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CakeCN
216
AlgoriAgent/examples/distributed_simulation/main.py
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216
AlgoriAgent/examples/distributed_simulation/main.py
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# -*- coding: utf-8 -*-
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""" A large-scale social simulation experiment """
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import argparse
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import time
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from concurrent import futures
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from concurrent.futures import as_completed
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from loguru import logger
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from participant import Moderator, RandomParticipant, LLMParticipant
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import agentscope
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from agentscope.agents import AgentBase
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from agentscope.server import RpcAgentServerLauncher
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from agentscope.message import Msg
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def parse_args() -> argparse.Namespace:
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"""Parse arguments"""
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--role",
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choices=["participant", "main"],
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default="main",
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)
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parser.add_argument(
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"--agent-type",
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choices=["random", "llm"],
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default="random",
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)
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parser.add_argument("--max-value", type=int, default=100)
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parser.add_argument("--sleep-time", type=float, default=1.0)
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parser.add_argument(
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"--hosts",
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type=str,
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nargs="+",
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default=["localhost"],
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)
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parser.add_argument("--participant-num", type=int, default=100)
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parser.add_argument("--base-port", type=int, default=12010)
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parser.add_argument(
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"--server-per-host",
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type=int,
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)
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parser.add_argument("--model-per-host", type=int, default=1)
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parser.add_argument("--moderator-per-host", type=int, default=1)
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return parser.parse_args()
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def setup_participant_agent_server(host: str, port: int) -> None:
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"""Set up agent server"""
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agentscope.init(
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project="simulation",
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name="server",
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runtime_id=str(port),
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save_code=False,
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save_api_invoke=False,
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model_configs="configs/model_configs.json",
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use_monitor=False,
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)
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assistant_server_launcher = RpcAgentServerLauncher(
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host=host,
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port=port,
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max_pool_size=16384,
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custom_agents=[Moderator, RandomParticipant, LLMParticipant],
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)
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assistant_server_launcher.launch(in_subprocess=False)
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assistant_server_launcher.wait_until_terminate()
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def init_moderator(
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name: str,
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configs: list[dict],
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host: str,
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port: int,
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agent_type: str,
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max_value: int,
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sleep_time: float,
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) -> AgentBase:
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"""Init moderator"""
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return Moderator( # pylint: disable=E1123
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name=name,
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part_configs=configs,
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agent_type=agent_type,
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max_value=max_value,
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sleep_time=sleep_time,
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to_dist={
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"host": host,
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"port": port,
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},
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)
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def run_main_process(
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hosts: list[str],
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base_port: int,
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server_per_host: int,
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model_per_host: int,
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participant_num: int,
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moderator_per_host: int = 10,
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agent_type: str = "random",
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max_value: int = 100,
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sleep_time: float = 1.0,
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) -> None:
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"""Run main process"""
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agentscope.init(
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project="simulation",
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name="main",
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save_code=False,
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save_api_invoke=False,
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model_configs="configs/model_configs.json",
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use_monitor=False,
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)
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host_num = len(hosts)
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total_agent_server_num = server_per_host * host_num
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participant_per_agent_server = participant_num // total_agent_server_num
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ist = time.time()
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configs = []
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logger.info(f"init {participant_num} {agent_type} participant agents...")
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# build init configs of participants
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for i in range(participant_num):
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idx = i // participant_per_agent_server
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host_id = idx // server_per_host
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port_id = idx % server_per_host
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model_id = i % model_per_host
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host = hosts[host_id]
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port = base_port + port_id
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config_name = f"model_{model_id + 1}"
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if agent_type == "random":
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configs.append(
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{
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"name": f"P{i}",
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"host": host,
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"port": port,
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},
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)
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else:
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configs.append(
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{
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"name": f"P{i}",
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"model_config_name": config_name,
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"host": host,
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"port": port,
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},
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)
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mods = []
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moderator_num = moderator_per_host * host_num
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participant_per_moderator = participant_num // moderator_num
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tasks = []
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logger.info(f"init {moderator_num} moderator agents...")
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# init moderators
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with futures.ThreadPoolExecutor(max_workers=None) as executor:
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for i in range(moderator_num):
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tasks.append(
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executor.submit(
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init_moderator,
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name=f"mod_{i}",
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configs=configs[
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i
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* participant_per_moderator : (i + 1) # noqa
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* participant_per_moderator
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],
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host=hosts[i // moderator_per_host],
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port=base_port + server_per_host + i % moderator_per_host,
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agent_type=agent_type,
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max_value=max_value,
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sleep_time=sleep_time,
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),
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)
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for task in as_completed(tasks):
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mods.append(task.result())
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iet = time.time()
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logger.info(f"[init takes {iet - ist} s]")
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# run te
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st = time.time()
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results = []
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for p in mods:
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results.append(p())
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summ = 0
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cnt = 0
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for r in results:
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try:
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summ += int(r["content"]["sum"])
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cnt += int(r["content"]["cnt"])
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except Exception:
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logger.error(r["content"])
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et = time.time()
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logger.chat(
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Msg(
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name="Moderator",
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role="assistant",
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content=f"The average value is {summ/cnt} [takes {et-st} s]",
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),
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)
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if __name__ == "__main__":
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args = parse_args()
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if args.role == "participant":
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setup_participant_agent_server(args.hosts[0], args.base_port)
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elif args.role == "main":
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run_main_process(
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hosts=args.hosts,
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base_port=args.base_port,
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participant_num=args.participant_num,
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server_per_host=args.server_per_host,
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model_per_host=args.model_per_host,
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moderator_per_host=args.moderator_per_host,
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agent_type=args.agent_type,
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sleep_time=args.sleep_time,
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max_value=args.max_value,
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)
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