A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 9, 05:00 UTC
[Critical] Revert #33110 (#33500)
Cyrus Leung committed
Feb 1, 2026 at 13:06 UTC
b6bb2842cf30251951ccf6d22f808a9a2884d96e
1 file changed
-31
vllm/v1/engine/input_processor.py
-31
@@ -35,7 +35,6 @@ from vllm.tokenizers import TokenizerLike
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from vllm.tokenizers.mistral import MistralTokenizer
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from vllm.utils import length_from_prompt_token_ids_or_embeds, random_uuid
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from vllm.utils.torch_utils import set_default_torch_num_threads
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-from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.metrics.stats import MultiModalCacheStats
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from vllm.v1.structured_output.backend_guidance import (
@@ -69,17 +68,6 @@ class InputProcessor:
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self.mm_registry = mm_registry
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self.mm_processor_cache = mm_registry.processor_cache_from_config(vllm_config)
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- self.mm_encoder_cache_size = None
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- if (
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- self.mm_registry.supports_multimodal_inputs(self.model_config)
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- and not self.model_config.skip_tokenizer_init
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- ):
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- max_tokens_by_modality = mm_registry.get_max_tokens_per_item_by_modality(
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- self.model_config
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- )
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- _, self.mm_encoder_cache_size = compute_mm_encoder_budget(
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- self.vllm_config.scheduler_config, max_tokens_by_modality
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- )
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self.input_preprocessor = InputPreprocessor(
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self.model_config,
@@ -755,25 +743,6 @@ class InputProcessor:
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f"model length of {max_prompt_len}. {suggestion}"
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)
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- if (
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- prompt_type == "decoder"
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- and prompt_inputs["type"] == "multimodal"
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- and self.mm_encoder_cache_size is not None
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- ):
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- decoder_mm_positions = prompt_inputs["mm_placeholders"]
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- for modality, mm_positions in decoder_mm_positions.items():
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- for mm_position in mm_positions:
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- embed_length = mm_position.get_num_embeds
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- if embed_length > self.mm_encoder_cache_size:
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- raise ValueError(
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- f"The {prompt_type} prompt contains a(n) {modality} item "
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- f"with length {embed_length}, which exceeds the "
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- f"pre-allocated encoder cache size "
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- f"{self.mm_encoder_cache_size}. Please reduce the input "
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- f"size or increase the encoder cache size "
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- f"by setting --limit-mm-per-prompt at startup."
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- )
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-
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def stat_mm_cache(self) -> MultiModalCacheStats | None:
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return self.input_preprocessor.stat_mm_cache()
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