mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-09 22:05:44 +08:00
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
import uuid
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
from PIL import Image, ImageDraw
|
|
|
|
from vllm.multimodal.hasher import MultiModalHasher
|
|
|
|
pytestmark = pytest.mark.cpu_test
|
|
|
|
ASSETS_DIR = Path(__file__).parent / "assets"
|
|
assert ASSETS_DIR.exists()
|
|
|
|
|
|
# NOTE: Images that are the same visually are allowed to have the same hash
|
|
@pytest.mark.parametrize("mode_pair", [("1", "L"), ("RGBA", "CMYK")])
|
|
def test_hash_collision_image_mode(mode_pair):
|
|
mode1, mode2 = mode_pair
|
|
image1 = Image.new(mode1, size=(10, 10), color=1)
|
|
image2 = Image.new(mode2, size=(10, 10), color=1)
|
|
|
|
hasher = MultiModalHasher
|
|
assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
|
|
|
|
|
|
def test_hash_collision_image_palette():
|
|
# These images differ only in Image.palette._palette
|
|
image1 = Image.open(ASSETS_DIR / "image1.png")
|
|
image2 = Image.open(ASSETS_DIR / "image2.png")
|
|
|
|
hasher = MultiModalHasher
|
|
assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
|
|
|
|
|
|
def test_hash_collision_image_transpose():
|
|
image1 = Image.new("1", size=(10, 20))
|
|
ImageDraw.Draw(image1).line([(0, 0), (10, 0)])
|
|
|
|
image2 = Image.new("1", size=(20, 10))
|
|
ImageDraw.Draw(image2).line([(0, 0), (0, 10)])
|
|
|
|
hasher = MultiModalHasher
|
|
assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
|
|
def test_hash_collision_tensor_shape(dtype):
|
|
# The hash should be different though the data is the same when flattened
|
|
arr1 = torch.zeros((5, 10, 20, 3), dtype=dtype)
|
|
arr2 = torch.zeros((10, 20, 5, 3), dtype=dtype)
|
|
|
|
hasher = MultiModalHasher
|
|
assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
|
|
|
|
|
|
def test_hash_collision_array_shape():
|
|
# The hash should be different though the data is the same when flattened
|
|
arr1 = np.zeros((5, 10, 20, 3))
|
|
arr2 = np.zeros((10, 20, 5, 3))
|
|
|
|
hasher = MultiModalHasher
|
|
assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
|
|
|
|
|
|
def test_hash_non_contiguous_array():
|
|
arr = np.arange(24).reshape(4, 6).T
|
|
assert not arr.flags.c_contiguous
|
|
|
|
arr_c = np.ascontiguousarray(arr)
|
|
assert arr_c.flags.c_contiguous
|
|
|
|
hasher = MultiModalHasher
|
|
# Both should be hashable and produce the same hashes
|
|
assert hasher.hash_kwargs(data=arr) == hasher.hash_kwargs(data=arr_c)
|
|
|
|
|
|
def test_hash_image_exif_id():
|
|
# Test that EXIF ImageId tag can be used to store UUID
|
|
# and the hasher will use that instead of the image data.
|
|
image1 = image2 = Image.new("1", size=(10, 20))
|
|
id = uuid.uuid4()
|
|
image1.getexif()[Image.ExifTags.Base.ImageID] = id
|
|
image2 = Image.open(ASSETS_DIR / "image1.png")
|
|
image2.getexif()[Image.ExifTags.Base.ImageID] = "Not a UUID"
|
|
image2a = Image.open(ASSETS_DIR / "image1.png")
|
|
|
|
hasher = MultiModalHasher
|
|
# first image has UUID in ImageID, so it should hash to that UUID
|
|
assert hasher.hash_kwargs(image=image1) == hasher.hash_kwargs(image=id.bytes)
|
|
# second image has non-UUID in ImageID, so it should hash to the image data
|
|
assert hasher.hash_kwargs(image=image2) == hasher.hash_kwargs(image=image2a)
|