biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Critical] Revert #33110 (#33500)

Browse Source
This commit is contained in:
Cyrus Leung
2026-02-01 13:06:42 +08:00
committed by GitHub
parent 79b6ec6aab
commit b6bb2842cf
1 changed files with 0 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files

31
vllm/v1/engine/input_processor.py
View File

@@ -35,7 +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 (
@@ -69,17 +68,6 @@ class InputProcessor:
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
):
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.input_preprocessor = InputPreprocessor(
self.model_config,
@@ -755,25 +743,6 @@ class InputProcessor:
f"model length of {max_prompt_len}. {suggestion}"
)
if (
prompt_type == "decoder"
and prompt_inputs["type"] == "multimodal"
and self.mm_encoder_cache_size is not None
):
decoder_mm_positions = prompt_inputs["mm_placeholders"]
for modality, mm_positions in decoder_mm_positions.items():
for mm_position in mm_positions:
embed_length = mm_position.get_num_embeds
if embed_length > self.mm_encoder_cache_size:
raise ValueError(
f"The {prompt_type} prompt contains a(n) {modality} item "
f"with length {embed_length}, which exceeds the "
f"pre-allocated encoder cache size "
f"{self.mm_encoder_cache_size}. Please reduce the input "
f"size or increase the encoder cache size "
f"by setting --limit-mm-per-prompt at startup."
)
def stat_mm_cache(self) -> MultiModalCacheStats | None:
return self.input_preprocessor.stat_mm_cache()