenderPrompt:
renderer = _build_renderer(MockModelConfig())
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, "x" * 200)
_preprocess_prompt(renderer.model_config, "x" * 200)
)
results = renderer.tokenize_prompts(
prompts,
@@ -209,7 +216,7 @@ class TestRenderPrompt:
renderer = _build_renderer(MockModelConfig())
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, "x" * 200)
_preprocess_prompt(renderer.model_config, "x" * 200)
)
results = renderer.tokenize_prompts(
prompts,
@@ -224,7 +231,7 @@ class TestRenderPrompt:
long_tokens = [100, 101, 102, 103, 104, 105, 106, 107, 108, 109] # 10 tokens
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, long_tokens)
_preprocess_prompt(renderer.model_config, long_tokens)
)
results = renderer.tokenize_prompts(
prompts,
@@ -240,7 +247,7 @@ class TestRenderPrompt:
long_tokens = [100, 101, 102, 103, 104, 105, 106, 107, 108, 109] # 10 tokens
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, long_tokens)
_preprocess_prompt(renderer.model_config, long_tokens)
)
results = renderer.tokenize_prompts(
prompts,
@@ -257,7 +264,7 @@ class TestRenderPrompt:
# Exceeds max_total_tokens and max_total_tokens * VLLM_MAX_CHARS_PER_TOKEN
long_tokens = "x" * 150
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, long_tokens)
_preprocess_prompt(renderer.model_config, long_tokens)
)
with pytest.raises(
@@ -278,7 +285,7 @@ class TestRenderPrompt:
# Exceeds max_total_tokens but not max_total_tokens * VLLM_MAX_CHARS_PER_TOKEN
long_tokens = "x" * 150
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, long_tokens)
_preprocess_prompt(renderer.model_config, long_tokens)
)
with pytest.raises(
@@ -299,7 +306,7 @@ class TestRenderPrompt:
long_tokens = list(range(150)) # Exceeds max_total_tokens=100
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, long_tokens)
_preprocess_prompt(renderer.model_config, long_tokens)
)
with pytest.raises(
@@ -315,7 +322,7 @@ class TestRenderPrompt:
renderer = _build_renderer(MockModelConfig(skip_tokenizer_init=True))
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, "Hello world")
_preprocess_prompt(renderer.model_config, "Hello world")
)
with pytest.raises(ValueError, match="`skip_tokenizer_init=True`"):
@@ -328,7 +335,9 @@ class TestRenderPrompt:
renderer = _build_renderer(MockModelConfig())
tokens = [1, 2, 3, 4]
prompts = renderer.render_prompts(_preprocess_prompt(renderer.config, tokens))
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.model_config, tokens)
)
results = renderer.tokenize_prompts(
prompts,
TokenizeParams(
@@ -358,7 +367,7 @@ class TestRenderEmbedPrompt:
embed_bytes = self._create_test_embed_bytes(tensor_input)
prompts = renderer.render_prompts(
_preprocess_prompt(renderer.config, embed_bytes)
_preprocess_prompt(renderer.model_config, embed_bytes)
)
results = renderer.tokenize_prompts(
prompts,
@@ -379,7 +388,7 @@ class TestRenderEmbedPrompt:
prompts = renderer.render_prompts(
_preprocess_prompt(
renderer.config,
renderer.model_config,
[self._create_test_embed_bytes(t) for t in tensor_inputs],
)
)
@@ -400,7 +409,7 @@ class TestRenderEmbedPrompt:
prompts = renderer.render_prompts(
_preprocess_prompt(
renderer.config, self._create_test_embed_bytes(tensor_input)
renderer.model_config, self._create_test_embed_bytes(tensor_input)
)
)
results = renderer.tokenize_prompts(
@@ -427,7 +436,7 @@ class TestRenderEmbedPrompt:
prompts = renderer.render_prompts(
_preprocess_prompt(
renderer.config, self._create_test_embed_bytes(tensor_input)
renderer.model_config, self._create_test_embed_bytes(tensor_input)
)
)
results = renderer.tokenize_prompts(
@@ -446,7 +455,7 @@ class TestRenderEmbedPrompt:
prompts = renderer.render_prompts(
_preprocess_prompt(
renderer.config, self._create_test_embed_bytes(tensor_input)
renderer.model_config, self._create_test_embed_bytes(tensor_input)
)
)
results = renderer.tokenize_prompts(
@@ -466,7 +475,7 @@ class TestRenderEmbedPrompt:
prompts = renderer.render_prompts(
_preprocess_prompt(
renderer.config,
renderer.model_config,
[text_input, self._create_test_embed_bytes(tensor_input)],
)
)

