Merge pull request #105 from deepseek-ai/ydj/leetcode

Update leetcode contest evaluation
This commit is contained in:
DejianYang 2024-01-26 14:14:37 +08:00 committed by GitHub
commit e7841b5b1b
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
10 changed files with 1664 additions and 1 deletions

1
.gitignore vendored
View File

@ -1,2 +1,3 @@
__pycache__/
Evaluation/MBPP/eval_instruct.sh
Evaluation/LeetCode/output/

File diff suppressed because one or more lines are too long

File diff suppressed because one or more lines are too long

View File

@ -0,0 +1,86 @@
import re
import json
from pathlib import Path
from collections import defaultdict
from human_eval.evaluation import evaluate_functional_correctness
version = "20240121-Jul"
DATA_DIR = Path(__file__).parent / "data"
def extract_python_code(generation: str):
generation = generation.replace("[PYTHON]", '```python').replace("[/PYTHON]", '```')
if '```python' in generation:
p_code = re.compile(r'```python\n(.*?)\n```', flags=re.DOTALL)
code_block = p_code.findall(generation)[0]
return code_block
else:
codelist = re.split("\ndef|\nclass|\nif|\n#|\nprint", generation)
return codelist[0]
def evaluate_main(generation_path: str, result_path: str, temp_dir: str):
problem_path = (DATA_DIR / f"{version}.jsonl").as_posix()
print(problem_path)
problems = [json.loads(line) for line in open(problem_path, 'r')]
id2problems = { x['task_id']: x for x in problems }
results = [json.loads(line) for line in open(generation_path, 'r')]
for result in results:
if 'task_id' not in result:
result['task_id'] = problems[result['index']]['task_id']
if 'generation' not in result:
try:
if 'output' not in result:
result['output'] = result['response']
if result['output'].startswith("\n "):
func_code = extract_python_code(result['prompt_sft']).strip()
result['generation'] = func_code + '\n' + result['output']
else:
result['generation'] = extract_python_code(result['output'])
except:
result['generation'] = result['output']
with open(result_path, 'w') as fr:
for result in results:
fr.write(json.dumps(result) + "\n")
score = evaluate_functional_correctness(
input_file=result_path,
tmp_dir=temp_dir,
problem_file=problem_path,
result_path=result_path
)
hardness_results = defaultdict(int)
for result in [json.loads(line) for line in open(result_path, 'r')]:
problem = id2problems[result['task_id']]
hardness = problem['meta']['difficulty']
hardness_results[hardness] += 1
hardness_results[hardness + "_correct"] += result['passed']
print("="*100)
print("Evaluate {} over.".format(generation_path))
print("Pass@1: {:.3f}".format(score["pass@1"]))
for key in ["Easy", "Medium", "Hard"]:
if key.endswith("_correct"):
continue
acc = hardness_results[key+"_correct"] / hardness_results[key]
print("{}: {:.3f}({}/{})".format(key, acc, hardness_results[key+"_correct"], hardness_results[key]))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--generation_path", type=str, required=True)
parser.add_argument("--result_path", type=str)
parser.add_argument("--temp_dir", type=str, default="output/temp")
args = parser.parse_args()
if args.result_path is None:
args.result_path = args.generation_path.replace(".jsonl", "_result.jsonl")
evaluate_main(args.generation_path, args.result_path, temp_dir=args.temp_dir)
pass

View File

@ -0,0 +1,49 @@
from typing import Iterable, Dict
import gzip
import json
import os
ROOT = os.path.dirname(os.path.abspath(__file__))
HUMAN_EVAL = os.path.join(ROOT, "..", "data", "HumanEval.jsonl.gz")
def read_problems(evalset_file: str = HUMAN_EVAL) -> Dict[str, Dict]:
return {task["task_id"]: task for task in stream_jsonl(evalset_file)}
def stream_jsonl(filename: str) -> Iterable[Dict]:
"""
Parses each jsonl line and yields it as a dictionary
"""
if filename.endswith(".gz"):
with open(filename, "rb") as gzfp:
with gzip.open(gzfp, 'rt') as fp:
for line in fp:
if any(not x.isspace() for x in line):
yield json.loads(line)
else:
with open(filename, "r") as fp:
for line in fp:
if any(not x.isspace() for x in line):
yield json.loads(line)
def write_jsonl(filename: str, data: Iterable[Dict], append: bool = False):
"""
Writes an iterable of dictionaries to jsonl
"""
if append:
mode = 'ab'
else:
mode = 'wb'
filename = os.path.expanduser(filename)
if filename.endswith(".gz"):
with open(filename, mode) as fp:
with gzip.GzipFile(fileobj=fp, mode='wb') as gzfp:
for x in data:
gzfp.write((json.dumps(x) + "\n").encode('utf-8'))
else:
with open(filename, mode) as fp:
for x in data:
fp.write((json.dumps(x) + "\n").encode('utf-8'))

