Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import uuid
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from pathlib import Path
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import numpy as np
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import pytest
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import torch
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from PIL import Image, ImageDraw
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from vllm.multimodal.hasher import MultiModalHasher
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pytestmark = pytest.mark.cpu_test
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ASSETS_DIR = Path(__file__).parent / "assets"
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assert ASSETS_DIR.exists()
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def test_hash_single_item_different_shape():
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x1 = torch.zeros(())
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x2 = torch.zeros((1,))
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(x=x1) != hasher.hash_kwargs(x=x2)
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def test_hash_key_order_invariant():
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x = torch.zeros((5, 10))
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y = torch.ones((5, 10))
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(x=x, y=y) == hasher.hash_kwargs(y=y, x=x)
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# NOTE: Images that are the same visually are allowed to have the same hash
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@pytest.mark.parametrize("mode_pair", [("1", "L"), ("RGBA", "CMYK")])
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def test_hash_collision_image_mode(mode_pair):
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mode1, mode2 = mode_pair
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image1 = Image.new(mode1, size=(10, 10), color=1)
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image2 = Image.new(mode2, size=(10, 10), color=1)
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
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def test_hash_collision_image_palette():
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# These images differ only in Image.palette._palette
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image1 = Image.open(ASSETS_DIR / "image1.png")
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image2 = Image.open(ASSETS_DIR / "image2.png")
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
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def test_hash_collision_image_transpose():
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image1 = Image.new("1", size=(10, 20))
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ImageDraw.Draw(image1).line([(0, 0), (10, 0)])
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image2 = Image.new("1", size=(20, 10))
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ImageDraw.Draw(image2).line([(0, 0), (0, 10)])
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(image=image1) != hasher.hash_kwargs(image=image2)
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@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
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def test_hash_collision_tensor_shape(dtype):
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# The hash should be different though the data is the same when flattened
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arr1 = torch.zeros((5, 10, 20, 3), dtype=dtype)
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arr2 = torch.zeros((10, 20, 5, 3), dtype=dtype)
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
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def test_hash_collision_array_shape():
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# The hash should be different though the data is the same when flattened
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arr1 = np.zeros((5, 10, 20, 3))
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arr2 = np.zeros((10, 20, 5, 3))
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hasher = MultiModalHasher
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assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
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def test_hash_non_contiguous_array():
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arr = np.arange(24).reshape(4, 6).T
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assert not arr.flags.c_contiguous
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arr_c = np.ascontiguousarray(arr)
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assert arr_c.flags.c_contiguous
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hasher = MultiModalHasher
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# Both should be hashable and produce the same hashes
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assert hasher.hash_kwargs(data=arr) == hasher.hash_kwargs(data=arr_c)
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def test_hash_image_exif_id():
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# Test that EXIF ImageId tag can be used to store UUID
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# and the hasher will use that instead of the image data.
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image1 = image2 = Image.new("1", size=(10, 20))
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id = uuid.uuid4()
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image1.getexif()[Image.ExifTags.Base.ImageID] = id
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image2 = Image.open(ASSETS_DIR / "image1.png")
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image2.getexif()[Image.ExifTags.Base.ImageID] = "Not a UUID"
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image2a = Image.open(ASSETS_DIR / "image1.png")
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hasher = MultiModalHasher
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# first image has UUID in ImageID, so it should hash to that UUID
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assert hasher.hash_kwargs(image=image1) == hasher.hash_kwargs(image=id.bytes)
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# second image has non-UUID in ImageID, so it should hash to the image data
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assert hasher.hash_kwargs(image=image2) == hasher.hash_kwargs(image=image2a)
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