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Unverified Commit ae0770fa authored by Lucas Wilkinson's avatar Lucas Wilkinson Committed by GitHub
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[CI] Fix H200 Distributed test (#31054) 


Signed-off-by: default avatarLucas Wilkinson <lwilkins@redhat.com>
parent ee52d990
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.buildkite/test-amd.yaml
View file @ ae0770fa
......@@ -1254,13 +1254,13 @@ steps:
- # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
- 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-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
- 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
- # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
- 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-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
- 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
- label: Distributed Tests (2 GPUs) # 68min
timeout_in_minutes: 90
......@@ -1508,7 +1508,7 @@ steps:
- "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
- VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
- pytest -v -s tests/distributed/test_context_parallel.py
- HIP_VISIBLE_DEVICES=0,1 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1 --dp-size=2 --max-model-len 2048 --all2all-backend deepep_high_throughput
- HIP_VISIBLE_DEVICES=0,1 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
- pytest -v -s tests/v1/distributed/test_dbo.py
##### B200 test #####
......
.buildkite/test-pipeline.yaml
View file @ ae0770fa
......@@ -1109,13 +1109,13 @@ steps:
- # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
- 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-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
- 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
- # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
- 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-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
- 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
- label: Distributed Tests (2 GPUs) # 68min
timeout_in_minutes: 90
......@@ -1334,7 +1334,7 @@ steps:
- "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
- VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
- pytest -v -s tests/distributed/test_context_parallel.py
- CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1 --dp-size=2 --max-model-len 2048 --all2all-backend deepep_high_throughput
- CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
- pytest -v -s tests/v1/distributed/test_dbo.py
##### B200 test #####
......
.buildkite/test_areas/distributed.yaml
View file @ ae0770fa
......@@ -145,7 +145,7 @@ steps:
- VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'
- VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
- pytest -v -s tests/distributed/test_context_parallel.py
- CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1 --dp-size=2 --max-model-len 2048 --all2all-backend deepep_high_throughput
- CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
- pytest -v -s tests/v1/distributed/test_dbo.py
- label: Distributed Tests (2 GPUs)(B200)
......@@ -171,7 +171,7 @@ steps:
- tests/distributed/
- tests/examples/offline_inference/data_parallel.py
commands:
- ./.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-size=2 --tp-size=1 --node-size=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-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code"
- ./.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"
- label: Distributed NixlConnector PD accuracy (4 GPUs)
timeout_in_minutes: 30
......
examples/offline_inference/data_parallel.py
View file @ ae0770fa
......@@ -5,25 +5,25 @@ Usage:
Single node:
python examples/offline_inference/data_parallel.py \
--model="ibm-research/PowerMoE-3b" \
--dp-size=2 \
--tp-size=2
-dp=2 \
-tp=2
Multi-node:
Node 0 (assume the node has ip of 10.99.48.128):
python examples/offline_inference/data_parallel.py \
--model="ibm-research/PowerMoE-3b" \
--dp-size=2 \
--tp-size=2 \
--node-size=2 \
-dp=2 \
-tp=2 \
--nnodes=2 \
--node-rank=0 \
--master-addr=10.99.48.128 \
--master-port=13345
Node 1:
python examples/offline_inference/data_parallel.py \
--model="ibm-research/PowerMoE-3b" \
--dp-size=2 \
--tp-size=2 \
--node-size=2 \
-dp=2 \
-tp=2 \
--nnodes=2 \
--node-rank=1 \
--master-addr=10.99.48.128 \
--master-port=13345
......@@ -32,103 +32,40 @@ Multi-node:
import os
from time import sleep
from vllm import LLM, SamplingParams
from vllm import LLM, EngineArgs, SamplingParams
from vllm.platforms import current_platform
from vllm.utils.argparse_utils import FlexibleArgumentParser
from vllm.utils.network_utils import get_open_port
def parse_args():
import argparse
def create_parser():
parser = FlexibleArgumentParser(description="Data Parallel Inference")
parser = argparse.ArgumentParser(description="Data Parallel Inference")
parser.add_argument(
"--model",
type=str,
default="ibm-research/PowerMoE-3b",
help="Model name or path",
)
parser.add_argument("--dp-size", type=int, default=2, help="Data parallel size")
parser.add_argument("--tp-size", type=int, default=2, help="Tensor parallel size")
parser.add_argument(
"--node-size", type=int, default=1, help="Total number of nodes"
)
parser.add_argument(
"--node-rank", type=int, default=0, help="Rank of the current node"
)
parser.add_argument(
"--master-addr", type=str, default="", help="Master node IP address"
)
parser.add_argument("--master-port", type=int, default=0, help="Master node port")
parser.add_argument(
"--enforce-eager", action="store_true", help="Enforce eager mode execution."
