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import argparse
import json
import os
import pickle
from concurrent.futures import ProcessPoolExecutor, as_completed
from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple
from rich.progress import track
from evalplus.data import write_jsonl
from tools.tsr.coverage_init import collect_coverage_info
from tools.tsr.mutation_init import collect_mutation_info
from tools.tsr.sample_init import collect_sample_info
from tools.tsr.utils import get_problems, get_task_ids, to_path
def global_util_init(dataset: str):
global problems
global task_ids
global problem_count
problems = get_problems(dataset)
task_ids = get_task_ids(dataset)
problem_count = len(problems)
###########################
# Greedy Min Set Covering #
###########################
def merge_set_cover(*args) -> Dict[str, List[str]]:
merged_set_cover = {task_id: [] for task_id in task_ids}
for set_cover_dict in args:
for task_id, plus_tests in set_cover_dict.items():
for plus_test in plus_tests:
if plus_test not in merged_set_cover[task_id]:
merged_set_cover[task_id].append(plus_test)
return merged_set_cover
def greedy_cover(
task_id: str, tests: Dict[str, List[Any]], exclude_model: str
) -> Tuple[str, List[str]]:
q, U = [], set()
for test_name, test_cover in tests.items():
cover_set = set()
for model_path, i_code in test_cover:
if exclude_model not in model_path:
cover_set.add((model_path, i_code))
q.append((test_name, cover_set))
U = U.union(cover_set)
# Greedy algorithm for min set cover
min_cover = []
while len(U) > 0:
max_uncover_set, max_test_name = {}, ""
for test_name, cover_set in q:
if len(cover_set) > len(max_uncover_set):
max_uncover_set = cover_set
max_test_name = test_name
min_cover.append(max_test_name)
U = U - max_uncover_set
qq = []
for test_name, cover_set in q:
new_cover_set = U.intersection(cover_set)
if len(new_cover_set) != 0:
qq.append((test_name, new_cover_set))
q = qq
return task_id, min_cover
def parallel_greedy_cover(
info_dict: Optional[Dict[str, Dict[str, List[Any]]]],
exclude_model: str,
type: str,
**kwargs,
) -> Dict[str, List[str]]:
plus_tests = {task_id: [] for task_id in task_ids}
with ProcessPoolExecutor(max_workers=32) as executor:
futures = []
for task_id in task_ids:
if type == "sample":
path_task_id = to_path(task_id)
sample_dir = kwargs["sample_dir"]
with open(os.path.join(sample_dir, f"{path_task_id}.pkl"), "rb") as f:
td = pickle.load(f)
args = (task_id, td, exclude_model)
else:
args = (task_id, info_dict[task_id], exclude_model)
futures.append(executor.submit(greedy_cover, *args))
for future in track(as_completed(futures), f"min set cover :: {type}"):
task_id, min_cover = future.result()
plus_tests[task_id] = min_cover
return plus_tests
#####################
# Collect Set Cover #
#####################
def get_coverage_set_cover(
coverage_dir: str, exclude_model: str, dataset: str
) -> Dict[str, List[str]]:
coverage_info_dict = collect_coverage_info(coverage_dir, dataset)
return parallel_greedy_cover(coverage_info_dict, exclude_model, "coverage")
def get_mutation_set_cover(
mutation_dir: str, exclude_model: str, dataset: str
) -> Dict[str, List[str]]:
mutation_info_dict = collect_mutation_info(
os.path.join(mutation_dir, "eval_results.json"), dataset
)
return parallel_greedy_cover(mutation_info_dict, exclude_model, "mutation")
def get_sample_set_cover(
sample_dir: str, sample_eval_dir: str, exclude_model: str, dataset: str
) -> Dict[str, List[str]]:
collect_sample_info(sample_dir, sample_eval_dir, dataset)
return parallel_greedy_cover(None, exclude_model, "sample", sample_dir=sample_dir)
#################
# pass@1 greedy #
#################
