65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
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"""Make sure ray assigns GPU workers to the correct node.
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Run:
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```sh
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cd $VLLM_PATH/tests
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pytest distributed/test_multi_node_assignment.py
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```
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"""
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import os
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import pytest
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import ray
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from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
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from vllm import initialize_ray_cluster
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from vllm.config import ParallelConfig
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from vllm.executor.ray_utils import _wait_until_pg_removed
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from vllm.utils import get_ip
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VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
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@pytest.mark.skipif(not VLLM_MULTI_NODE,
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reason="Need at least 2 nodes to run the test.")
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def test_multi_node_assignment() -> None:
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# NOTE: important to keep this class definition here
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# to let ray use cloudpickle to serialize it.
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class Actor:
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def get_ip(self):
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return get_ip()
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for _ in range(10):
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config = ParallelConfig(1, 2)
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initialize_ray_cluster(config)
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current_ip = get_ip()
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workers = []
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for bundle_id, bundle in enumerate(
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config.placement_group.bundle_specs):
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if not bundle.get("GPU", 0):
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continue
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scheduling_strategy = PlacementGroupSchedulingStrategy(
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placement_group=config.placement_group,
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placement_group_capture_child_tasks=True,
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placement_group_bundle_index=bundle_id,
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)
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worker = ray.remote(
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num_cpus=0,
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num_gpus=1,
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scheduling_strategy=scheduling_strategy,
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)(Actor).remote()
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worker_ip = ray.get(worker.get_ip.remote())
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assert worker_ip == current_ip
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workers.append(worker)
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for worker in workers:
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ray.kill(worker)
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_wait_until_pg_removed(config.placement_group)
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