)
parser.add_argument(
"--trust-remote-code", action="store_true", help="Trust remote code."
)
parser.add_argument(
"--max-num-seqs",
type=int,
default=64,
help=("Maximum number of sequences to be processed in a single iteration."),
)
parser.add_argument(
"--max-model-len",
type=int,
help=("Maximum number of tokens to be processed in a single iteration."),
# Add all engine args
EngineArgs.add_cli_args(parser)
parser.set_defaults(
model="ibm-research/PowerMoE-3b",
enable_expert_parallel=True,
)
# Add timeout (not in EngineArgs)
parser.add_argument(
"--timeout",
type=int,
default=300,
help=("Number of seconds before unresponsive process is killed."),
)
parser.add_argument(
"--gpu-memory-utilization",
type=float,
default=0.8,
help=("Fraction of GPU memory vLLM is allowed to allocate (0.0, 1.0]."),
)
parser.add_argument(
"--enable-dbo",
action="store_true",
help=("Enable microbatched execution"),
)
parser.add_argument(
"--compilation-config",
type=int,
help=("Compilation optimization (O) mode 0-3."),
)
parser.add_argument(
"--quantization",
type=str,
)
parser.add_argument(
"--disable-expert-parallel",
dest="enable_expert_parallel",
action="store_false",
help="Disable expert parallel (default: enabled).",
help="Number of seconds before unresponsive process is killed.",
)
parser.set_defaults(enable_expert_parallel=True)
return parser.parse_args()
return parser
def main(
model,
dp_size,
local_dp_rank,
global_dp_rank,
dp_master_ip,
dp_master_port,
GPUs_per_dp_rank,
enforce_eager,
enable_expert_parallel,
trust_remote_code,
max_num_seqs,
max_model_len,
compilation_config,
gpu_memory_utilization,
enable_dbo,
quantization,
engine_args,
):
os.environ["VLLM_DP_RANK"] = str(global_dp_rank)
os.environ["VLLM_DP_RANK_LOCAL"] = str(local_dp_rank)
......@@ -173,19 +110,7 @@ def main(
)
# Create an LLM.
llm = LLM(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=enforce_eager,
enable_expert_parallel=enable_expert_parallel,
trust_remote_code=trust_remote_code,
max_num_seqs=max_num_seqs,
max_model_len=max_model_len,
gpu_memory_utilization=gpu_memory_utilization,
enable_dbo=enable_dbo,
quantization=quantization,
compilation_config=compilation_config,
)
llm = LLM(**engine_args)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for i, output in enumerate(outputs):
......@@ -204,22 +129,29 @@ def main(
if __name__ == "__main__":
args = parse_args()
parser = create_parser()
args = vars(parser.parse_args())
# Extract DP-specific args
dp_size = args.pop("data_parallel_size")
nnodes = args.get("nnodes", 1)
node_rank = args.get("node_rank", 0)
master_addr = args.get("master_addr", "")
master_port = args.get("master_port", 0)
timeout = args.pop("timeout")
dp_size = args.dp_size
tp_size = args.tp_size
node_size = args.node_size
node_rank = args.node_rank
# Remaining args are engine args
engine_args = args
if node_size == 1:
if nnodes == 1:
dp_master_ip = "127.0.0.1"
dp_master_port = get_open_port()
else:
dp_master_ip = args.master_addr
dp_master_port = args.master_port
dp_master_ip = master_addr
dp_master_port = master_port
assert dp_size % node_size == 0, "dp_size should be divisible by node_size"
dp_per_node = dp_size // node_size
assert dp_size % nnodes == 0, "dp_size should be divisible by nnodes"
dp_per_node = dp_size // nnodes
from multiprocessing import Process
......@@ -235,29 +167,19 @@ if __name__ == "__main__":
proc = Process(
target=main,
args=(
args.model,
dp_size,
local_dp_rank,
global_dp_rank,
dp_master_ip,
dp_master_port,
tp_size,
args.enforce_eager,
args.enable_expert_parallel,
args.trust_remote_code,
args.max_num_seqs,
args.max_model_len,
args.compilation_config,
args.gpu_memory_utilization,
args.enable_dbo,
args.quantization,
engine_args,
),
)
proc.start()
procs.append(proc)
exit_code = 0
for proc in procs:
proc.join(timeout=args.timeout)
proc.join(timeout=timeout)
if proc.exitcode is None:
print(f"Killing process {proc.pid} that didn't stop within 5 minutes.")
proc.kill()
......
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