mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-22 01:15:44 +08:00
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import pickle
|
|
import uuid
|
|
from collections.abc import Iterable
|
|
from typing import Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
from blake3 import blake3
|
|
from PIL import Image
|
|
|
|
from vllm.logger import init_logger
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class MultiModalHasher:
|
|
|
|
@classmethod
|
|
def serialize_item(cls, obj: object) -> Iterable[Union[bytes, memoryview]]:
|
|
# Simple cases
|
|
if isinstance(obj, (bytes, memoryview)):
|
|
return (obj, )
|
|
if isinstance(obj, str):
|
|
return (obj.encode("utf-8"), )
|
|
if isinstance(obj, (int, float)):
|
|
return (np.array(obj).tobytes(), )
|
|
|
|
if isinstance(obj, Image.Image):
|
|
exif = obj.getexif()
|
|
if Image.ExifTags.Base.ImageID in exif and isinstance(
|
|
exif[Image.ExifTags.Base.ImageID], uuid.UUID):
|
|
# If the image has exif ImageID tag, use that
|
|
return (exif[Image.ExifTags.Base.ImageID].bytes, )
|
|
data = {"mode": obj.mode, "data": np.asarray(obj)}
|
|
if obj.palette is not None:
|
|
data["palette"] = obj.palette.palette
|
|
if obj.palette.rawmode is not None:
|
|
data["palette_rawmode"] = obj.palette.rawmode
|
|
return cls.iter_item_to_bytes("image", data)
|
|
if isinstance(obj, torch.Tensor):
|
|
tensor_obj: torch.Tensor = obj.cpu()
|
|
tensor_dtype = tensor_obj.dtype
|
|
tensor_shape = tensor_obj.shape
|
|
|
|
# NumPy does not support bfloat16.
|
|
# Workaround: View the tensor as a contiguous 1D array of bytes
|
|
if tensor_dtype == torch.bfloat16:
|
|
tensor_obj = tensor_obj.contiguous()
|
|
tensor_obj = tensor_obj.view(
|
|
(tensor_obj.numel(), )).view(torch.uint8)
|
|
|
|
return cls.iter_item_to_bytes(
|
|
"tensor", {
|
|
"original_dtype": str(tensor_dtype),
|
|
"original_shape": tuple(tensor_shape),
|
|
"data": tensor_obj.numpy(),
|
|
})
|
|
return cls.iter_item_to_bytes("tensor", tensor_obj.numpy())
|
|
if isinstance(obj, np.ndarray):
|
|
# If the array is non-contiguous, we need to copy it first
|
|
arr_data = obj.view(
|
|
np.uint8).data if obj.flags.c_contiguous else obj.tobytes()
|
|
return cls.iter_item_to_bytes("ndarray", {
|
|
"dtype": obj.dtype.str,
|
|
"shape": obj.shape,
|
|
"data": arr_data,
|
|
})
|
|
logger.warning(
|
|
"No serialization method found for %s. "
|
|
"Falling back to pickle.", type(obj))
|
|
|
|
return (pickle.dumps(obj), )
|
|
|
|
@classmethod
|
|
def iter_item_to_bytes(
|
|
cls,
|
|
key: str,
|
|
obj: object,
|
|
) -> Iterable[Union[bytes, memoryview]]:
|
|
# Recursive cases
|
|
if isinstance(obj, (list, tuple)):
|
|
for i, elem in enumerate(obj):
|
|
yield from cls.iter_item_to_bytes(f"{key}.{i}", elem)
|
|
elif isinstance(obj, dict):
|
|
for k, v in obj.items():
|
|
yield from cls.iter_item_to_bytes(f"{key}.{k}", v)
|
|
else:
|
|
yield key.encode("utf-8")
|
|
yield from cls.serialize_item(obj)
|
|
|
|
@classmethod
|
|
def hash_kwargs(cls, **kwargs: object) -> str:
|
|
hasher = blake3()
|
|
|
|
for k, v in kwargs.items():
|
|
for bytes_ in cls.iter_item_to_bytes(k, v):
|
|
hasher.update(bytes_)
|
|
|
|
return hasher.hexdigest()
|