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Add vLLM v0.18.1 source tree with KV transfer abort fix

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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>
This commit is contained in:
Gahow Wang
2026-05-22 00:30:38 +08:00
parent b6591950bc
commit 445e491123
4285 changed files with 1111303 additions and 1 deletions
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218
third_party/vllm/tests/multimodal/test_utils.py vendored Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from vllm.multimodal.inputs import (
MultiModalBatchedField,
MultiModalFieldElem,
MultiModalKwargsItem,
MultiModalSharedField,
PlaceholderRange,
)
from vllm.multimodal.utils import argsort_mm_positions, group_and_batch_mm_items
@pytest.mark.parametrize(
"case",
[
# Single modality
## Internally sorted
dict(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=3, length=2),
]
},
expected_modality_idxs=[
("image", 0),
("image", 1),
],
),
## Internally unsorted
dict(
mm_positions={
"image": [
PlaceholderRange(offset=3, length=2),
PlaceholderRange(offset=0, length=2),
]
},
expected_modality_idxs=[
("image", 1),
("image", 0),
],
),
# Two modalities
## Internally sorted
dict(
mm_positions={
"image": [
PlaceholderRange(offset=7, length=4),
PlaceholderRange(offset=11, length=5),
],
"audio": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=2, length=3),
],
},
expected_modality_idxs=[
("audio", 0),
("audio", 1),
("image", 0),
("image", 1),
],
),
## Interleaved, internally sorted
dict(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=4),
PlaceholderRange(offset=8, length=2),
],
"audio": [
PlaceholderRange(offset=5, length=2),
PlaceholderRange(offset=11, length=4),
],
},
expected_modality_idxs=[
("image", 0),
("audio", 0),
("image", 1),
("audio", 1),
],
),
## Interleaved, internally unsorted
dict(
mm_positions={
"image": [
PlaceholderRange(offset=8, length=2),
PlaceholderRange(offset=0, length=4),
],
"audio": [
PlaceholderRange(offset=11, length=4),
PlaceholderRange(offset=5, length=2),
],
},
expected_modality_idxs=[
("image", 1),
("audio", 1),
("image", 0),
("audio", 0),
],
),
# Three modalities
## Internally sorted
dict(
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),
],
},
expected_modality_idxs=[
("audio", 0),
("video", 0),
("video", 1),
("video", 2),
("image", 0),
("image", 1),
],
),
## Interleaved, internally sorted
dict(
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),
],
},
expected_modality_idxs=[
("image", 0),
("image", 1),
("audio", 0),
("video", 0),
("image", 2),
],
),
## Interleaved, internally unsorted
dict(
mm_positions={
"image": [
PlaceholderRange(offset=0, length=2),
PlaceholderRange(offset=20, length=4),
PlaceholderRange(offset=2, length=3),
],
"audio": [
PlaceholderRange(offset=5, length=2),
],
"video": [
PlaceholderRange(offset=8, length=5),
],
},
expected_modality_idxs=[
("image", 0),
("image", 2),
("audio", 0),
("video", 0),
("image", 1),
],
),
],
)
def test_argsort_mm_positions(case):
mm_positions = case["mm_positions"]
expected_modality_idxs = case["expected_modality_idxs"]
modality_idxs = argsort_mm_positions(mm_positions)
assert modality_idxs == expected_modality_idxs
def test_group_and_batch_mm_items_split_by_fieldset():
elem = MultiModalFieldElem(
data=torch.empty(1, dtype=torch.uint8),
field=MultiModalBatchedField(),
)
item1 = MultiModalKwargsItem({"x": elem, "y": elem})
item2 = MultiModalKwargsItem({"y": elem, "x": elem})
item3 = MultiModalKwargsItem({"x": elem, "y": elem, "z": elem})
item4 = MultiModalKwargsItem({"x": elem})
item5 = MultiModalKwargsItem({"x": elem, "y": elem})
res = group_and_batch_mm_items([item1, item2, item3, item4, item5])
assert [num_items for num_items, _ in res] == [2, 1, 1, 1]
def test_group_and_batch_mm_items_split_by_shared_data():
elem1 = MultiModalFieldElem(
data=torch.zeros(1, dtype=torch.uint8),
field=MultiModalSharedField(batch_size=1),
)
elem2 = MultiModalFieldElem(
data=torch.zeros(2, dtype=torch.uint8),
field=MultiModalSharedField(batch_size=1),
)
item1 = MultiModalKwargsItem({"x": elem1})
item2 = MultiModalKwargsItem({"x": elem1})
item3 = MultiModalKwargsItem({"x": elem2})
item4 = MultiModalKwargsItem({"x": elem1})
item5 = MultiModalKwargsItem({"x": elem2})
res = group_and_batch_mm_items([item1, item2, item3, item4, item5])
assert [num_items for num_items, _ in res] == [2, 1, 1, 1]