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vllm/tests/multimodal/test_utils.py
Russell Bryant e489ad7a21
[Misc] Add SPDX-License-Identifier headers to python source files (#12628) 
- **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>
2025-02-02 11:58:18 -08:00

403 lines
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# SPDX-License-Identifier: Apache-2.0
import base64
import mimetypes
import os
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import TYPE_CHECKING, Dict, NamedTuple, Optional, Tuple
import numpy as np
import pytest
from PIL import Image, ImageChops
from transformers import AutoConfig, AutoTokenizer
from vllm.multimodal.inputs import PlaceholderRange
from vllm.multimodal.utils import (MediaConnector,
merge_and_sort_multimodal_metadata,
repeat_and_pad_placeholder_tokens)
if TYPE_CHECKING:
from vllm.multimodal.hasher import MultiModalHashDict
from vllm.multimodal.inputs import MultiModalPlaceholderDict
# Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA)
TEST_IMAGE_URLS = [
"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
"https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png",
"https://upload.wikimedia.org/wikipedia/commons/thumb/9/91/Venn_diagram_rgb.svg/1280px-Venn_diagram_rgb.svg.png",
"https://upload.wikimedia.org/wikipedia/commons/0/0b/RGBA_comp.png",
]
@pytest.fixture(scope="module")
def url_images() -> Dict[str, Image.Image]:
connector = MediaConnector()
return {
image_url: connector.fetch_image(image_url)
for image_url in TEST_IMAGE_URLS
}
def get_supported_suffixes() -> Tuple[str, ...]:
# We should at least test the file types mentioned in GPT-4 with Vision
OPENAI_SUPPORTED_SUFFIXES = ('.png', '.jpeg', '.jpg', '.webp', '.gif')
# Additional file types that are supported by us
EXTRA_SUPPORTED_SUFFIXES = ('.bmp', '.tiff')
return OPENAI_SUPPORTED_SUFFIXES + EXTRA_SUPPORTED_SUFFIXES
def _image_equals(a: Image.Image, b: Image.Image) -> bool:
return (np.asarray(a) == np.asarray(b.convert(a.mode))).all()
@pytest.mark.asyncio
@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
async def test_fetch_image_http(image_url: str):
connector = MediaConnector()
image_sync = connector.fetch_image(image_url)
image_async = await connector.fetch_image_async(image_url)
assert _image_equals(image_sync, image_async)
@pytest.mark.asyncio
@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
@pytest.mark.parametrize("suffix", get_supported_suffixes())
async def test_fetch_image_base64(url_images: Dict[str, Image.Image],
image_url: str, suffix: str):
connector = MediaConnector()
url_image = url_images[image_url]
try:
mime_type = Image.MIME[Image.registered_extensions()[suffix]]
except KeyError:
try:
mime_type = mimetypes.types_map[suffix]
except KeyError:
pytest.skip('No MIME type')
with NamedTemporaryFile(suffix=suffix) as f:
try:
url_image.save(f.name)
except Exception as e:
if e.args[0] == 'cannot write mode RGBA as JPEG':
pytest.skip('Conversion not supported')
raise
base64_image = base64.b64encode(f.read()).decode("utf-8")
data_url = f"data:{mime_type};base64,{base64_image}"
data_image_sync = connector.fetch_image(data_url)
if _image_equals(url_image, Image.open(f)):
assert _image_equals(url_image, data_image_sync)
else:
pass # Lossy format; only check that image can be opened
data_image_async = await connector.fetch_image_async(data_url)
assert _image_equals(data_image_sync, data_image_async)
@pytest.mark.asyncio
@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
async def test_fetch_image_local_files(image_url: str):
connector = MediaConnector()
with TemporaryDirectory() as temp_dir:
local_connector = MediaConnector(allowed_local_media_path=temp_dir)
origin_image = connector.fetch_image(image_url)
origin_image.save(os.path.join(temp_dir, os.path.basename(image_url)),
quality=100,
icc_profile=origin_image.info.get('icc_profile'))
image_async = await local_connector.fetch_image_async(
f"file://{temp_dir}/{os.path.basename(image_url)}")
image_sync = local_connector.fetch_image(
f"file://{temp_dir}/{os.path.basename(image_url)}")
# Check that the images are equal
assert not ImageChops.difference(image_sync, image_async).getbbox()
with pytest.raises(ValueError, match="must be a subpath"):
await local_connector.fetch_image_async(
f"file://{temp_dir}/../{os.path.basename(image_url)}")
with pytest.raises(RuntimeError, match="Cannot load local files"):
await connector.fetch_image_async(
f"file://{temp_dir}/../{os.path.basename(image_url)}")
with pytest.raises(ValueError, match="must be a subpath"):
local_connector.fetch_image(
f"file://{temp_dir}/../{os.path.basename(image_url)}")
with pytest.raises(RuntimeError, match="Cannot load local files"):
connector.fetch_image(
f"file://{temp_dir}/../{os.path.basename(image_url)}")
@pytest.mark.parametrize("model", ["llava-hf/llava-v1.6-mistral-7b-hf"])
def test_repeat_and_pad_placeholder_tokens(model):
config = AutoConfig.from_pretrained(model)
image_token_id = config.image_token_index
tokenizer = AutoTokenizer.from_pretrained(model)
test_cases = [
(
"<image>",
2,
"<image><image>",
[32000, 32000],
[{ "offset": 0, "length": 2 }],
),
(
"<image><image>",
2,
"<image><image><image>",
[32000, 32000, 32000],
[{ "offset": 0, "length": 2 }],
),
(
"<image><image>",
[3, 2],
"<image><image><image><image><image>",
[32000, 32000, 32000, 32000, 32000],
[{ "offset": 0, "length": 3 }, { "offset": 3, "length": 2 }],
),
(
"Image:<image>Image:<image>!",
[3, 2],
"Image:<image><image><image>Image:<image><image>!",
[9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
[{ "offset": 2, "length": 3 }, { "offset": 7, "length": 2 }],
),
(
"<image>",
[3, 2],
"<image><image><image>",
[32000, 32000, 32000],
[{ "offset": 0, "length": 3 }],
),
] # yapf: disable
for (
prompt,
repeat_count,
expected_prompt,
expected_token_ids,
expected_ranges,
) in test_cases:
new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
tokenizer=tokenizer,
prompt=prompt,
prompt_token_ids=tokenizer.encode(prompt,
add_special_tokens=False),
placeholder_token_id=image_token_id,
repeat_count=repeat_count,
)
