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run_open_LLM_with_vllm.py
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run_open_LLM_with_vllm.py
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import os
import time
import argparse
from utils import error
from utils.llm_aft_trainer import LLM_Inference_VLLM
from utils.config import *
from utils.utils import merge
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--memo", type=str, default='LLMTLCSRun')
parser.add_argument("--llm_model", type=str, default="llama_cgpr_13b_jinan_1")
parser.add_argument("--llm_path", type=str, default="./ft_models/merged/llama_cgpr_13b_jinan_1")
parser.add_argument("--num_rounds", type=int, default=1)
parser.add_argument("--new_max_tokens", type=int, default=1024)
parser.add_argument("--proj_name", type=str, default="LLM-TSCS-extreme")
parser.add_argument("--eightphase", action="store_true", default=False)
parser.add_argument("--multi_process", action="store_true", default=True)
parser.add_argument("--workers", type=int, default=1)
parser.add_argument("--dataset", type=str, default="template")
parser.add_argument("--traffic_file", type=str, default="flow_main_stream.json")
return parser.parse_args()
def main(in_args):
traffic_file_list = []
if in_args.dataset == 'jinan':
count = 3600
road_net = "3_4"
traffic_file_list = ["anon_3_4_jinan_real.json", "anon_3_4_jinan_real_2000.json",
"anon_3_4_jinan_real_2500.json", "anon_3_4_jinan_synthetic_24000_60min.json",
"anon_3_4_jinan_synthetic_24h_6000.json"]
template = "Jinan"
elif in_args.dataset == 'hangzhou':
count = 3600
road_net = "4_4"
traffic_file_list = ["anon_4_4_hangzhou_real.json", "anon_4_4_hangzhou_real_5816.json",
"anon_4_4_hangzhou_synthetic_24000_60min.json"]
template = "Hangzhou"
elif in_args.dataset == 'newyork_16x3':
count = 3600
road_net = "16_3"
traffic_file_list = ["anon_16_3_newyork_real.json"]
template = "NewYork"
elif in_args.dataset == 'newyork_28x7':
count = 3600
road_net = "28_7"
traffic_file_list = ["anon_28_7_newyork_real_double.json", "anon_28_7_newyork_real_triple.json"]
template = "NewYork"
in_args.model = in_args.memo
if "24h" in in_args.traffic_file:
count = 86400
# flow_file error
try:
if in_args.traffic_file not in traffic_file_list:
raise error.flowFileException('Flow file does not exist.')
except error.flowFileException as e:
print(e)
return
NUM_ROW = int(road_net.split('_')[0])
NUM_COL = int(road_net.split('_')[1])
num_intersections = NUM_ROW * NUM_COL
print('num_intersections:', num_intersections)
print(in_args.traffic_file)
dic_agent_conf_extra = {
"LLM_PATH": in_args.llm_path,
"LLM_MODEL": in_args.llm_model,
"LOG_DIR": f"./{in_args.llm_model}_logs",
"NEW_MAX_TOKENS": in_args.new_max_tokens
}
dic_traffic_env_conf_extra = {
"NUM_AGENTS": num_intersections,
"NUM_INTERSECTIONS": num_intersections,
"MODEL_NAME": f"{in_args.model}-{dic_agent_conf_extra['LLM_MODEL']}",
"PROJECT_NAME": in_args.proj_name,
"RUN_COUNTS": count,
"NUM_ROUNDS": in_args.num_rounds,
"NUM_ROW": NUM_ROW,
"NUM_COL": NUM_COL,
"TRAFFIC_FILE": in_args.traffic_file,
"ROADNET_FILE": "roadnet_{0}.json".format(road_net),
"LIST_STATE_FEATURE": [
"cur_phase",
"traffic_movement_pressure_queue",
],
"DIC_REWARD_INFO": {
'queue_length': -0.25
}
}
if in_args.eightphase:
dic_traffic_env_conf_extra["PHASE"] = {
1: [0, 1, 0, 1, 0, 0, 0, 0],
2: [0, 0, 0, 0, 0, 1, 0, 1],
3: [1, 0, 1, 0, 0, 0, 0, 0],
4: [0, 0, 0, 0, 1, 0, 1, 0],
5: [1, 1, 0, 0, 0, 0, 0, 0],
6: [0, 0, 1, 1, 0, 0, 0, 0],
7: [0, 0, 0, 0, 0, 0, 1, 1],
8: [0, 0, 0, 0, 1, 1, 0, 0]
}
dic_traffic_env_conf_extra["PHASE_LIST"] = ['WT_ET', 'NT_ST', 'WL_EL', 'NL_SL',
'WL_WT', 'EL_ET', 'SL_ST', 'NL_NT']
dic_agent_conf_extra["FIXED_TIME"] = [30, 30, 30, 30, 30, 30, 30, 30]
else:
dic_agent_conf_extra["FIXED_TIME"] = [30, 30, 30, 30]
dic_traffic_env_conf_extra["NUM_AGENTS"] = dic_traffic_env_conf_extra["NUM_INTERSECTIONS"]
dic_path_extra = {
"PATH_TO_MODEL": os.path.join("model", in_args.memo, in_args.traffic_file + "_" +
time.strftime('%m_%d_%H_%M_%S', time.localtime(time.time()))),
"PATH_TO_WORK_DIRECTORY": os.path.join("records", in_args.memo, in_args.traffic_file + "_" +
time.strftime('%m_%d_%H_%M_%S', time.localtime(time.time()))),
"PATH_TO_DATA": os.path.join("data", template, str(road_net))
}
trainer = LLM_Inference_VLLM(dic_agent_conf_extra,
merge(dic_traffic_env_conf, dic_traffic_env_conf_extra),
dic_path_extra,
f'{template}-{road_net}', in_args.traffic_file.split(".")[0])
trainer.train_test()
if __name__ == "__main__":
args = parse_args()
main(args)