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[BugFix] Ray with multiple nodes #28873

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Merged
youkaichao merged 2 commits into from
Nov 19, 2025
Merged

[BugFix] Ray with multiple nodes #28873

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7e8304e
Hotfix: ray with multiple nodes
juliendenize Nov 17, 2025
2dc905d
Merge branch 'main' into hotfix_ray
njhill Nov 19, 2025
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16 changes: 8 additions & 8 deletions vllm/v1/worker/gpu_worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,14 +204,14 @@ def init_device(self):
assert self.local_rank < torch.cuda.device_count(), (
f"DP adjusted local rank {self.local_rank} is out of bounds. "
)
visible_device_count = (
torch.cuda.device_count() if torch.cuda.is_available() else 0
)
assert self.parallel_config.local_world_size <= visible_device_count, (
f"local_world_size ({self.parallel_config.local_world_size}) must be "
f"less than or equal to the number of visible devices "
f"({visible_device_count})."
)
visible_device_count = (
torch.cuda.device_count() if torch.cuda.is_available() else 0
)
assert self.parallel_config.local_world_size <= visible_device_count, (
f"local_world_size ({self.parallel_config.local_world_size}) must "
f"be less than or equal to the number of visible devices "
f"({visible_device_count})."
)
Comment on lines +207 to +214
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@gemini-code-assist gemini-code-assist bot Nov 17, 2025

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critical

This change moves the assertion for local_world_size inside the if block that handles a specific single-node data parallelism setup. While this fixes the issue for multi-node Ray where local_world_size might be miscalculated, it incorrectly disables this important sanity check for other valid configurations, such as multi-node setups without Ray or single-node setups without data parallelism.

The assertion self.parallel_config.local_world_size <= visible_device_count is a general check to ensure that the number of workers on a node does not exceed the number of available GPUs. It should not be confined to the specific data parallelism case.

A more targeted fix would be to skip this check only for Ray, or to fix the underlying issue with the calculation of local_world_size for Ray environments. Disabling this check for all other configurations could hide potential resource allocation issues and lead to crashes in other scenarios.

self.device = torch.device(f"cuda:{self.local_rank}")
current_platform.set_device(self.device)

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