Files
wehub-resource-sync 7ce4c8e27e
pre-commit / pre-run-check (push) Has been cancelled
pre-commit / pre-commit (push) Has been cancelled
chore: import upstream snapshot with attribution
2026-07-13 12:55:37 +08:00

205 lines
6.0 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import os
from collections.abc import Callable
from concurrent.futures import Future
from typing import Any
import pytest
from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.sampling_params import SamplingParams
from vllm.v1.engine.async_llm import AsyncLLM
from vllm.v1.engine.llm_engine import LLMEngine
from vllm.v1.executor import multiproc_executor as multiproc_executor_module
from vllm.v1.executor.abstract import Executor
from vllm.v1.executor.multiproc_executor import MultiprocExecutor
from vllm.v1.executor.uniproc_executor import (
ExecutorWithExternalLauncher,
UniProcExecutor,
)
class Mock: ...
def test_supports_async_scheduling_base_executor():
assert Executor.supports_async_scheduling() is False
def test_supports_async_scheduling_uniproc_executor():
assert UniProcExecutor.supports_async_scheduling() is True
def test_supports_async_scheduling_executor_with_external_launcher():
# ExecutorWithExternalLauncher inherits from UniProcExecutor and does not
# override supports_async_scheduling, so it should return True.
assert ExecutorWithExternalLauncher.supports_async_scheduling() is True
def test_supports_async_scheduling_multiproc_executor():
assert MultiprocExecutor.supports_async_scheduling() is True
class _FakeClock:
def __init__(self) -> None:
self.now = 0.0
def time(self) -> float:
return self.now
def sleep(self, seconds: float) -> None:
self.now += seconds
class _FakeProcess:
def __init__(self, clock: _FakeClock, exits_at: float) -> None:
self.clock = clock
self.exits_at = exits_at
self.terminate_called = False
def is_alive(self) -> bool:
return self.clock.time() < self.exits_at
def terminate(self) -> None:
self.terminate_called = True
@pytest.mark.parametrize(
("timeout", "exits_at", "expected_terminate"),
[
pytest.param(6, 5, False, id="worker-exits-before-timeout"),
pytest.param(6, 7, True, id="worker-exceeds-timeout"),
],
)
def test_multiproc_executor_worker_termination_timeout(
monkeypatch, timeout, exits_at, expected_terminate
):
monkeypatch.setenv("VLLM_WORKER_SHUTDOWN_TIMEOUT_SECONDS", str(timeout))
clock = _FakeClock()
monkeypatch.setattr(multiproc_executor_module.time, "time", clock.time)
monkeypatch.setattr(multiproc_executor_module.time, "sleep", clock.sleep)
executor = MultiprocExecutor.__new__(MultiprocExecutor)
proc = _FakeProcess(clock, exits_at=exits_at)
executor._ensure_worker_termination([proc])
assert proc.terminate_called is expected_terminate
class CustomMultiprocExecutor(MultiprocExecutor):
def collective_rpc(
self,
method: str | Callable,
timeout: float | None = None,
args: tuple = (),
kwargs: dict | None = None,
non_block: bool = False,
unique_reply_rank: int | None = None,
kv_output_aggregator: KVOutputAggregator = None,
) -> Any | list[Any] | Future[Any | list[Any]]:
# Drop marker to show that this was run
with open(".marker", "w"):
...
return super().collective_rpc(
method,
timeout,
args,
kwargs,
non_block,
unique_reply_rank,
kv_output_aggregator,
)
CustomMultiprocExecutorAsync = CustomMultiprocExecutor
MODEL = "Qwen/Qwen3-0.6B"
def test_custom_executor_type_checking():
with pytest.raises(ValueError):
engine_args = EngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=Mock,
)
LLMEngine.from_engine_args(engine_args)
with pytest.raises(ValueError):
engine_args = AsyncEngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=Mock,
)
AsyncLLM.from_engine_args(engine_args)
@pytest.mark.parametrize(
"distributed_executor_backend",
[
CustomMultiprocExecutor,
"tests.v1.executor.test_executor.CustomMultiprocExecutor",
],
)
def test_custom_executor(distributed_executor_backend, tmp_path):
cwd = os.path.abspath(".")
os.chdir(tmp_path)
try:
assert not os.path.exists(".marker")
engine_args = EngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True, # reduce test time
)
engine = LLMEngine.from_engine_args(engine_args)
sampling_params = SamplingParams(max_tokens=1)
engine.add_request("0", "foo", sampling_params)
engine.step()
assert os.path.exists(".marker")
finally:
os.chdir(cwd)
@pytest.mark.parametrize(
"distributed_executor_backend",
[
CustomMultiprocExecutorAsync,
"tests.v1.executor.test_executor.CustomMultiprocExecutorAsync",
],
)
def test_custom_executor_async(distributed_executor_backend, tmp_path):
cwd = os.path.abspath(".")
os.chdir(tmp_path)
try:
assert not os.path.exists(".marker")
engine_args = AsyncEngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True, # reduce test time
)
engine = AsyncLLM.from_engine_args(engine_args)
sampling_params = SamplingParams(max_tokens=1)
async def t():
stream = engine.generate(
request_id="0", prompt="foo", sampling_params=sampling_params
)
async for x in stream:
...
asyncio.run(t())
assert os.path.exists(".marker")
finally:
os.chdir(cwd)