View File

@ -0,0 +1,304 @@
import os
import json
import gzip
import numpy as np
import itertools
from typing import *
from tqdm.auto import tqdm
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from human_eval.data import stream_jsonl
from human_eval.execution import check_correctness
IMPORT_HELPER = {
"python": [
"import math",
"import re",
"import sys",
"import copy",
"import datetime",
"import itertools",
"import collections",
"import heapq",
"import functools",
"import hashlib",
"import numpy",
"import numpy as np",
"import string",
"from typing import *",
"from collections import *",
"from functools import *"
],
"go" : [
"math",
"strings",
"fmt",
"strconv",
"time",
"bytes",
"regexp",
"sort",
"math/rand",
"crypto/md5",
],
"cpp" : [
"#include<stdlib.h>",
"#include<algorithm>",
"#include<math.h>",
"#include<stdio.h>",
"#include<vector>",
"#include<string>",
"#include<climits>",
"#include<cstring>",
"#include<iostream>",
],
}
LANGUAGE_NAME = {
"cpp" : "CPP",
"go" : "Go",
"java" : "Java",
"js" : "JavaScript",
"python": "Python",
}
def read_dataset(
data_file: str = None,
dataset_type: str = "humaneval",
num_shot=None,
) -> Dict:
if num_shot is not None:
print(f"{num_shot}-shot setting...")
if "humaneval" in dataset_type.lower():
if data_file is None:
current_path = os.path.dirname(os.path.abspath(__file__))
data_file = os.path.join(current_path, "..", "humaneval-x", "python", "data", "humaneval_python.jsonl.gz")
dataset = {task["task_id"]: task for task in stream_jsonl(data_file)}
else:
raise f"Dataset: {dataset_type} not supported."
return dataset
def estimate_pass_at_k(
num_samples: Union[int, List[int], np.ndarray],
num_correct: Union[List[int], np.ndarray],
k: int
) -> np.ndarray:
"""
Estimates pass@k of each problem and returns them in an array.
"""
def estimator(n: int, c: int, k: int) -> float:
"""
Calculates 1 - comb(n - c, k) / comb(n, k).
"""
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
if isinstance(num_samples, int):
num_samples_it = itertools.repeat(num_samples, len(num_correct))
else:
assert len(num_samples) == len(num_correct)
num_samples_it = iter(num_samples)
return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])
def process_humaneval_test(sample, problems, example_test=False, is_mbpp=False, language="python"):
task_id = sample["task_id"]
if is_mbpp:
return sample["generation"] + "\n" + "\n".join(problems[task_id]["test"])
#language = task_id.split("/")[0].lower()
prompt = sample.get("prompt", "")
if example_test and "example_test" in problems[task_id] and problems[task_id]["example_test"] != "":
test = problems[task_id]["example_test"]
else:
test = problems[task_id]["test"]
code = sample["generation"]
# Pre-process for different languages
if language == "python":
'''code_ = []
for line in code.split("\n"):
if (len(line.strip()) > 0 and line[0] != ' ' and line[0] != '\t'):
break
code_.append(line)
code = "\n".join(code_)'''
test_setup = "\n".join(IMPORT_HELPER["python"]) + "\n"
test_string = test_setup + code + "\n" + test + "\n"
elif language == "cpp":
test_set_up = ""
for s in IMPORT_HELPER["cpp"]:
if s not in prompt:
test_set_up += s + "\n"
test_string = test_set_up + "\n" + code + "\n" + test
elif language == "java":
test_string = code + "\n" + test
elif language in ["js", "javascript", "ts", "cs", "sh"]:
test_string = code + "\n" + test
elif language == "go":
import_string = problems[task_id]["import"]
prompt = prompt.replace(import_string, "")
if example_test and "example_test" in problems[task_id]:
test = problems[task_id]["example_test"]
else:
test = problems[task_id]["test"]
test_setup = problems[task_id]["test_setup"]
other_pkgs = []
for pkg in IMPORT_HELPER["go"]:
if pkg not in test_setup:
p = pkg.split("/")[-1]
if p + "." in code:
other_pkgs.append(f"\"{pkg}\"")
if other_pkgs:
import_other_pkgs = "import (\n" + " ".join([p + "\n" for p in other_pkgs]) + ")"
test_string = test_setup + "\n" + import_other_pkgs + "\n" + prompt + code + "\n" + test
else:
