import gradio as gr import json import logging import multiprocessing import os import pickle import threading import time from collections import Counter, defaultdict from concurrent.futures import ProcessPoolExecutor, as_completed, wait, FIRST_COMPLETED from datetime import datetime from typing import Any, Dict, List, Tuple from warnings import warn import gc import numpy as np from huggingface_hub import HfApi from bigcodebench.data import get_bigcodebench, get_bigcodebench_hash, load_solutions from bigcodebench.data.utils import CACHE_DIR from bigcodebench.eval import PASS, compatible_eval_result, estimate_pass_at_k, untrusted_check from bigcodebench.gen.util import trusted_check from apscheduler.schedulers.background import BackgroundScheduler REPO_ID = "bigcode/bigcodebench-evaluator" HF_TOKEN = os.environ.get("HF_TOKEN", None) API = HfApi(token=HF_TOKEN) Result = Tuple[str, List[bool]] def get_groundtruth(n_workers, problems, hashcode, check_gt_only, max_as_limit, max_data_limit, max_stack_limit, min_time_limit): cache_file = os.path.join(CACHE_DIR, f"{hashcode}.pkl") if os.path.exists(cache_file): with open(cache_file, "rb") as f: return pickle.load(f) os.makedirs(CACHE_DIR, exist_ok=True) tbegin = time.time() with ProcessPoolExecutor(max_workers=n_workers) as executor: futures = [] n_samples = 0 expected_time = dict() for problem in problems.values(): args = ( problem["complete_prompt"] + "\n" + problem["canonical_solution"], problem["test"], problem["task_id"], max_as_limit, max_data_limit, max_stack_limit, min_time_limit, ) futures.append(executor.submit(trusted_check, *args)) n_samples += 1 for future in as_completed(futures): result = future.result() expected_time[result["task_id"]] = result["time"] if any(expected_time.values()): with open(cache_file, "wb") as f: pickle.dump(expected_time, f) return expected_time def check_correctness( completion_id: int, problem: Dict[str, Any], solution: str, max_as_limit: float, max_data_limit: float, max_stack_limit: float, identifier=None, min_time_limit: float = 0.1, gt_time_limit: float = 2.0, ) -> Dict[str, Result]: ret = { "completion_id": completion_id, "task_id": problem["task_id"], "_identifier": identifier, "solution": solution, } ret["base"] = untrusted_check( solution, problem["test"], problem["entry_point"], max_as_limit, max_data_limit, max_stack_limit, min_time_limit, gt_time_limit, ) return ret def evaluate( split: str, subset: str, samples: str, pass_k: str="1,5,10", parallel: int = -1, min_time_limit: float = 1, max_as_limit: int = 30 * 1024, max_data_limit: int = 30 * 1024, max_stack_limit: int = 10, check_gt_only: bool = False, no_gt: bool = False, ): pass_k = [int(k.strip()) for k in pass_k.split(',') if k.strip().isdigit()] if parallel < 1: n_workers = max(1, multiprocessing.cpu_count() // 2) else: n_workers = parallel if check_gt_only: samples = "__dummy__.jsonl" extra = subset + "_" if subset != "full" else "" problems = get_bigcodebench(subset=subset) dataset_hash = get_bigcodebench_hash(subset=subset) if not no_gt: expected_time = get_groundtruth(n_workers, problems, dataset_hash, check_gt_only, max_as_limit, max_data_limit, max_stack_limit, min_time_limit) else: expected_time = {task_id: None for task_id in problems} gt_pass_rate = np.mean([1 if v is not None else 0 for k, v in expected_time.items() if k in problems]) failed_tasks = [k for k, v in expected_time.items() if v is None and k in problems] pass_at_k = dict() results = { "date": datetime.now().strftime("%Y-%m-%d %H:%M"), "eval": {}, } if not check_gt_only: with ProcessPoolExecutor(max_workers=n_workers) as executor: futures = [] completion_id = Counter() n_samples = 0 eval_results = defaultdict(list) # task_id -> remainings = set() for sample in load_solutions(samples): task_id = sample["task_id"] if task_id not in problems: continue solution = ( sample["solution"] if "solution" in sample else problems[task_id]["complete_prompt"] + sample["completion"] ) if "sanitized-calibrated" in samples: solution = problems[task_id]["code_prompt"] + "\n pass\n" + solution remainings.add(sample["_identifier"]) args = ( completion_id[task_id], problems[task_id], solution, max_as_limit, max_data_limit, max_stack_limit, sample["_identifier"], min_time_limit, expected_time[task_id] if expected_time[task_id] else 20 ) futures.append(executor.submit(check_correctness, *args)) completion_id[task_id] += 1 n_samples += 1 assert n_samples == len(remainings), "Missing problems in unfinished" assert len(completion_id) == len(problems), "Missing problems in samples" for future in as_completed(futures): result = future.result() remainings.remove(result["_identifier"]) eval_results[result["task_id"]].append(result) del future, result gc.collect() # sort the results for each problem by completion_id for task_id, task_results in eval_results.items(): task_results.sort(key=lambda x: x["completion_id"]) results["eval"][task_id] = [] for res in task_results: stat, details = res["base"] results["eval"][task_id].append( { "task_id": task_id, "solution": res["solution"], "status": stat, "details": details, } ) # Calculate pass@k. total = np.array([len(r) for k, r in results["eval"].items() if k in problems]) base_correct = [] for key, res in results["eval"].items(): if key not in problems: continue bc = sum([r["status"] == PASS for r in res]) base_correct.append(bc) base_correct = np.array(base_correct) pass_at_k.update({ f"pass@{k}": estimate_pass_at_k(total, base_correct, k).mean() for k in pass_k if total.min() >= k }) del problems, futures gc.collect() pass_at_k["model"] = os.path.basename(samples).split("--bigcodebench-")[0] pass_at_k["split"] = split pass_at_k["subset"] = subset pass_at_k["calibrated"] = "sanitized-calibrated" in samples pass_at_k["gt_pass_rate"] = gt_pass_rate pass_at_k["failed_tasks"] = failed_tasks return results, pass_at_k # def run_gradio(): interface = gr.Interface( fn=evaluate, inputs=[ gr.Dropdown(["complete", "instruct"], label="BigCodeBench Split"), gr.Dropdown(["full", "hard"], label="BigCodeBench Subset"), gr.File(label="Samples Path (.jsonl)"), gr.Textbox(label="Pass k Values (comma-separated)", value="1,5,10"), gr.Slider(-1, multiprocessing.cpu_count(), step=1, label="Parallel Workers", value=-1), gr.Slider(0.1, 10, step=0.1, label="Min Time Limit", value=1), gr.Slider(1, 100 * 1024, step=1024, label="Max AS Limit", value=30 * 1024), gr.Slider(1, 100 * 1024, step=1024, label="Max Data Limit", value=30 * 1024), gr.Slider(1, 100, step=1, label="Max Stack Limit", value=10), gr.Checkbox(label="Check GT Only"), gr.Checkbox(label="No GT"), ], outputs=[ gr.JSON(label="Results"), gr.JSON(label="Eval Results"), ], # concurrency_limit=None ) interface.queue(default_concurrency_limit=None) def preload_gt(): evaluate(split="complete", subset="full", samples="", check_gt_only=True) evaluate(split="complete", subset="hard", samples="", check_gt_only=True) def restart_space(): logging.info(f"Restarting space with repo ID: {REPO_ID}") try: # Now restart the space API.restart_space(repo_id=REPO_ID, token=HF_TOKEN) logging.info("Space restarted successfully.") except Exception as e: logging.error(f"Failed to restart space: {e}") # if __name__ == "__main__": preload_gt() scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", hours=5) # Restart every 5hs scheduler.start() interface.launch(show_error=True)