def compute_avg_test(set_cover_info: Dict[str, List[str]]) -> float:
sum_tests = sum(
len(problems[task_id]["base_input"]) + len(set_cover_info[task_id])
for task_id in task_ids
)
return sum_tests / problem_count
def gen_report(set_cover_info: Dict[str, List[str]], sample_eval_dir: str, model: str):
tsr_dict = {"ntests": compute_avg_test(set_cover_info), "pass@1": 0}
model_path = os.path.join(sample_eval_dir, f"{model}_temp_0.0", "eval_results.json")
with open(model_path, "r") as f:
mdict = json.load(f)
correct_cnt = 0
for task_id in task_ids:
legacy_task_id = task_id
if legacy_task_id not in mdict["eval"]:
legacy_task_id = legacy_task_id.replace("/", "_")
if mdict["eval"][legacy_task_id]["base"][0][0] != "success":
continue
correct = True
for plus_id in set_cover_info[task_id]:
index = int(plus_id.split("_")[-1])
if mdict["eval"][legacy_task_id]["plus"][0][1][index] == False:
correct = False
break
if correct:
correct_cnt += 1
tsr_dict["pass@1"] = correct_cnt / problem_count
return tsr_dict
def dump_humaneval_plus_mini(set_cover_info: Dict[str, List[str]], mini_path: str):
new_problems = []
for task_id in task_ids:
otask = problems[task_id]
task = {
"task_id": task_id,
"prompt": otask["prompt"],
"contract": otask["contract"],
"canonical_solution": otask["canonical_solution"],
"entry_point": otask["entry_point"],
"base_input": otask["base_input"],
"plus_input": [],
"atol": otask["atol"],
}
for plus_test in set_cover_info[task_id]:
index = int(plus_test.split("_")[-1])
task["plus_input"].append(otask["plus_input"][index])
new_problems.append(deepcopy(task))
write_jsonl(os.path.join(mini_path, "HumanEvalPlus-Mini.jsonl"), new_problems)
def main(flags):
coverage_dir = os.path.join(flags.report_dir, "coverage_cache")
mutation_dir = os.path.join(flags.report_dir, "mutation_cache")
sample_dir = os.path.join(flags.report_dir, "sample_cache")
os.makedirs(flags.report_dir, exist_ok=True)
exclude_model: str = flags.model
if exclude_model.endswith("b"): # format: model_name + parameter size
exclude_model = "".join(exclude_model.split("-")[:-1])
coverage_set_cover = get_coverage_set_cover(
coverage_dir, exclude_model, flags.dataset
)
mutation_set_cover = get_mutation_set_cover(
mutation_dir, exclude_model, flags.dataset
)
sample_set_cover = get_sample_set_cover(
sample_dir, flags.sample_eval_dir, exclude_model, flags.dataset
)
merged_set_cover = merge_set_cover(
coverage_set_cover, mutation_set_cover, sample_set_cover
)
if flags.model != "ALL":
final_report = dict()
# Stage 1: Coverage min set cover
final_report["coverage"] = gen_report(
coverage_set_cover, flags.sample_eval_dir, flags.model
)
# Stage 2: Mutation min set cover
final_report["mutation"] = gen_report(
mutation_set_cover, flags.sample_eval_dir, flags.model
)
# Stage 3: Sampling min set cover
final_report["sample"] = gen_report(
sample_set_cover, flags.sample_eval_dir, flags.model
)
# Stage 4: All
final_report["full"] = gen_report(
merged_set_cover, flags.sample_eval_dir, flags.model
)
with open(
os.path.join(flags.report_dir, f"report_{flags.model}.json"), "w"
) as f:
json.dump(final_report, f, indent=4)
else:
dump_humaneval_plus_mini(merged_set_cover, flags.mini_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True, type=str, help="Model for testing")
parser.add_argument("--dataset", type=str, choices=["humaneval", "mbpp"])
parser.add_argument(
"--report_dir", type=str, help="Path to JSON report and cache files"
)
parser.add_argument(
"--sample_eval_dir", type=str, help="Path to sample evaluation files"
)
parser.add_argument("--mini_path", type=str, help="Path to Mini Dataset")
args = parser.parse_args()
global_util_init(args.dataset)
main(args)
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