test_string = test_setup + "\n" + prompt + code + "\n" + test
elif language == "rust":
main = "\nfn main(){ \n } \n"
declaration = problems[task_id]["declaration"]
test_string = main + declaration + prompt + code + test
elif language == "php":
test_string = code + "\n" + test + "?>"
return test_string
def stream_jsonl_all(filename: str) -> Iterable[Dict]:
results = []
if filename.endswith(".gz"):
fp = gzip.open(open(filename, "rb"), "rt")
else:
fp = open(filename, "r")
for line in fp:
if any(not x.isspace() for x in line):
results.append(json.loads(line))
fp.close()
return results
def evaluate_functional_correctness(
input_file: str = None,
tmp_dir: str = "./",
n_workers: int = 32,
timeout: float = 10.0,
problem_file: str = "../data/humaneval_python.jsonl.gz",
result_path: str = None,
k: List[int] = [1, 10, 100],
test_groundtruth: bool = False,
example_test: bool = False,
is_mbpp: bool = False,
language: str = "python",
):
if example_test:
print("Example test...")
problems = read_dataset(problem_file, dataset_type="humaneval")
sample_jsonl = stream_jsonl_all(input_file)
with ThreadPoolExecutor(max_workers=n_workers) as executor:
futures = []
completion_id = Counter()
n_samples = 0
results = defaultdict(list)
if test_groundtruth:
print("Testing ground truth...")
for sample in tqdm(problems.values()):
task_id = sample["task_id"]
lang = task_id.split("/")[0].lower()
if lang == "javascript":
lang = "js"
tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation")
sample["generation"] = sample["canonical_solution"]
sample["test_code"] = process_humaneval_test(sample, problems, example_test, language)
if sample["test_code"] is None:
continue
args = (task_id, sample, lang, timeout, tmp_dir_, completion_id[task_id])
future = executor.submit(check_correctness, *args)
futures.append(future)
completion_id[task_id] += 1
n_samples += 1
else:
print("Reading Samples...")
id2samples = {}
for sample in tqdm(sample_jsonl):
task_id = sample["task_id"]
if not is_mbpp:
lang = language
if not is_mbpp and lang == "javascript":
lang = "js"
if is_mbpp:
lang = "python"
tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation")
sample["task_id"] = task_id
sample["test_code"] = process_humaneval_test(sample, problems, example_test, is_mbpp, language)
if sample["test_code"] is None:
continue
if "completion_id" in sample:
completion_id_ = sample["completion_id"]
else:
completion_id_ = completion_id[task_id]
args = (task_id, sample, lang, timeout, tmp_dir_, completion_id_)
id2samples[(task_id, completion_id_)] = sample
future = executor.submit(check_correctness, *args)
futures.append(future)
completion_id[task_id] += 1
n_samples += 1
if len(completion_id) == len(problems):
evaluate_pass_at_k = True
else:
evaluate_pass_at_k = False
print("Running test suites...")
sample_with_results = []
for future in tqdm(as_completed(futures), total=len(futures)):
result = future.result()
results[result["task_id"]].append((result["completion_id"], result))
sample = id2samples[(result["task_id"], result["completion_id"])]
sample_with_results.append({
'task_id': result['task_id'],
'completion_id': result["completion_id"],
'passed': result['passed'],
'generation': sample['generation']
})
for key in sample:
if key not in sample_with_results[-1]:
sample_with_results[-1][key] = sample[key]
# Calculate pass@k.
total, correct = [], []
for result in results.values():
passed = [r[1]["passed"] for r in result]
total.append(len(passed))
correct.append(sum(passed))
total = np.array(total)
correct = np.array(correct)
if evaluate_pass_at_k:
ks = k
pass_at_k = {
f"pass@{k}": estimate_pass_at_k(total, correct, k).mean()
for k in ks if (total >= k).all()
}
print(pass_at_k)
else:
print("Total:", np.sum(total))
print("Correct:", np.sum(correct))
if result_path is not None:
with open(result_path, 'w', encoding='utf-8') as fw:
for sample_with_result in sample_with_results:
fw.write(json.dumps(sample_with_result) + '\n')
print("Save evaluation results to\n{}".format(result_path))
return pass_at_k

