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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import pickle
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from typing import TYPE_CHECKING, Iterable, Mapping, Optional
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import numpy as np
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import torch
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from blake3 import blake3
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from PIL import Image
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from vllm.logger import init_logger
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if TYPE_CHECKING:
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from vllm.inputs import TokensPrompt
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logger = init_logger(__name__)
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MultiModalHashDict = Mapping[str, list[str]]
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"""
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A dictionary containing hashes for items in each modality.
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"""
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class MultiModalHasher:
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@classmethod
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def serialize_item(cls, obj: object) -> bytes:
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# Simple cases
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if isinstance(obj, str):
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return obj.encode("utf-8")
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if isinstance(obj, bytes):
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return obj
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if isinstance(obj, Image.Image):
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return obj.tobytes()
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# Convertible to NumPy arrays
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if isinstance(obj, torch.Tensor):
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obj = obj.numpy()
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if isinstance(obj, (int, float)):
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obj = np.array(obj)
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if isinstance(obj, np.ndarray):
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return obj.tobytes()
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logger.warning(
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"No serialization method found for %s. "
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"Falling back to pickle.", type(obj))
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return pickle.dumps(obj)
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@classmethod
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def item_to_bytes(
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cls,
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key: str,
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obj: object,
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) -> Iterable[tuple[bytes, bytes]]:
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# Recursive cases
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if isinstance(obj, (list, tuple)):
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for i, elem in enumerate(obj):
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yield from cls.item_to_bytes(f"{key}.{i}", elem)
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elif isinstance(obj, dict):
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for k, v in obj.items():
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yield from cls.item_to_bytes(f"{key}.{k}", v)
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else:
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key_bytes = cls.serialize_item(key)
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value_bytes = cls.serialize_item(obj)
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yield key_bytes, value_bytes
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@classmethod
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def hash_kwargs(cls, **kwargs: object) -> str:
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hasher = blake3()
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for k, v in kwargs.items():
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for k_bytes, v_bytes in cls.item_to_bytes(k, v):
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hasher.update(k_bytes)
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hasher.update(v_bytes)
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return hasher.hexdigest()
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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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if "multi_modal_data" not in prompt:
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return None
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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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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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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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return mm_hashes
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