10
tests/renderers/test_mistral.py
View File

@@ -38,6 +38,11 @@ class MockModelConfig:
is_encoder_decoder: bool = False
@dataclass
class MockVllmConfig:
model_config: MockModelConfig
@pytest.mark.asyncio
async def test_async_mistral_tokenizer_does_not_block_event_loop():
expected_tokens = [1, 2, 3]
@@ -50,7 +55,10 @@ async def test_async_mistral_tokenizer_does_not_block_event_loop():
mock_model_config = MockModelConfig(skip_tokenizer_init=True)
mock_tokenizer = Mock(spec=MistralTokenizer)
mock_tokenizer.apply_chat_template = mocked_apply_chat_template
mock_renderer = MistralRenderer(mock_model_config, tokenizer_kwargs={})
mock_renderer = MistralRenderer(
MockVllmConfig(mock_model_config),
tokenizer_kwargs={},
)
mock_renderer._tokenizer = mock_tokenizer
task = mock_renderer.render_messages_async([], ChatParams())

5
tests/test_inputs.py
View File

@@ -3,7 +3,7 @@
import pytest
from vllm.config import ModelConfig
from vllm.config import ModelConfig, VllmConfig
from vllm.inputs.preprocess import InputPreprocessor
pytestmark = pytest.mark.cpu_test
@@ -20,7 +20,8 @@ pytestmark = pytest.mark.cpu_test
)
def test_preprocessor_always_mm_code_path(model_id, prompt):
model_config = ModelConfig(model=model_id)
input_preprocessor = InputPreprocessor(model_config)
vllm_config = VllmConfig(model_config=model_config)
input_preprocessor = InputPreprocessor(vllm_config)
# HF processor adds sep token
tokenizer = input_preprocessor.get_tokenizer()

11
vllm/inputs/preprocess.py
View File

@@ -6,7 +6,7 @@ from typing import Any, overload
from typing_extensions import assert_never
from vllm.config import ModelConfig, ObservabilityConfig
from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.multimodal.cache import BaseMultiModalProcessorCache
@@ -54,17 +54,16 @@ logger = init_logger(__name__)
class InputPreprocessor:
def __init__(
self,
model_config: ModelConfig,
observability_config: ObservabilityConfig | None = None,
vllm_config: VllmConfig,
renderer: BaseRenderer | None = None,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
mm_processor_cache: BaseMultiModalProcessorCache | None = None,
) -> None:
super().__init__()
self.model_config = model_config
self.observability_config = observability_config
self.renderer = renderer or renderer_from_config(model_config)
self.model_config = vllm_config.model_config
self.observability_config = vllm_config.observability_config
self.renderer = renderer or renderer_from_config(vllm_config)
self.mm_registry = mm_registry
self.mm_processor_cache = mm_processor_cache

14
vllm/renderers/base.py
View File

@@ -21,7 +21,7 @@ from .inputs.preprocess import extract_target_prompt
from .params import ChatParams, TokenizeParams
if TYPE_CHECKING:
from vllm.config import ModelConfig
from vllm.config import VllmConfig
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ConversationMessage,
@@ -35,15 +35,15 @@ class BaseRenderer(ABC):
@abstractmethod
def from_config(
cls,
config: "ModelConfig",
config: "VllmConfig",
tokenizer_kwargs: dict[str, Any],
) -> "BaseRenderer":
raise NotImplementedError
def __init__(self, config: "ModelConfig") -> None:
def __init__(self, config: "VllmConfig") -> None:
super().__init__()
self.config = config
self.model_config = config.model_config
# Lazy initialization since offline LLM doesn't use async
self._async_tokenizer: AsyncMicrobatchTokenizer | None = None
@@ -90,7 +90,7 @@ class BaseRenderer(ABC):
prompt: DictPrompt | bytes,
) -> DictPrompt:
if isinstance(prompt, bytes):
embeds = safe_load_prompt_embeds(self.config, prompt)
embeds = safe_load_prompt_embeds(self.model_config, prompt)
prompt = EmbedsPrompt(prompt_embeds=embeds)
return prompt
@@ -310,7 +310,7 @@ class BaseRenderer(ABC):
return
for prompt in prompts:
target_prompt = extract_target_prompt(self.config, prompt)
target_prompt = extract_target_prompt(self.model_config, prompt)
target_prompt.update(prompt_extras) # type: ignore[arg-type]
# Top-level methods
@@ -325,7 +325,7 @@ class BaseRenderer(ABC):
# NOTE: Some MM models have non-default `add_special_tokens`
# so we handle tokenization in multi-modal processor
if self.config.is_multimodal_model:
if self.model_config.is_multimodal_model:
self._apply_prompt_extras(dict_prompts, prompt_extras)
return dict_prompts