View File

@ -0,0 +1,731 @@
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import random
import subprocess
import tempfile
import gzip
import json
from typing import *
import traceback
java_exec = ""
node_exec = ""
tsc_exec = ""
go_exec = ""
php_exec = ""
cs_exec = ""
def check_correctness(
task_id: str,
sample: dict,
language_type: str,
timeout: float = 3.0,
tmp_dir: str = None,
completion_id: Optional[int] = None,
) -> Dict:
"""
Evaluates the functional correctness of a completion by running the test
suite provided in the problem.
"""
def unsafe_execute(tmp_dir):
random_id = random.randint(1, 100000)
if "python" in language_type.lower():
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
rmtree = shutil.rmtree
rmdir = os.rmdir
chdir = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
try:
exec_globals = {}
with swallow_io():
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec(sample["test_code"], exec_globals)
result.append("passed")
except TimeoutException:
result.append("timed out")
except AssertionError as e:
result.append(f"failed: AssertionError")
except BaseException as e:
result.append(f"failed: {e}")
#print(sample["test_code"])
#print(result)
# Needed for cleaning up.
shutil.rmtree = rmtree
os.rmdir = rmdir
os.chdir = chdir
elif "go" in language_type.lower():
assert tmp_dir is not None, "Go should be evaluated in a dir where necessary module files installed."
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
origin_path = os.getcwd()
os.chdir(tmp_dir)
open(f"main_test.go", 'w').write(sample["test_code"])
try:
exec_result = None
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run([f"{go_exec}go", "test", f"-timeout={timeout}s", "main_test.go"], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "js" in language_type.lower():
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
origin_path = os.getcwd()
os.chdir(tmp_dir)
open(f"test.js", 'w').write(sample["test_code"])
try:
exec_result = None
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run([f"{node_exec}node", "test.js"], timeout=timeout, capture_output=True)
if exec_result.stderr.decode():
err = exec_result.stderr.decode()
result.append(f"failed: {err}")
elif exec_result.stdout.decode():
err = exec_result.stdout.decode()
result.append(f"failed: {err}")
else:
result.append("passed")
except TimeoutException:
result.append("timed out")
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "cpp" in language_type.lower():
import os
import shutil
origin_path = os.getcwd()
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"test.cpp", 'w').write(sample["test_code"])
if "162" in task_id:
compilation_result = subprocess.run(["/usr/bin/g++", "-std=c++17", "test.cpp", "-lcrypto", "-lssl"],
timeout=timeout,
capture_output=True)
else:
compilation_result = subprocess.run(["/usr/bin/g++", "-std=c++17", "test.cpp"], timeout=timeout,
capture_output=True)
if compilation_result.returncode != 0:
if compilation_result.stderr:
err = compilation_result.stderr.decode()
else:
err = compilation_result.stdout.decode()
result.append(f"failed: compilation error: {err}")
else:
try:
exec_result = None
with time_limit(timeout):
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
exec_result = subprocess.run(["./a.out"], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
#print(result[-1])
#print(sample["test_code"])
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "php" in language_type.lower():
import os
import shutil
origin_path = os.getcwd()
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"test.php", 'w').write(sample["test_code"])
try:
exec_result = None
with time_limit(timeout):
cmd = f"{php_exec}php -f test.php"
exec_result = subprocess.run(cmd, timeout=timeout, capture_output=True, shell=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
print(result[-1])
print(sample["test_code"])
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "sh" in language_type.lower():
import os
import shutil
origin_path = os.getcwd()
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"test.sh", 'w').write(sample["test_code"])
try:
exec_result = None
with time_limit(timeout):
cmd = "/bin/bash test.sh"
exec_result = subprocess.run(cmd, timeout=10, capture_output=True, shell=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
#print(result[-1])
#print(sample["test_code"])
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "ts" in language_type.lower():
import os
import shutil
origin_path = os.getcwd()
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
env = {"PATH": f"{node_exec}:" + subprocess.os.environ["PATH"]}
open(f"test.ts", 'w').write(sample["test_code"])
cmd = f"{tsc_exec}tsc test.ts --target ES2015 --lib ES2015,DOM"
compilation_result = subprocess.run(cmd, timeout=timeout, capture_output=True, env=env, shell=True)
if compilation_result.returncode != 0:
if compilation_result.stderr:
err = compilation_result.stderr.decode()
