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import argparse
import json
import os
import numpy as np
import random
import torch
import time
from vllm import LLM, SamplingParams
from kk_processor import KKProcessor
import datasets
from concurrent.futures import ThreadPoolExecutor
def load_jsonl(file_path):
"""Load data from a JSONL file."""
if not os.path.exists(file_path):
return []
with open(file_path, "r") as file:
return [json.loads(line) for line in file]
def write_jsonl(output_file, data):
"""Write data to a JSONL file."""
with open(output_file, "w+") as file:
for item in data:
file.write(json.dumps(item) + "\n")
def init_seed(seed=42):
"""Initialize random seeds for reproducibility."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
def load_eval_records(args, subject):
"""Load evaluation records based on arguments."""
if args.problem_type != "clean":
records = datasets.load_dataset('K-and-K/perturbed-knights-and-knaves',
data_files=f"{args.split}/{args.problem_type}/{subject}.jsonl")["train"]
else:
records = datasets.load_dataset('K-and-K/knights-and-knaves',
data_files=f"{args.split}/{subject}.jsonl")["train"]
return records
def eval_subject(args, subject, llm, test_records, kk_proc, exist_result_records):
"""Evaluate one subject."""
cors = []
start_index = len(exist_result_records)
print(f"Processing {subject} starting from index {start_index}")
# Prepare all prompts using multi-threading
prompts, labels = [], []
new_prompts = []
with ThreadPoolExecutor() as executor:
futures = [
executor.submit(kk_proc.gen_test_prompt, args.ntrain, test_records, i, args.model)
for i in range(start_index, len(test_records))
]
for future in futures:
prompt, label = future.result()
prompts.append(prompt)
labels.append(label)
question = prompt.split("### Question:")[1].strip().split("### Answer:")[0].strip()
new_prompt = f"""<|im_start|>system\nYou are a helpful assistant. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and<answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>. Now the user asks you to solve a logical reasoning problem. After thinking, when you finally reach a conclusion, clearly state the identity of each character within <answer> </answer> tags. i.e., <answer> (1) ...\n(2) ... </answer>.\n<|im_end|>\n<|im_start|>user\n{question}\n<|im_end|>\n<|im_start|>assistant\n<think>"""
new_prompts.append(new_prompt)
# Batch inference using vLLM
sampling_params = SamplingParams(
temperature=args.temperature,
top_p=args.top_p,
max_tokens=args.max_token,
seed=args.seed
)
outputs = llm.generate(new_prompts, sampling_params)
responses = [output.outputs[0].text for output in outputs]
# Process results and save to exist_result_records
for i, (new_prompt, label, response) in enumerate(zip(new_prompts, labels, responses), start=start_index):
cor, parsed_pred, reformat_gold_conditions = kk_proc._parse_cot_eval_instruct(response, label, args.model, test_records[i]['names'], test_records[i]['solution_text_format'])
cors.append(cor)
new_item = {
'quiz': test_records[i]['quiz'],
'names': test_records[i]['names'],
'solution': test_records[i]['solution'],
'solution_text': test_records[i]['solution_text'],
'solution_text_format': test_records[i]['solution_text_format'],
'index': test_records[i]['index'],
'predicts': parsed_pred,
'labels': reformat_gold_conditions,
'correct': cor,
'response': response,
'prompts': new_prompt,
}
exist_result_records.append(new_item)
acc = np.mean(cors)
print(f"Subject: {subject}, Accuracy: {acc:.3f}")
return cors, acc, exist_result_records
def load_limited_test_records(args, subject, exist_result_records):
"""Load limited test records based on given arguments."""
test_records = load_eval_records(args, subject)
print("test_records:", test_records)
if args.limit is not None:
test_records = test_records.select(range(min(args.limit, len(test_records))))
if args.limit <= len(exist_result_records):
return None # Already processed all samples
return test_records
def save_final_acc_results(all_cors, results, fname):
"""Save final accuracy results to a file."""
if all_cors:
weighted_acc = np.mean(np.concatenate(all_cors))
results["weighted_accuracy"] = weighted_acc
print(f"Overall Average Accuracy: {weighted_acc:.3f}")
with open(fname, "w") as f:
json.dump(results, f)
def load_previous_acc_results(fname):
"""Load previous accuracy results."""
