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Better error when world size is larger than node and distributed_executor_backend is not set (#30140)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor 2025-12-06 04:53:52 +00:00 committed by GitHub
parent 7e31c3a3f6
commit bf4a901af9
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1 changed files with 13 additions and 9 deletions
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22
vllm/config/parallel.py
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@ -184,13 +184,14 @@ class ParallelConfig:
distributed_executor_backend: (
str | DistributedExecutorBackend | type[Executor] | None
) = None
"""Backend to use for distributed model
workers, either "ray" or "mp" (multiprocessing). If the product
of pipeline_parallel_size and tensor_parallel_size is less than
or equal to the number of GPUs available, "mp" will be used to
keep processing on a single host. Otherwise, this will default
to "ray" if Ray is installed and fail otherwise. Note that tpu
only support Ray for distributed inference."""
"""Backend to use for distributed model workers, either "ray" or "mp"
(multiprocessing). If the product of pipeline_parallel_size and tensor_parallel_size
is less than or equal to the number of GPUs available, "mp" will be used to
keep processing on a single host. Otherwise, an error will be raised. To use "mp"
you must also set nnodes, and to use "ray" you must manually set
distributed_executor_backend to "ray".
Note that tpu only support Ray for distributed inference."""
worker_cls: str = "auto"
"""The full name of the worker class to use. If "auto", the worker class
@ -566,8 +567,11 @@ class ParallelConfig:
):
gpu_count = cuda_device_count_stateless()
raise ValueError(
f"Tensor parallel size ({self.world_size}) cannot be "
f"larger than the number of available GPUs ({gpu_count})."
f"World size ({self.world_size}) is larger than the number of "
f"available GPUs ({gpu_count}) in this node. If this is "
"intentional and you are using:\n"
"- ray, set '--distributed-executor-backend ray'.\n"
"- multiprocessing, set '--nnodes' appropriately."
)
elif self.data_parallel_backend == "ray":
logger.info(