else:
err = compilation_result.stdout.decode()
result.append(f"failed: compilation error: {err}")
else:
try:
exec_result = None
with time_limit(timeout):
exec_result = subprocess.run([f"{node_exec}node", "test.js"], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
if result[-1] != "passed":
env = {"PATH": f"{node_exec}:" + subprocess.os.environ["PATH"]}
cmd = f"{tsc_exec}tsc test.ts"
compilation_result = subprocess.run(cmd, timeout=timeout, capture_output=True, env=env, shell=True)
if compilation_result.returncode != 0:
if compilation_result.stderr:
err = compilation_result.stderr.decode()
else:
err = compilation_result.stdout.decode()
result[-1] = f"failed: compilation error: {err}"
else:
try:
exec_result = None
with time_limit(timeout):
exec_result = subprocess.run([f"{node_exec}node", "test.js"], timeout=timeout, capture_output=True)
if exec_result.returncode == 0:
result[-1] = "passed"
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result[-1] = f"failed: {err}"
except TimeoutException:
result[-1] = "timed out"
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "cs" in language_type.lower():
import os
import shutil
origin_path = os.getcwd()
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
os.chdir(tmp_dir)
open(f"Program.cs", 'w').write(sample["test_code"])
cmd = f"{cs_exec}mcs -d:DEBUG Program.cs"
compilation_result = subprocess.run(cmd, shell=True, capture_output=True)
if compilation_result.returncode != 0:
if compilation_result.stderr:
err = compilation_result.stderr.decode()
else:
err = compilation_result.stdout.decode()
result.append(f"failed: compilation error: {err}")
else:
try:
exec_result = None
cmd = f"{cs_exec}mono Program.exe"
env = dict(MONO_TRACE_LISTENER="Console.Error")
with time_limit(timeout):
exec_result = subprocess.run(cmd, timeout=timeout, shell=True, capture_output=True, env=env)
if "Fail" not in exec_result.stderr.decode():
result.append("passed")
else:
if exec_result.stderr:
try:
err = exec_result.stderr.decode()
except:
err = exec_result.stderr
else:
try:
err = exec_result.stdout.decode()
except:
err = exec_result.stdout
result.append(f"failed: {err}")
except TimeoutException:
result.append("timed out")
except Exception as e:
result.append(f"failed: {e}")
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
elif "rust" in language_type.lower():
import os
WD: str = os.path.dirname(os.path.abspath(__file__))
RUST_DIR: str = os.path.join(WD, "rust")
RUST_SRC: str = os.path.join(RUST_DIR, "src")
RUST_BIN: str = os.path.join(RUST_SRC, "bin")
RUST_TMP_DIR: str = os.path.join(RUST_DIR, "tmp")
RUST_LOGS: str = os.path.join(RUST_TMP_DIR, "logs")
RUST_EXT: str = ".rs"
# Create mandatory tmp directories
os.makedirs(RUST_TMP_DIR, exist_ok=True)
os.makedirs(RUST_LOGS, exist_ok=True)
os.makedirs(RUST_SRC, exist_ok=True)
os.makedirs(RUST_BIN, exist_ok=True)
with tempfile.NamedTemporaryFile(dir = RUST_BIN, delete=False) as f:
#temporal file name
file_prefix = sample["task_id"].lower().replace("/", "_")
file_name:str = file_prefix +RUST_EXT
os.rename(f.name, os.path.join(RUST_BIN, file_name))
# Sample to pure Rust function
rust_code: str = sample["test_code"]
# dump the rust source code in the target temporal file
f.write(rust_code.encode('utf-8'))
# Proceed towards Rust binaries compilation. Therefore move to Rust module root dir.
os.chdir(RUST_DIR)
# Two possible outcomes
# Pass OR Fail compilation
log_filename: str = file_prefix + ".jsonl"
log_path: str = os.path.join(RUST_LOGS, log_filename)
cargo_check: str = "cargo check --bin " + file_prefix + " --message-format json >> " + log_path
# Compilation build status
returned_val_compilation: int
# Overwrite file content
if os.path.exists(log_path):
if(file_size := os.path.getsize(log_path)) >= 0:
os.remove(log_path)
returned_val_compilation = os.system(cargo_check)
else:
returned_val_compilation = os.system(cargo_check)
# 0 means success
if returned_val_compilation == 0:
#Execution pipeline
cargo_test: str = "cargo test --bin " +file_prefix+ " --message-format json >> " + log_path
returned_val_execution = os.system(cargo_test)
if returned_val_execution == 0:
result.append("passed")
else:
result.append(f"failed: execution error")
else:
result.append(f"failed: compilation error")
elif "java" in language_type.lower():
assert tmp_dir is not None, "Java should be evaluated in a temporary dir."
import os
import shutil
if "tmp" not in tmp_dir:
tmp_dir = os.path.join(tmp_dir, "tmp")
tmp_dir = os.path.join(tmp_dir, f"{task_id.replace('/', '-')}-{random_id}")
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
open(os.path.join(tmp_dir, "Problem.java"), 'w').write(sample["test_code"])
origin_path = os.getcwd()
os.system(f"cp ./javatuples-1.2.jar {tmp_dir}/")
os.chdir(tmp_dir)
res = "failed: unknown error"
compile_returncode = -1
for _ in range(5):
try:
cmd = f"{java_exec}javac -cp javatuples-1.2.jar Problem.java"
