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[Misc] Configurable timeout for execute_model RPC calls via env var (#19544)

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Signed-off-by: jinqinn <goodqinjin@163.com>
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jinqinn 2025-06-23 11:36:26 +08:00 committed by GitHub
parent 4a0f7888a3
commit f39ab2d4bd
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2 changed files with 12 additions and 11 deletions
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6
vllm/envs.py
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@ -130,6 +130,7 @@ if TYPE_CHECKING:
VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1 VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
VLLM_SLEEP_WHEN_IDLE: bool = False VLLM_SLEEP_WHEN_IDLE: bool = False
VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16 VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
VLLM_KV_CACHE_LAYOUT: Optional[str] = None VLLM_KV_CACHE_LAYOUT: Optional[str] = None
VLLM_COMPUTE_NANS_IN_LOGITS: bool = False VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
@ -897,6 +898,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
"VLLM_MQ_MAX_CHUNK_BYTES_MB": "VLLM_MQ_MAX_CHUNK_BYTES_MB":
lambda: int(os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")), lambda: int(os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")),
# Timeout in seconds for execute_model RPC calls in multiprocessing
# executor (only applies when TP > 1).
"VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS":
lambda: int(os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")),
# KV Cache layout used throughout vllm. # KV Cache layout used throughout vllm.
# Some common values are: # Some common values are:
# - NHD # - NHD

17
vllm/v1/executor/multiproc_executor.py
View File

@ -37,11 +37,6 @@ from vllm.worker.worker_base import WorkerWrapperBase
logger = init_logger(__name__) logger = init_logger(__name__)
POLLING_TIMEOUT_MS = 5000
POLLING_TIMEOUT_S = POLLING_TIMEOUT_MS // 1000
EXECUTE_MODEL_TIMEOUT_S = 300
class MultiprocExecutor(Executor): class MultiprocExecutor(Executor):
@ -160,12 +155,12 @@ class MultiprocExecutor(Executor):
self, self,
scheduler_output, scheduler_output,
) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]: ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
(output, ) = self.collective_rpc("execute_model", (output, ) = self.collective_rpc(
args=(scheduler_output, ), "execute_model",
unique_reply_rank=self.output_rank, args=(scheduler_output, ),
non_block=self.max_concurrent_batches unique_reply_rank=self.output_rank,
> 1, non_block=self.max_concurrent_batches > 1,
timeout=EXECUTE_MODEL_TIMEOUT_S) timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS)
return output return output
def collective_rpc(self, def collective_rpc(self,