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[V1] Extend beyond image modality and support mixed-modality inference with Llava-OneVision (#11685)

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Signed-off-by: Roger Wang <ywang@roblox.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Roger Wang
2025-01-06 11:58:16 -08:00
committed by GitHub
parent e20c92bb61
commit 91b361ae89
17 changed files with 633 additions and 279 deletions
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209
tests/multimodal/test_utils.py
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@@ -2,16 +2,22 @@ import base64
import mimetypes
import os
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import Dict, Tuple
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",
@@ -191,3 +197,204 @@ def test_repeat_and_pad_placeholder_tokens(model):
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)