compilation_result = subprocess.run(cmd, timeout=60, capture_output=True, shell=True)
compile_returncode = compilation_result.returncode
break
except subprocess.TimeoutExpired as e:
continue
if compile_returncode != 0:
res = "failed: compilation error"
else:
exec_result = None
try:
# WARNING
# This program exists to execute untrusted model-generated code. Although
# it is highly unlikely that model-generated code will do something overtly
# malicious in response to this test suite, model-generated code may act
# destructively due to a lack of model capability or alignment.
# Users are strongly encouraged to sandbox this evaluation suite so that it
# does not perform destructive actions on their host or network.
# Once you have read this disclaimer and taken appropriate precautions,
# uncomment the following line and proceed at your own risk:
cmd = f"{java_exec}java -ea -cp .:javatuples-1.2.jar Problem"
exec_result = subprocess.run(cmd, timeout=timeout, capture_output=True, shell=True)
if exec_result.returncode == 0:
res = "passed"
elif exec_result.returncode == 1:
if "AssertionError" in exec_result.stderr.decode('unicode-escape'):
res = "failed: wrong answer"
else:
res = f"failed: {exec_result.stderr.decode()}"
except subprocess.TimeoutExpired as e:
res = "time out"
except BaseException as e:
res = f"failed: {e}"
result.append(res)
os.chdir(origin_path)
shutil.rmtree(tmp_dir)
manager = multiprocessing.Manager()
result = manager.list()
p = multiprocessing.Process(target=unsafe_execute, args=(tmp_dir,))
p.start()
p.join(timeout=timeout + 1)
if p.is_alive():
p.kill()
if not result:
result.append("timed out")
return {
"task_id" : task_id,
"completion_id": completion_id,
"result" : result[0],
"passed" : result[0] == "passed",
"finish" : -1 if "finish" not in sample else sample["finish"],
"code" : sample["test_code"]
}
# Copyright (c) OpenAI (https://openai.com)
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# ============================================================================
@contextlib.contextmanager
def time_limit(seconds: float):
def signal_handler(signum, frame):
raise TimeoutException("Timed out!")
signal.setitimer(signal.ITIMER_REAL, seconds)
signal.signal(signal.SIGALRM, signal_handler)
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL, 0)
@contextlib.contextmanager
def swallow_io():
stream = WriteOnlyStringIO()
with contextlib.redirect_stdout(stream):
with contextlib.redirect_stderr(stream):
with redirect_stdin(stream):
yield
@contextlib.contextmanager
def create_tempdir():
with tempfile.TemporaryDirectory() as dirname:
with chdir(dirname):
yield dirname
class TimeoutException(Exception):
pass
class WriteOnlyStringIO(io.StringIO):
""" StringIO that throws an exception when it's read from """
def read(self, *args, **kwargs):
raise IOError
def readline(self, *args, **kwargs):
raise IOError
def readlines(self, *args, **kwargs):
raise IOError
def readable(self, *args, **kwargs):
""" Returns True if the IO object can be read. """
return False
class redirect_stdin(contextlib._RedirectStream): # type: ignore
_stream = 'stdin'
@contextlib.contextmanager
def chdir(root):
if root == ".":
yield
return
cwd = os.getcwd()
os.chdir(root)
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(cwd)
def reliability_guard(maximum_memory_bytes: Optional[int] = None):
"""
This disables various destructive functions and prevents the generated code
from interfering with the test (e.g. fork bomb, killing other processes,
removing filesystem files, etc.)
WARNING
This function is NOT a security sandbox. Untrusted code, including, model-
generated code, should not be blindly executed outside of one. See the
Codex paper for more information about OpenAI's code sandbox, and proceed
with caution.
"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))
resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))
if not platform.uname().system == 'Darwin':
resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))
faulthandler.disable()
import builtins
builtins.exit = None
builtins.quit = None
import os
os.environ['OMP_NUM_THREADS'] = '1'
os.kill = None
os.system = None
os.putenv = None
os.remove = None
os.removedirs = None
os.rmdir = None
os.fchdir = None
os.setuid = None
os.fork = None
os.forkpty = None
os.killpg = None
os.rename = None
os.renames = None
os.truncate = None
os.replace = None
os.unlink = None
os.fchmod = None
os.fchown = None
os.chmod = None
os.chown = None
os.chroot = None
os.fchdir = None
os.lchflags = None
os.lchmod = None
os.lchown = None
os.getcwd = None
os.chdir = None
import shutil
shutil.rmtree = None
shutil.move = None
shutil.chown = None
import subprocess
subprocess.Popen = None # type: ignore
__builtins__['help'] = None
import sys
sys.modules['ipdb'] = None
sys.modules['joblib'] = None
sys.modules['resource'] = None
sys.modules['psutil'] = None
sys.modules['tkinter'] = None

