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