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run_usingLLM.py
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import os
import json
import argparse
from tot.tasks import get_task
from tot.methods.bfs import naive_solve, solve_usingLLM_eval
from tot.models import gpt_usage
import openai
def run(args):
task = get_task(args.task)
logs, cnt_avg, cnt_any = [], 0, 0
lat_all, lat_generate, lat_eval = 0, 0, 0
if args.naive_run:
file = f"./logs/{args.task}/{args.localbackend}/{args.remotebackend}/{args.temperature}_naive_{args.prompt_sample}_sample_{args.n_generate_sample}_start{args.task_start_index}_end{args.task_end_index}_usingLLM"
else:
file = f"./logs/{args.task}/{args.localbackend}/{args.remotebackend}/{args.temperature}_{args.method_generate}{args.n_generate_sample}_{args.method_evaluate}{args.n_evaluate_sample}_{args.method_select}{args.n_select_sample}_start{args.task_start_index}_end{args.task_end_index}_smg_{args.slm_generate}_sme_{args.slm_eval}_check_{args.check_format}_rule_{args.eval_rule}_warm_{args.warm_start}_last_{args.last_lm}_idx_{args.inference_idx}"
os.makedirs(os.path.dirname(file + ".json"), exist_ok=True)
for i in range(args.task_start_index, args.task_end_index):
print("Solve task ", i)
# solve
if args.naive_run:
ys, info = naive_solve(args, task, i)
else:
ys, info, lat_dict = solve_usingLLM_eval(args, task, i)
# log
print("ys ", ys)
infos, output_list = [], []
for y in ys:
r, new_output = task.test_output_modfiy(i, y) # type: ignore
if new_output not in output_list: # Avoid duplication of outputs
output_list.append(new_output)
else:
r = {"r": 0} # Do not count twice
infos.append(r)
token_consumption = gpt_usage(args.localbackend)
info.update(
{
"idx": i,
"ys": ys,
"infos": infos,
"usage_so_far": token_consumption,
} # type: ignore
) # type: ignore
info.update(lat_dict) # jinyu: update the latency
lat_all, lat_generate, lat_eval = (
lat_all + sum(lat_dict["all"]),
lat_generate + sum(lat_dict["generate"]),
lat_eval + sum(lat_dict["eval"]),
)
logs.append(info)
with open(file + ".json", "w") as f:
json.dump(logs, f, indent=4)
# log main metric
accs = [info["r"] for info in infos]
cnt_avg += sum(accs) # / len(accs) #jinyu: counting the sum
cnt_any += any(accs)
print(i, "sum(accs)", sum(accs), "cnt_avg", cnt_avg, "cnt_any", cnt_any, "\n")
n = args.task_end_index - args.task_start_index
print("The average sum is ", cnt_avg / n, ". The accuracy is: ", cnt_any / n)
print("Token consumption: ", token_consumption)
print("Latency: ", lat_all, ", ", lat_generate, ", ", lat_eval)
res_json = {
"avg_sum": cnt_avg / n,
"acc": cnt_any / n,
"lat": lat_all,
"lat_generate": lat_generate,
"lat_eval": lat_eval,
"sm": args.localbackend,
"llm": args.remotebackend,
}
res_json.update(token_consumption)
with open(file + "_performance.json", "w") as f:
json.dump(res_json, f, indent=4)
def parse_args():
args = argparse.ArgumentParser()
args.add_argument(
"--localbackend",
type=str,
choices=[
"gpt-4",
"gpt-3.5-turbo",
"gpt-4o",
"gpt-4o-mini",
"lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",
"bartowski/Phi-3-medium-128k-instruct-GGUF",
"meta-llama-3.1-8b-instruct@q4_k_m",
"Qwen/Qwen2.5-32B-Instruct-GGUF",
"phi-3.1-mini-128k-instruct",
],
default="bartowski/Phi-3-medium-128k-instruct-GGUF",
)
args.add_argument(
"--remotebackend",
type=str,
choices=[
"gpt-4",
"gpt-3.5-turbo",
"gpt-4o",
"gpt-4o-mini",
"lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",
"bartowski/Phi-3-medium-128k-instruct-GGUF",
"meta-llama-3.1-8b-instruct@q4_k_m",
"Qwen/Qwen2.5-32B-Instruct-GGUF",
"qwen2.5-32b-instruct",
],
default="qwen2.5-32b-instruct",
)
args.add_argument("--temperature", type=float, default=0.9)
args.add_argument("--task", type=str, required=True, choices=["game24", "text", "crosswords"])
args.add_argument("--task_start_index", type=int, default=900)
args.add_argument("--task_end_index", type=int, default=1000)
args.add_argument("--naive_run", action="store_true")
args.add_argument(
"--prompt_sample", type=str, choices=["standard", "cot"]
) # only used when method_generate = sample, or naive_run
args.add_argument("--method_generate", type=str, choices=["sample", "propose"])
args.add_argument("--method_evaluate", type=str, choices=["value", "vote"])
args.add_argument("--method_select", type=str, choices=["sample", "greedy"], default="greedy")
args.add_argument("--n_generate_sample", type=int, default=1) # only thing needed if naive_run
args.add_argument("--n_evaluate_sample", type=int, default=1)
args.add_argument("--n_select_sample", type=int, default=1)
# jinyu
args.add_argument("--slm_generate", action="store_true", help="use small lm for generation")
args.add_argument("--slm_eval", action="store_true", help="use small lm for evaluation")
args.add_argument(
"--check_format",
action="store_true",
help="check the format and correctness of the generated contents",
)
args.add_argument("--eval_rule", action="store_true", help="use rules for evaluation")
args.add_argument(
"--warm_start",
action="store_true",
help="step 0 uses large model for generation",
)
args.add_argument("--inference_idx", type=int, default=0, help="Do multiple experiments")
args.add_argument("--last_lm", action="store_true", help="Use the large model for the last step")
args.add_argument("--filter", action="store_true", help="Enable filtering for specific runs.")
args = args.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
print(args)
print(f"api_key is set to {openai.api_key}, api_base is set to{openai.api_base}.")
run(args)