Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[misc][distributed] use localhost for single-node #5619

Merged
merged 7 commits into from
Jun 19, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 5 additions & 2 deletions vllm/executor/multiproc_gpu_executor.py
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
from vllm.logger import init_logger
from vllm.sequence import ExecuteModelRequest, SamplerOutput
from vllm.utils import (cuda_device_count_stateless,
get_distributed_init_method, get_ip, get_open_port,
get_distributed_init_method, get_open_port,
get_vllm_instance_id, make_async)

logger = init_logger(__name__)
Expand All @@ -37,8 +37,11 @@ def _init_executor(self) -> None:
assert world_size <= cuda_device_count_stateless(), (
"please set tensor_parallel_size to less than max local gpu count")

# Multiprocessing-based executor does not support multi-node setting.
# Since it only works for single node, we can use the loopback address
# 127.0.0.1 for communication.
distributed_init_method = get_distributed_init_method(
get_ip(), get_open_port())
"127.0.0.1", get_open_port())

if world_size == 1:
self.workers = []
Expand Down
10 changes: 10 additions & 0 deletions vllm/executor/ray_gpu_executor.py
Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,16 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
self._run_workers("update_environment_variables",
all_args=all_args_to_update_environment_variables)

if len(node_gpus) == 1:
# in single node case, we don't need to get the IP address.
# the loopback address is sufficient
# NOTE: a node may have several IP addresses, one for each
# network interface. `get_ip()` might return any of them,
# while they might not work for communication inside the node
# if the network setup is complicated. Using the loopback address
# solves this issue, as it always works for communication inside
# the node.
driver_ip = "127.0.0.1"
distributed_init_method = get_distributed_init_method(
driver_ip, get_open_port())

Expand Down
Loading