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[Bugfix] Fix custom all reduce nvlink check on multi node #4903

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[Bugfix] Fix custom all reduce nvlink check on multi node #4903

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fix
esmeetu May 19, 2024
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esmeetu May 19, 2024
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fix check
esmeetu May 19, 2024
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esmeetu May 19, 2024
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13 changes: 1 addition & 12 deletions vllm/distributed/device_communicators/custom_all_reduce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,21 +138,10 @@ def __init__(self,
else:
device_ids = list(range(torch.cuda.device_count()))

physical_device_id = device_ids[device.index]
tensor = torch.tensor([physical_device_id],
dtype=torch.int,
device="cpu")
gather_list = [
torch.tensor([0], dtype=torch.int, device="cpu")
for _ in range(world_size)
]
dist.all_gather(gather_list, tensor, group=self.group)
physical_device_ids = [t.item() for t in gather_list]

# test nvlink first, this will filter out most of the cases
# where custom allreduce is not supported
# this checks hardware and driver support for NVLink
full_nvlink = _is_full_nvlink(physical_device_ids)
full_nvlink = _is_full_nvlink(device_ids)

@youkaichao youkaichao May 19, 2024

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This is not true. Sometimes, e.g. in the testing code, we manually remove cuda visible devices variables, and the process can see all device ids. However, we only need to check nvlink topology within several gpus, rather than all.

if world_size > 2 and not full_nvlink:
logger.warning(
"Custom allreduce is disabled because it's not supported on"
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