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[Renderer] Separate out RendererConfig from ModelConfig (#30145)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-12-07 15:15:42 +08:00
committed by GitHub
parent a49d813fa8
commit 27f4c2fd46
105 changed files with 969 additions and 797 deletions
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22
vllm/model_executor/models/voxtral.py
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@@ -20,7 +20,7 @@ from mistral_common.tokens.tokenizers.audio import Audio, AudioEncoder
from transformers import BatchFeature, TensorType, WhisperConfig
from transformers.tokenization_utils_base import TextInput
from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
from vllm.config import RendererConfig, SpeechToTextConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.inputs.data import PromptType
from vllm.logger import init_logger
@@ -176,7 +176,7 @@ class VoxtralProcessorAdapter:
class VoxtralProcessingInfo(BaseProcessingInfo):
def get_tokenizer(self) -> MistralTokenizer:
tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
tokenizer = cached_tokenizer_from_config(self.ctx.renderer_config)
if not isinstance(tokenizer, MistralTokenizer):
raise ValueError("This model requires `--tokenizer-mode mistral`")
@@ -339,7 +339,7 @@ class VoxtralForConditionalGeneration(
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
self.tokenizer = cached_tokenizer_from_config(vllm_config.renderer_config)
# update quant config to so that ignored module and target module names
# match the vLLM model names
@@ -450,9 +450,11 @@ class VoxtralForConditionalGeneration(
@classmethod
def get_speech_to_text_config(
cls, model_config: ModelConfig, task_type: str
cls,
renderer_config: RendererConfig,
task_type: str,
) -> SpeechToTextConfig:
tokenizer = cached_tokenizer_from_config(model_config)
tokenizer = cached_tokenizer_from_config(renderer_config)
audio_config = tokenizer.instruct.audio_encoder.audio_config
max_audio_clip_s = audio_config.chunk_length_s
sample_rate = audio_config.sampling_rate
@@ -468,17 +470,17 @@ class VoxtralForConditionalGeneration(
def get_generation_prompt(
cls,
audio: np.ndarray,
model_config: ModelConfig,
renderer_config: RendererConfig, # not needed here
stt_config: SpeechToTextConfig,
language: str | None,
task_type: Literal["transcribe", "translate"],
request_prompt: str,
to_language: str | None,
) -> PromptType:
tokenizer = cached_tokenizer_from_config(model_config)
tokenizer = cached_tokenizer_from_config(renderer_config)
audio = Audio(audio, int(stt_config.sample_rate), format="wav") # lossless
req = TranscriptionRequest(
model=model_config.model,
model=renderer_config.model_config.model,
audio=RawAudio.from_audio(audio),
language=language,
)
@@ -494,14 +496,14 @@ class VoxtralForConditionalGeneration(
cls,
audio_duration_s: float,
stt_config: SpeechToTextConfig,
model_config: ModelConfig,
renderer_config: RendererConfig,
) -> int | None:
"""
Map from audio duration to number of audio tokens produced by the ASR
model, without running a forward pass.
This is used for estimating the amount of processing for this audio.
"""
tokenizer = cached_tokenizer_from_config(model_config)
tokenizer = cached_tokenizer_from_config(renderer_config)
adapter = VoxtralProcessorAdapter(tokenizer)
return adapter.get_num_audio_tokens(
int(audio_duration_s * stt_config.sample_rate)