assert new_prompt == expected_prompt
assert new_token_ids == expected_token_ids
assert ranges == expected_ranges
# Used for the next two tests related to `merge_and_sort_multimodal_metadata`.
class TestCase(NamedTuple):
mm_positions: "MultiModalPlaceholderDict"
mm_hashes: Optional["MultiModalHashDict"]
expected_modalities: list[str]
expected_ranges: list[PlaceholderRange]
expected_hashes: Optional[list[str]]
def test_merge_and_sort_multimodal_metadata():
test_cases = [
# Single modality should return result as is but flattened
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=3, length=2),
]
},
mm_hashes={"image": ["hash1", "hash2"]},
expected_modalities=["image"],
expected_ranges=[
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=3, length=2),
],
expected_hashes=["hash1", "hash2"],
),
# Single modality without hashes return None for mm hash.
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=2),
]
},
mm_hashes=None,
expected_modalities=["image"],
expected_ranges=[
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=2),
],
expected_hashes=None,
),
# Multiple modalities with hashes should return sorted modalities
# and flattened ranges and hashes.
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=7, length=4),
PlaceholderRange(offset=11, length=5),
],
"audio": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=3),
]
},
mm_hashes={
"image": ["image_hash1", "image_hash2"],
"audio": ["audio_hash1", "audio_hash2"],
},
expected_modalities=["audio", "image"],
expected_ranges=[
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=3),
PlaceholderRange(offset=7, length=4),
PlaceholderRange(offset=11, length=5),
],
expected_hashes=[
"audio_hash1", "audio_hash2", "image_hash1", "image_hash2"
],
),
# Multiple modalities without hashes should return sorted modalities
# and flattened ranges and None.
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=7, length=4),
PlaceholderRange(offset=11, length=5),
],
"audio": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=3),
]
},
mm_hashes=None,
expected_modalities=["audio", "image"],
expected_ranges=[
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=3),
PlaceholderRange(offset=7, length=4),
PlaceholderRange(offset=11, length=5),
],
expected_hashes=None,
),
# Three modalities
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=15, length=7),
PlaceholderRange(offset=22, length=8),
],
"audio": [
PlaceholderRange(offset=0, length=2),
],
"video": [
PlaceholderRange(offset=3, length=4),
PlaceholderRange(offset=7, length=5),
PlaceholderRange(offset=12, length=6),
]
},
mm_hashes={
"image": ["image_hash1", "image_hash2"],
"audio": ["audio_hash1"],
"video": ["video_hash1", "video_hash2", "video_hash3"]
},
expected_modalities=["audio", "video", "image"],
expected_ranges=[
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=3, length=4),
PlaceholderRange(offset=7, length=5),
PlaceholderRange(offset=12, length=6),
PlaceholderRange(offset=15, length=7),
PlaceholderRange(offset=22, length=8),
],
expected_hashes=[
"audio_hash1", "video_hash1", "video_hash2", "video_hash3",
"image_hash1", "image_hash2"
],
),
]
for (mm_positions, mm_hashes, expected_modalities, expected_ranges,
expected_hashes) in test_cases:
modalities, ranges, hashes = merge_and_sort_multimodal_metadata(
mm_positions, mm_hashes)
assert modalities == expected_modalities
assert ranges == expected_ranges
assert hashes == expected_hashes
def test_merge_and_sort_multimodal_metadata_with_interleaving():
test_cases = [
# <image> <audio> <image> <audio>
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=4),
PlaceholderRange(offset=8, length=2),
],
"audio": [
PlaceholderRange(offset=5, length=2),
PlaceholderRange(offset=11, length=4),
]
},
mm_hashes={
"image": ["image_hash1", "image_hash2"],
"audio": ["audio_hash1", "audio_hash2"],
},
expected_modalities=[],
expected_ranges=[],
expected_hashes=None,
),
# <image> <image> <video> <audio> <image>
TestCase(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=3),
PlaceholderRange(offset=20, length=4),
],
"audio": [
PlaceholderRange(offset=5, length=2),
],
"video": [
PlaceholderRange(offset=8, length=5),
]
},
mm_hashes=None,
expected_modalities=[],
expected_ranges=[],
expected_hashes=None,
),
]
for case in test_cases:
with pytest.raises(ValueError) as ex_info:
merge_and_sort_multimodal_metadata(case.mm_positions,
case.mm_hashes)
assert "Interleaved mixed-modality" in str(ex_info.value)
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