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Remove MQ multi-node tests (#38934)

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Signed-off-by: Jeffrey Wang <jeffreywang@anyscale.com>
This commit is contained in:
Jeffrey Wang
2026-04-03 13:00:08 -07:00
committed by GitHub
parent 5f1de2b14b
commit ab79863e6c
2 changed files with 0 additions and 133 deletions
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14
.buildkite/test_areas/distributed.yaml
View File

@@ -224,20 +224,6 @@ steps:
commands:
- ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
- label: MessageQueue TCP Multi-Node (2 GPUs)
timeout_in_minutes: 10
working_dir: "/vllm-workspace/tests"
num_devices: 1
num_nodes: 2
no_plugin: true
optional: true
source_file_dependencies:
- vllm/distributed/device_communicators/shm_broadcast.py
- vllm/distributed/parallel_state.py
- tests/distributed/test_mq_tcp_multinode.py
commands:
- ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 1 $IMAGE_TAG "torchrun --nnodes 2 --nproc-per-node=1 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_mq_tcp_multinode.py" "torchrun --nnodes 2 --nproc-per-node=1 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_mq_tcp_multinode.py"
- label: Distributed NixlConnector PD accuracy (4 GPUs)
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"

119
tests/distributed/test_mq_tcp_multinode.py
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@@ -1,119 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Multi-node integration test for MessageQueue TCP fallback.
Verifies that when writer and readers span separate nodes (Docker containers
with isolated /dev/shm), `create_from_process_group` correctly detects
cross-node ranks via `in_the_same_node_as()` and falls back to ZMQ TCP
transport — and that data actually arrives.
"""
import numpy as np
import torch.distributed as dist
from vllm.distributed.device_communicators.shm_broadcast import MessageQueue
from vllm.distributed.parallel_state import in_the_same_node_as
def main():
dist.init_process_group(backend="gloo")
rank = dist.get_rank()
world_size = dist.get_world_size()
assert world_size >= 2, (
f"Need at least 2 ranks across nodes, got world_size={world_size}"
)
# Verify that in_the_same_node_as detects cross-node correctly
status = in_the_same_node_as(dist.group.WORLD, source_rank=0)
local_count = sum(status)
print(
f"[Rank {rank}] in_the_same_node_as(source=0): {status} "
f"(local={local_count}/{world_size})"
)
# With 2 Docker containers (1 proc each), rank 0 and rank 1
# should be on different nodes.
assert local_count < world_size, (
f"Expected cross-node ranks but all {world_size} ranks appear local."
)
# Create MessageQueue
writer_rank = 0
mq = MessageQueue.create_from_process_group(
dist.group.WORLD,
max_chunk_bytes=1024 * 1024, # 1 MiB
max_chunks=10,
writer_rank=writer_rank,
)
# Verify the transport path selection
if rank == writer_rank:
print(
f"[Rank {rank}] Writer: n_local_reader={mq.n_local_reader}, "
f"n_remote_reader={mq.n_remote_reader}"
)
assert mq.n_remote_reader > 0, (
"Writer should have at least 1 remote (TCP) reader in a multi-node setup."
)
else:
if status[rank]:
assert mq._is_local_reader, (
f"Rank {rank} is on the same node as writer but is not a local reader."
)
print(f"[Rank {rank}] Reader: local (shared memory)")
else:
assert mq._is_remote_reader, (
f"Rank {rank} is on a different node but is not a remote (TCP) reader."
)
print(f"[Rank {rank}] Reader: remote (TCP)")
# Test data transfer: simple objects
dist.barrier()
if rank == writer_rank:
mq.enqueue("hello_from_node0")
else:
msg = mq.dequeue(timeout=10)
assert msg == "hello_from_node0"
dist.barrier()
print(f"[Rank {rank}] Simple object test passed")
# Test data transfer: numpy arrays
np.random.seed(42)
arrays = [
np.random.randint(0, 100, size=np.random.randint(100, 5000)) for _ in range(100)
]
dist.barrier()
if rank == writer_rank:
for arr in arrays:
mq.enqueue(arr)
else:
for i, expected in enumerate(arrays):
received = mq.dequeue(timeout=10)
assert np.array_equal(expected, received), (
f"Array mismatch at index {i}: "
f"expected shape {expected.shape}, got shape {received.shape}"
)
dist.barrier()
print(f"[Rank {rank}] Numpy array test passed")
# Test data transfer: large payload (> max_chunk_bytes)
dist.barrier()
big_array = np.zeros(200_000, dtype=np.int64) # ~1.6 MiB > 1 MiB chunk
if rank == writer_rank:
mq.enqueue(big_array)
else:
received = mq.dequeue(timeout=10)
assert np.array_equal(big_array, received)
dist.barrier()
print(f"[Rank {rank}] Large payload test passed")
# Done -- cleanup
dist.barrier()
print(f"[Rank {rank}] All MessageQueue TCP multi-node tests passed!")
dist.destroy_process_group()
if __name__ == "__main__":
main()