ut:add e2e test for external launcher (#2091)
### What this PR does / why we need it?
This pr add e2e testcase to make sure initialize LLM by
external_launcher method is ok.
### Does this PR introduce _any_ user-facing change?
not involved
### How was this patch tested?
not involved
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
This commit is contained in:
Ronald1995
239
examples/offline_external_launcher.py
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239
examples/offline_external_launcher.py
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/examples/offline_inference/data_parallel.py
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# Note: This script is designed to run with e2e test,
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# please be careful to modify it.
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"""
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Usage:
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Single node:
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Dense models:
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python examples/offline_external_launcher.py \
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--model="Qwen/Qwen2.5-0.5B-Instruct" \
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--tp-size=1 \
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--proc-per-node=2
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MOE models:
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python examples/offline_external_launcher.py \
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--model="Qwen/Qwen3-0.6B" \
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--tp-size=2 \
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--proc-per-node=2 \
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--enable-expert-parallel
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Multi-node:
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Node 0 (assume the node has ip of 10.99.48.128):
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python examples/offline_external_launcher.py \
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--model="Qwen/Qwen3-0.6B" \
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--tp-size=2 \
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--node-size=2 \
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--node-rank=0 \
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--proc-per-node=2 \
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--enable-expert-parallel \
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--master-addr=10.99.48.128 \
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--master-port=13345
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Node 1:
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python examples/offline_external_launcher.py \
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--model="Qwen/Qwen3-0.6B" \
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--tp-size=2 \
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--node-size=2 \
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--node-rank=1 \
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--enable-expert-parallel \
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--master-addr=10.99.48.128 \
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--master-port=13345
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"""
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import argparse
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import contextlib
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import gc
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import os
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from multiprocessing import Process
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from time import sleep
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import torch
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from vllm import LLM, SamplingParams
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from vllm.distributed.parallel_state import ( # noqa E402
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destroy_distributed_environment, destroy_model_parallel, get_tp_group)
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from vllm.utils import get_open_port
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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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def parse_args():
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parser = argparse.ArgumentParser(description="External launcher Inference")
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parser.add_argument(
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"--model",
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type=str,
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default="Qwen/Qwen3-0.6B",
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help="Model name or path",
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)
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parser.add_argument("--tp-size",
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type=int,
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default=1,
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help="Tensor parallel size")
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parser.add_argument("--node-size",
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type=int,
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default=1,
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help="Total number of nodes")
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parser.add_argument("--node-rank",
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type=int,
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default=0,
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help="Rank of the current node")
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parser.add_argument("--proc-per-node",
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type=int,
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default=1,
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help="Number of processes per node")
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parser.add_argument("--master-addr",
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type=str,
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default="",
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help="Master node IP address")
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parser.add_argument("--master-port",
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type=int,
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default=0,
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help="Master node port")
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parser.add_argument("--enforce-eager",
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action="store_true",
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help="Enforce eager mode execution.")
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parser.add_argument("--trust-remote-code",
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action="store_true",
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help="Trust remote code.")
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parser.add_argument("--enable-expert-parallel",
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action="store_true",
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help="Enable expert parallel, used in MOE models.")
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return parser.parse_args()
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def main(
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local_rank: int,
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rank: int,
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master_addr: str,
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master_port: int,
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model: str = "Qwen/Qwen3-0.6B",
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world_size: int = 4,
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tensor_parallel_size: int = 2,
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enable_expert_parallel: bool = False,
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enforce_eager: bool = False,
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trust_remote_code: bool = True,
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):
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os.environ["MASTER_ADDR"] = master_addr
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os.environ["MASTER_PORT"] = str(master_port)
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os.environ["RANK"] = str(rank)
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os.environ["LOCAL_RANK"] = str(local_rank)
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os.environ["WORLD_SIZE"] = str(world_size)
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if not torch.distributed.is_initialized():
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torch.distributed.init_process_group(
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backend="cpu:gloo,npu:hccl",
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world_size=world_size,
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rank=rank,
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)
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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] * 10
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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max_tokens=10,
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)
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llm = LLM(
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model=model,
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tensor_parallel_size=tensor_parallel_size,
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enable_expert_parallel=enable_expert_parallel,
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enforce_eager=enforce_eager,
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trust_remote_code=trust_remote_code,
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distributed_executor_backend="external_launcher",
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seed=0,
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)
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tp_ranks = get_tp_group().ranks
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print(f'TP RANKS: {tp_ranks}')
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outputs = llm.generate(prompts, sampling_params)
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for i, output in enumerate(outputs):
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if i >= 5:
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# print only 5 outputs
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break
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Global rank: {rank}, Prompt: {prompt!r}, "
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f"Generated text: {generated_text!r}")
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# Give engines time to pause their processing loops before exiting.
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sleep(5)
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del llm
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cleanup_env_and_memory()
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def cleanup_env_and_memory():
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destroy_model_parallel()
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destroy_distributed_environment()
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with contextlib.suppress(AssertionError):
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torch.distributed.destroy_process_group()
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gc.collect()
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torch.npu.empty_cache()
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torch.npu.reset_peak_memory_stats()
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if __name__ == "__main__":
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args = parse_args()
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tp_size = args.tp_size
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node_size = args.node_size
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proc_per_node = args.proc_per_node
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node_rank = args.node_rank
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if node_size == 1:
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master_addr = "127.0.0.1"
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master_port = get_open_port()
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else:
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master_addr = args.master_addr
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master_port = args.master_port
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world_size = node_size * proc_per_node
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procs = []
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for local_rank, rank in enumerate(
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range(proc_per_node * node_rank, proc_per_node * (node_rank + 1))):
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proc = Process(target=main,
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args=(
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local_rank,
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rank,
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master_addr,
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master_port,
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args.model,
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world_size,
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tp_size,
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args.enable_expert_parallel,
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args.enforce_eager,
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args.trust_remote_code,
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))
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proc.start()
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procs.append(proc)
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exit_code = 0
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for proc in procs:
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proc.join(timeout=600)
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if proc.exitcode is None:
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print(
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f"Killing process {proc.pid} that didn't stop within 30 minutes."
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)
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proc.kill()
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exit_code = 1
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elif proc.exitcode:
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exit_code = proc.exitcode
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exit(exit_code)
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73
tests/e2e/multicard/test_external_launcher.py
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73
tests/e2e/multicard/test_external_launcher.py
Normal file
@@ -0,0 +1,73 @@
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Compare the outputs of vLLM with and without aclgraph.
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Run `pytest tests/multicard/test_external_launcher.py`.
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"""
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import os
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import subprocess
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import sys
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from pathlib import Path
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from unittest.mock import patch
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import pytest
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MODELS = ["Qwen/Qwen3-0.6B"]
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@pytest.mark.parametrize("model", MODELS)
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@patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1,2,3"})
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def test_external_launcher(model):
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script = Path(
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__file__
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).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"
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env = os.environ.copy()
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# TODO: Change to 2 when ci machine has 4 cards
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cmd = [
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sys.executable,
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str(script),
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"--model",
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model,
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"--tp-size",
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"1",
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"--node-size",
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"1",
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"--node-rank",
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"0",
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"--proc-per-node",
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"2",
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"--trust-remote-code",
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]
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print(f"Running subprocess: {' '.join(cmd)}")
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proc = subprocess.run(
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cmd,
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env=env,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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timeout=600,
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)
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output = proc.stdout.decode()
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print(output)
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assert "TP RANKS: [0]" in output
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assert "TP RANKS: [1]" in output
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assert "Generated text:" in output
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assert proc.returncode == 0
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