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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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151
third_party/vllm/tests/entrypoints/openai/test_vision_embeds.py vendored Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import base64
import numpy as np
import pytest
import requests
import torch
from vllm.utils.serial_utils import tensor2base64
from ...utils import RemoteOpenAIServer
@pytest.mark.parametrize(
"model_name", ["ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]
)
def test_single_content(model_name: str):
args = [
"--runner",
"pooling",
# use half precision for speed and memory savings in CI environment
"--dtype",
"float16",
"--enforce-eager",
"--trust-remote-code",
"--max-num-seqs",
"32",
"--model-impl",
"terratorch",
"--skip-tokenizer-init",
"--enable-mm-embeds",
]
with RemoteOpenAIServer(model_name, args) as server:
response = requests.post(
server.url_for("pooling"),
json={
"model": model_name,
"messages": [
{
"role": "user",
"content": [
{
"type": "image_embeds",
"image_embeds": {
"pixel_values": tensor2base64(
torch.ones((6, 512, 512), dtype=torch.float16)
),
"location_coords": tensor2base64(
torch.ones((1, 2), dtype=torch.float16)
),
},
},
],
}
],
"encoding_format": "base64",
},
)
response.raise_for_status()
output = response.json()["data"][0]["data"]
np_response = np.frombuffer(base64.b64decode(output), dtype=np.float32)
assert len(np_response) == 524288
@pytest.mark.parametrize("model_name", ["Qwen/Qwen3-VL-2B-Instruct"])
def test_multi_content(model_name: str):
args = [
"--enforce-eager",
"--max-num-seqs",
"32",
"--max-model-len",
"8192",
"--enable-mm-embeds",
]
with RemoteOpenAIServer(model_name, args) as server:
client = server.get_client()
# Image only
chat_completion = client.chat.completions.create(
model=model_name,
messages=[
{
"role": "user",
"content": [
{
"type": "image_embeds",
"image_embeds": {
"image_embeds": tensor2base64(torch.zeros(220, 8192)),
"image_grid_thw": tensor2base64(
torch.tensor([1, 22, 40])
),
},
},
{
"type": "image_embeds",
"image_embeds": {
"image_embeds": tensor2base64(torch.zeros(220, 8192)),
"image_grid_thw": tensor2base64(
torch.tensor([1, 22, 40])
),
},
},
],
}
],
max_tokens=5,
)
assert chat_completion.id is not None
assert len(chat_completion.choices) == 1
# Interleaved text and image
chat_completion = client.chat.completions.create(
model=model_name,
messages=[
{
"role": "user",
"content": [
{
"type": "image_embeds",
"image_embeds": {
"image_embeds": tensor2base64(torch.zeros(220, 8192)),
"image_grid_thw": tensor2base64(
torch.tensor([1, 22, 40])
),
},
},
{"type": "text", "text": "OCR:"},
{
"type": "image_embeds",
"image_embeds": {
"image_embeds": tensor2base64(torch.zeros(220, 8192)),
"image_grid_thw": tensor2base64(
torch.tensor([1, 22, 40])
),
},
},
],
}
],
max_tokens=5,
)
assert chat_completion.id is not None
assert len(chat_completion.choices) == 1