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[CI] Fix "2 Node Tests (4 GPUs in total)" (#31090)
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>
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@ -1254,13 +1254,13 @@ steps:
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- # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
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- 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'
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- 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'
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- python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --nnodes=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
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- 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
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- VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
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- VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
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- # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
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- 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'
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- 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'
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- python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --nnodes=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
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- 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
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- label: Distributed Tests (2 GPUs) # 68min
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timeout_in_minutes: 90
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@ -1109,13 +1109,13 @@ steps:
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- # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
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- 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'
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- 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'
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- python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --nnodes=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
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- 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
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- VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
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- VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
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- # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
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- 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'
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- 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'
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- python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --nnodes=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
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- 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
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- label: Distributed Tests (2 GPUs) # 68min
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timeout_in_minutes: 90
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@ -171,7 +171,7 @@ steps:
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- tests/distributed/
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- tests/examples/offline_inference/data_parallel.py
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commands:
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- ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:0bec63fa317e1fbd62e19b0fc31c43c81bf89077 "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 --nnodes=2 --node-rank=0 --master-addr=192.168.10.10 --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 --nnodes=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code"
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- ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:0bec63fa317e1fbd62e19b0fc31c43c81bf89077 "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"
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- label: Distributed NixlConnector PD accuracy (4 GPUs)
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timeout_in_minutes: 30
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@ -14,19 +14,19 @@ Multi-node:
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--model="ibm-research/PowerMoE-3b" \
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-dp=2 \
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-tp=2 \
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--nnodes=2 \
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--node-rank=0 \
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--master-addr=10.99.48.128 \
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--master-port=13345
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--dp-num-nodes=2 \
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--dp-node-rank=0 \
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--dp-master-addr=10.99.48.128 \
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--dp-master-port=13345
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Node 1:
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python examples/offline_inference/data_parallel.py \
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--model="ibm-research/PowerMoE-3b" \
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-dp=2 \
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-tp=2 \
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--nnodes=2 \
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--node-rank=1 \
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--master-addr=10.99.48.128 \
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--master-port=13345
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--dp-num-nodes=2 \
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--dp-node-rank=1 \
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--dp-master-addr=10.99.48.128 \
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--dp-master-port=13345
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"""
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import os
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@ -48,7 +48,31 @@ def create_parser():
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enable_expert_parallel=True,
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)
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# Add timeout (not in EngineArgs)
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# Add DP-specific args (separate from engine args to avoid conflicts)
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parser.add_argument(
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"--dp-num-nodes",
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type=int,
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default=1,
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help="Total number of nodes for data parallel.",
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)
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parser.add_argument(
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"--dp-node-rank",
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type=int,
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default=0,
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help="Rank of the current node for data parallel.",
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)
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parser.add_argument(
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"--dp-master-addr",
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type=str,
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default="",
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help="Master node IP address for DP coordination.",
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)
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parser.add_argument(
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"--dp-master-port",
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type=int,
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default=0,
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help="Master node port for DP coordination.",
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)
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parser.add_argument(
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"--timeout",
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type=int,
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@ -132,26 +156,26 @@ if __name__ == "__main__":
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parser = create_parser()
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args = vars(parser.parse_args())
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# Extract DP-specific args
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# Extract DP-specific args (pop to remove from engine_args)
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dp_size = args.pop("data_parallel_size")
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nnodes = args.get("nnodes", 1)
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node_rank = args.get("node_rank", 0)
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master_addr = args.get("master_addr", "")
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master_port = args.get("master_port", 0)
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dp_num_nodes = args.pop("dp_num_nodes")
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dp_node_rank = args.pop("dp_node_rank")
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dp_master_addr = args.pop("dp_master_addr")
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dp_master_port = args.pop("dp_master_port")
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timeout = args.pop("timeout")
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# Remaining args are engine args
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engine_args = args
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if nnodes == 1:
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if dp_num_nodes == 1:
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dp_master_ip = "127.0.0.1"
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dp_master_port = get_open_port()
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dp_master_port_val = get_open_port()
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else:
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dp_master_ip = master_addr
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dp_master_port = master_port
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dp_master_ip = dp_master_addr
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dp_master_port_val = dp_master_port
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assert dp_size % nnodes == 0, "dp_size should be divisible by nnodes"
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dp_per_node = dp_size // nnodes
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assert dp_size % dp_num_nodes == 0, "dp_size should be divisible by dp_num_nodes"
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dp_per_node = dp_size // dp_num_nodes
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from multiprocessing import Process
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@ -162,7 +186,7 @@ if __name__ == "__main__":
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procs = []
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for local_dp_rank, global_dp_rank in enumerate(
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range(node_rank * dp_per_node, (node_rank + 1) * dp_per_node)
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range(dp_node_rank * dp_per_node, (dp_node_rank + 1) * dp_per_node)
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):
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proc = Process(
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target=main,
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@ -171,7 +195,7 @@ if __name__ == "__main__":
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local_dp_rank,
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global_dp_rank,
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dp_master_ip,
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dp_master_port,
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dp_master_port_val,
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engine_args,
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),
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)
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