acc_results = {"subject": {}}
if os.path.isfile(fname):
with open(fname, 'r', encoding='utf-8') as file:
acc_results = json.load(file)
return acc_results
def get_subjects_to_eval(args):
"""Get subjects to evaluate."""
subjects = []
if args.split == "test":
if args.eval_nppl == 0:
subjects = [f"people{nppl}_num100" for nppl in range(2, 9)]
else:
subjects = [f"people{args.eval_nppl}_num100"]
elif args.split == "train":
if args.eval_nppl == 2:
subjects = ["people2_num200"]
elif args.eval_nppl > 2:
subjects = [f"people{args.eval_nppl}_num1000"]
return subjects
def main(args):
model_short_name = "/".join(args.model.split("/")[-2:])
prefix = os.path.join(args.save_dir, f"{model_short_name}_{args.ntrain}shot")
args.config += f"_token{args.max_token}{('_cot' if args.cot else '')}" \
f"_{args.split}{('_' + args.problem_type if args.problem_type != 'clean' else '')}"
output_folder = os.path.join(prefix, args.config)
acc_fname = args.output_file # os.path.join(prefix, f"result_{args.config}.json")
os.makedirs(output_folder, exist_ok=True)
print("Configuration:", args.config)
print("Output Folder:", output_folder)
kk_proc = KKProcessor(cot=args.cot, no_linebreak=args.no_linebreak)
subjects = get_subjects_to_eval(args)
acc_results = load_previous_acc_results(acc_fname)
# Initialize vLLM model
llm = LLM(
model=args.model,
tensor_parallel_size=args.ngpus,
max_model_len=args.max_token,
gpu_memory_utilization=0.85,
enforce_eager=False
)
all_cors = []
for subject in subjects:
result_outfile = os.path.join(output_folder, f"{subject}.jsonl")
exist_result_records = load_jsonl(result_outfile)
test_records = load_limited_test_records(args, subject, exist_result_records)
print("exist_result_records:", exist_result_records)
print("test_records:", test_records)
if test_records is None:
continue
cors, acc, result_records = eval_subject(args, subject, llm, test_records, kk_proc, exist_result_records)
write_jsonl(result_outfile, result_records)
all_cors.append(cors)
acc_results["subject"][subject] = acc
save_final_acc_results(all_cors, acc_results, acc_fname)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Evaluation script for KK dataset")
parser.add_argument("--ntrain", "-k", type=int, default=0, help="Number of training examples")
parser.add_argument("--data_dir", "-d", type=str, default="data", help="Data directory")
parser.add_argument("--save_dir", "-s", type=str, default="result_qa", help="Save directory")
parser.add_argument("--model", "-m", type=str, required=True, help="Model name or path")
parser.add_argument("--max_token", type=int, default=1024, help="Maximum number of tokens")
parser.add_argument("--limit", type=int, default=None, help="Limit the number of examples")
parser.add_argument("--cot", action="store_true", help="Use chain-of-thought prompting")
parser.add_argument("--no_linebreak", action="store_true", help="Remove line breaks")
parser.add_argument("--batch_size", type=int, default=8, help="Batch size for VLLM")
parser.add_argument("--split", type=str, default="test", choices=["test", "train"], help="Data split to use")
parser.add_argument("--eval_nppl", type=int, default=0, help="Number of people to evaluate")
parser.add_argument("--problem_type", type=str, default="clean", help="Problem perturbation type")
parser.add_argument("--temperature", type=float, default=1.0, help="Temperature for sampling")
parser.add_argument("--top_p", type=float, default=1.0, help="Top-p (nucleus) sampling")
parser.add_argument("--config", type=str, default="default_config", help="Configuration string for saving results")
parser.add_argument("--ngpus", type=int, default=1, help="Number of GPUs for parallel inference")
parser.add_argument("--output_file", type=str, default="output_file", help="Output File Path")
parser.add_argument("--seed", type=int, default=42, help="Seed")
args = parser.parse_args()
init_seed(seed=args.seed)
main(args)
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