13
vllm/renderers/deepseek_v32.py
View File

@@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from vllm.config import ModelConfig
from vllm.config import VllmConfig
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ConversationMessage,
@@ -26,19 +26,20 @@ class DeepseekV32Renderer(BaseRenderer):
@classmethod
def from_config(
cls,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> "BaseRenderer":
return cls(config, tokenizer_kwargs)
def __init__(
self,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> None:
super().__init__(config)
if config.skip_tokenizer_init:
model_config = self.model_config
if model_config.skip_tokenizer_init:
tokenizer = None
else:
tokenizer = cached_get_tokenizer(
@@ -67,7 +68,7 @@ class DeepseekV32Renderer(BaseRenderer):
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = parse_chat_messages(
messages,
self.config,
self.model_config,
content_format="string",
)
@@ -93,7 +94,7 @@ class DeepseekV32Renderer(BaseRenderer):
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = await parse_chat_messages_async(
messages,
self.config,
self.model_config,
content_format="string",
)

13
vllm/renderers/grok2.py
View File

@@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from vllm.config import ModelConfig
from vllm.config import VllmConfig
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ConversationMessage,
@@ -25,19 +25,20 @@ class Grok2Renderer(BaseRenderer):
@classmethod
def from_config(
cls,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> "BaseRenderer":
return cls(config, tokenizer_kwargs)
def __init__(
self,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> None:
super().__init__(config)
if config.skip_tokenizer_init:
model_config = self.model_config
if model_config.skip_tokenizer_init:
tokenizer = None
else:
tokenizer = cached_get_tokenizer(
@@ -66,7 +67,7 @@ class Grok2Renderer(BaseRenderer):
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = parse_chat_messages(
messages,
self.config,
self.model_config,
content_format="string",
)
@@ -92,7 +93,7 @@ class Grok2Renderer(BaseRenderer):
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = await parse_chat_messages_async(
messages,
self.config,
self.model_config,
content_format="string",
)

15
vllm/renderers/hf.py
View File

@@ -14,7 +14,7 @@ import jinja2.nodes
import jinja2.parser
import jinja2.sandbox
from vllm.config import ModelConfig
from vllm.config import ModelConfig, VllmConfig
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ChatTemplateContentFormat,
@@ -589,23 +589,24 @@ class HfRenderer(BaseRenderer):
@classmethod
def from_config(
cls,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> "BaseRenderer":
return cls(config, tokenizer_kwargs)
def __init__(
self,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> None:
super().__init__(config)
model_config = self.model_config
self.use_unified_vision_chunk = getattr(
config.hf_config, "use_unified_vision_chunk", False
model_config.hf_config, "use_unified_vision_chunk", False
)
if config.skip_tokenizer_init:
if model_config.skip_tokenizer_init:
tokenizer = None
else:
tokenizer = cast(
@@ -634,7 +635,7 @@ class HfRenderer(BaseRenderer):
messages: list[ChatCompletionMessageParam],
params: ChatParams,
) -> tuple[list[ConversationMessage], DictPrompt]:
model_config = self.config
model_config = self.model_config
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = parse_chat_messages(
@@ -688,7 +689,7 @@ class HfRenderer(BaseRenderer):
messages: list[ChatCompletionMessageParam],
params: ChatParams,
) -> tuple[list[ConversationMessage], DictPrompt]:
model_config = self.config
model_config = self.model_config
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = await parse_chat_messages_async(

13
vllm/renderers/mistral.py
View File

@@ -3,7 +3,7 @@
from concurrent.futures import ThreadPoolExecutor
from typing import Any
from vllm.config import ModelConfig
from vllm.config import VllmConfig
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ConversationMessage,
@@ -54,19 +54,20 @@ class MistralRenderer(BaseRenderer):
@classmethod
def from_config(
cls,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> "BaseRenderer":
return cls(config, tokenizer_kwargs)
def __init__(
self,
config: ModelConfig,
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> None:
super().__init__(config)
if config.skip_tokenizer_init:
model_config = self.model_config
if model_config.skip_tokenizer_init:
tokenizer = None
else:
tokenizer = cached_get_tokenizer(
@@ -100,7 +101,7 @@ class MistralRenderer(BaseRenderer):
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = parse_chat_messages(
messages,
self.config,
self.model_config,
content_format="string",
)
@@ -126,7 +127,7 @@ class MistralRenderer(BaseRenderer):
tokenizer = self.get_tokenizer()
conversation, mm_data, mm_uuids = await parse_chat_messages_async(
messages,
self.config,
self.model_config,
content_format="string",
)

