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 15, 06:00 UTC
[Misc] Remove unused `MultiModalHasher.hash_prompt_mm_data` (#19422)
Signed-off-by: Lukas Geiger <lukas.geiger94@gmail.com>
Lukas Geiger committed
Jun 11, 2025 at 07:18 UTC
96ada386b765793cf65e1434ad4a6afc50681620
1 file changed
-29
vllm/multimodal/hasher.py
-29
@@ -3,7 +3,6 @@
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import pickle
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from collections.abc import Iterable, Mapping
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-from typing import TYPE_CHECKING, Optional
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import numpy as np
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import torch
@@ -13,9 +12,6 @@ from PIL import Image
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from vllm.logger import init_logger
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from vllm.multimodal.image import convert_image_mode
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-if TYPE_CHECKING:
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- from vllm.inputs import TokensPrompt
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-
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logger = init_logger(__name__)
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MultiModalHashDict = Mapping[str, list[str]]
@@ -91,28 +87,3 @@ class MultiModalHasher:
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hasher.update(v_bytes)
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return hasher.hexdigest()
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-
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- @classmethod
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- def hash_prompt_mm_data(
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- cls, prompt: "TokensPrompt") -> Optional["MultiModalHashDict"]:
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- """Hash multimodal data in the user input prompt if they exist."""
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-
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- if "multi_modal_data" not in prompt:
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- return None
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-
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- mm_data = prompt["multi_modal_data"]
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- if not mm_data:
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- # mm_data can be None or an empty dict.
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- return None
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-
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- mm_items = {
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- modality: items if isinstance(items, list) else [items]
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- for modality, items in mm_data.items()
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- }
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-
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- mm_hashes = {
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- modality: [cls.hash_kwargs(**{modality: item}) for item in items]
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- for modality, items in mm_items.items()
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- }
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-
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- return mm_hashes