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[Refactor] Move profiling methods to MM budget (#33559)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-02-02 23:27:00 +08:00
committed by GitHub
parent 528e9b1490
commit d7e17aaacd
8 changed files with 201 additions and 296 deletions
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32
vllm/v1/engine/input_processor.py
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@@ -19,6 +19,7 @@ from vllm.inputs.preprocess import InputPreprocessor
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.multimodal.budget import MultiModalBudget
from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFeatureSpec,
@@ -34,7 +35,6 @@ from vllm.tokenizers import TokenizerLike
from vllm.tokenizers.mistral import MistralTokenizer
from vllm.utils import length_from_prompt_token_ids_or_embeds, random_uuid
from vllm.utils.torch_utils import set_default_torch_num_threads
from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
from vllm.v1.engine import EngineCoreRequest
from vllm.v1.metrics.stats import MultiModalCacheStats
from vllm.v1.structured_output.backend_guidance import (
@@ -59,32 +59,30 @@ class InputProcessor:
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
) -> None:
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.model_config = model_config = vllm_config.model_config
self.cache_config = vllm_config.cache_config
self.lora_config = vllm_config.lora_config
self.scheduler_config = vllm_config.scheduler_config
self.structured_outputs_config = vllm_config.structured_outputs_config
self.observability_config = vllm_config.observability_config
self.generation_config_fields = self.model_config.try_get_generation_config()
self.generation_config_fields = model_config.try_get_generation_config()
self.mm_registry = mm_registry
self.mm_processor_cache = mm_registry.processor_cache_from_config(vllm_config)
self.mm_encoder_cache_size = None
if (
self.mm_registry.supports_multimodal_inputs(self.model_config)
and not self.model_config.skip_tokenizer_init
):
with set_default_torch_num_threads():
max_tokens_by_modality = (
mm_registry.get_max_tokens_per_item_by_modality(self.model_config)
)
_, self.mm_encoder_cache_size = compute_mm_encoder_budget(
self.vllm_config.scheduler_config, max_tokens_by_modality
)
self.mm_encoder_cache_size: int | None = None
if (
mm_registry.supports_multimodal_inputs(model_config)
and not model_config.skip_tokenizer_init
):
mm_budget = MultiModalBudget(vllm_config, mm_registry)
self.mm_encoder_cache_size = mm_budget.encoder_cache_size
mm_budget.reset_cache() # Not used anymore
self.input_preprocessor = InputPreprocessor(
self.model_config,
vllm_config.observability_config,
model_config,
self.observability_config,
mm_registry,
mm_processor_cache=self.mm_processor_cache,
)