14
vllm/renderers/registry.py
View File

@@ -10,7 +10,7 @@ from vllm.utils.import_utils import resolve_obj_by_qualname
from .base import BaseRenderer
if TYPE_CHECKING:
from vllm.config import ModelConfig
from vllm.config import VllmConfig
logger = init_logger(__name__)
@@ -55,7 +55,7 @@ class RendererRegistry:
def load_renderer(
self,
renderer_mode: str,
config: "ModelConfig",
config: "VllmConfig",
tokenizer_kwargs: dict[str, Any],
) -> BaseRenderer:
renderer_cls = self.load_renderer_cls(renderer_mode)
@@ -71,12 +71,16 @@ RENDERER_REGISTRY = RendererRegistry(
"""The global `RendererRegistry` instance."""
def renderer_from_config(config: "ModelConfig", **kwargs):
def renderer_from_config(config: "VllmConfig", **kwargs):
model_config = config.model_config
tokenizer_mode, tokenizer_name, args, kwargs = tokenizer_args_from_config(
config, **kwargs
model_config, **kwargs
)
if config.tokenizer_mode == "auto" and config.model_impl == "terratorch":
if (
model_config.tokenizer_mode == "auto"
and model_config.model_impl == "terratorch"
):
renderer_mode = "terratorch"
else:
renderer_mode = tokenizer_mode

13
vllm/renderers/terratorch.py
View File

@@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from vllm.config import ModelConfig
from vllm.config import VllmConfig
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ConversationMessage,
@@ -24,15 +24,16 @@ class TerratorchRenderer(BaseRenderer):
@classmethod
def from_config(
cls,
config: "ModelConfig",
config: VllmConfig,
tokenizer_kwargs: dict[str, Any],
) -> "BaseRenderer":
return cls(config)
def __init__(self, config: ModelConfig) -> None:
def __init__(self, config: VllmConfig) -> None:
super().__init__(config)
if not config.skip_tokenizer_init:
model_config = self.model_config
if not model_config.skip_tokenizer_init:
raise ValueError("Terratorch renderer requires `skip_tokenizer_init=True`")
@property
@@ -47,7 +48,7 @@ class TerratorchRenderer(BaseRenderer):
messages: list[ChatCompletionMessageParam],
params: ChatParams,
) -> tuple[list[ConversationMessage], DictPrompt]:
model_config = self.config
model_config = self.model_config
conversation, mm_data, mm_uuids = parse_chat_messages(
messages,
@@ -68,7 +69,7 @@ class TerratorchRenderer(BaseRenderer):
messages: list[ChatCompletionMessageParam],
params: ChatParams,
) -> tuple[list[ConversationMessage], DictPrompt]:
model_config = self.config
model_config = self.model_config
conversation, mm_data, mm_uuids = await parse_chat_messages_async(
messages,

2
vllm/v1/engine/async_llm.py
View File

@@ -132,7 +132,7 @@ class AsyncLLM(EngineClient):
"enabling logging without default stat loggers."
)
self.renderer = renderer = renderer_from_config(self.model_config)
self.renderer = renderer = renderer_from_config(self.vllm_config)
self.io_processor = get_io_processor(
self.vllm_config,
self.model_config.io_processor_plugin,

5
vllm/v1/engine/input_processor.py
View File

@@ -59,7 +59,7 @@ class InputProcessor:
self.generation_config_fields = model_config.try_get_generation_config()
self.renderer = renderer or renderer_from_config(model_config)
self.renderer = renderer or renderer_from_config(vllm_config)
self.mm_registry = mm_registry
self.mm_processor_cache = mm_registry.processor_cache_from_config(vllm_config)
@@ -75,8 +75,7 @@ class InputProcessor:
mm_budget.reset_cache() # Not used anymore
self.input_preprocessor = InputPreprocessor(
model_config,
self.observability_config,
vllm_config,
renderer=renderer,
mm_registry=mm_registry,
mm_processor_cache=self.mm_processor_cache,

2
vllm/v1/engine/llm_engine.py
View File

@@ -90,7 +90,7 @@ class LLMEngine:
self.dp_group = None
self.should_execute_dummy_batch = False
self.renderer = renderer = renderer_from_config(self.model_config)
self.renderer = renderer = renderer_from_config(self.vllm_config)
self.io_processor = get_io_processor(
self.vllm_config,
self.model_config.io_processor_plugin,