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[V1] Unify VLLM_ENABLE_V1_MULTIPROCESSING handling in RayExecutor #11472

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Dec 25, 2024
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[V1] Unify VLLM_ENABLE_V1_MULTIPROCESSING handling in RayExecutor #11472

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5 changes: 0 additions & 5 deletions tests/basic_correctness/test_basic_correctness.py
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Expand Up @@ -127,11 +127,6 @@ def test_models_distributed(
if attention_backend:
os.environ["VLLM_ATTENTION_BACKEND"] = attention_backend

# Import VLLM_USE_V1 dynamically to handle patching
from vllm.envs import VLLM_USE_V1
if VLLM_USE_V1 and distributed_executor_backend != "mp":
os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"

dtype = "half"
max_tokens = 5

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2 changes: 0 additions & 2 deletions vllm/v1/engine/llm_engine.py
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Expand Up @@ -21,7 +21,6 @@
from vllm.v1.engine.detokenizer import Detokenizer
from vllm.v1.engine.processor import Processor
from vllm.v1.executor.abstract import Executor
from vllm.v1.executor.ray_utils import initialize_ray_cluster

logger = init_logger(__name__)

Expand Down Expand Up @@ -112,7 +111,6 @@ def _get_executor_cls(cls, vllm_config: VllmConfig) -> Type[Executor]:
distributed_executor_backend = (
vllm_config.parallel_config.distributed_executor_backend)
if distributed_executor_backend == "ray":
initialize_ray_cluster(vllm_config.parallel_config)
from vllm.v1.executor.ray_executor import RayExecutor
executor_class = RayExecutor
elif distributed_executor_backend == "mp":
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5 changes: 4 additions & 1 deletion vllm/v1/executor/ray_executor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,8 @@
from vllm.logger import init_logger
from vllm.utils import get_distributed_init_method, get_ip, get_open_port
from vllm.v1.executor.abstract import Executor
from vllm.v1.executor.ray_utils import RayWorkerWrapper, ray
from vllm.v1.executor.ray_utils import (RayWorkerWrapper,
initialize_ray_cluster, ray)
from vllm.v1.outputs import ModelRunnerOutput

if ray is not None:
Expand All @@ -33,7 +34,9 @@ def __init__(self, vllm_config: VllmConfig) -> None:
if ray_usage != "1":
os.environ["RAY_USAGE_STATS_ENABLED"] = "0"

initialize_ray_cluster(self.parallel_config)
placement_group = self.parallel_config.placement_group

# Create the parallel GPU workers.
self._init_workers_ray(placement_group)

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