View File

@ -0,0 +1,48 @@
## 1. Introduction
We construct the LeetCode Contest benchmark to to further validate the model's capability in real-world programming problems.
[LeetCode](https://leetcode.com/) presents competition-level problems, offering significant challenges that test the model's problem understanding and code generation skills. We collected the latest problems from LeetCode Contests to prevent the appearance of both the problems or their solutions in our pre-training data. A total of `180` problems were collected from July 2023 to January 2024. For each problem, we collected `100` test cases. The data format is the same as human-eval. For more details, please refer to [leetcode_contest_data](./data/20240121-Jul.jsonl).
## 2. Evaluation
Please follow the following two steps to evaluate the model's performance on our LeetCode Contest benchmark:
1. Run `vllm_inference.py` to get generation results.
```bash
cd Evaluation/LeetCode
# Set the model or path here
MODEL="deepseek-ai/deepseek-coder-7b-instruct"
python vllm_inference.py --model_name_or_path $MODEL --saved_path output/20240121-Jul.deepseek-coder-7b-instruct.jsonl
```
If you want to evaluate the model with COT, please add `--cot` to the command:
```bash
python vllm_inference.py --model_name_or_path $MODEL --saved_path output/20240121-Jul.deepseek-coder-7b-instruct.jsonl --cot
```
2. Run `evaluate_leetcode.py` to get evaluation results.
```bash
python evaluate_leetcode.py --generation_path output/20240121-Jul.deepseek-coder-7b-instruct.jsonl --result_path output/20240121-Jul.deepseek-coder-7b-instruct.result.jsonl
```
## 3. Experimental Results
We report experimental results here:
| Model | Size | Easy (45) | Medium (91) | Hard (44) | Overall(180) |
|-----------------------------|------|-----------|-------------|-----------|--------------|
| WizardCoder-V1.0 | 15B | 17.8% | 1.1% | 0.0% | 5.0% |
| CodeLlama-Instruct | 34B | 24.4% | 4.4% | 4.5% | 9.4% |
| Phind-CodeLlama-V2 | 34B | 26.7% | 8.8% | 9.1% | 13.3% |
| | | | |
| GPT-3.5-Turbo | - | 46.7% | 15.4 % | 15.9% | 23.3% |
| GPT-3.5-Turbo + CoT | - | 42.2% | 15.4% | 20.5% | 23.3% |
| GPT-4-Turbo | - | 73.3% | 31.9% | 25.0% | 40.6% |
| GPT-4-Turbo + CoT | - | 71.1% | 35.2% | 25.0% | 41.8% |
| | | | |
| DeepSeek-Coder-Instruct | 1.3B | 22.2% | 1.1% | 4.5% | 7.2% |
| DeepSeek-Coder-Instruct + CoT | 1.3B | 22.2% | 2.2% | 2.3% | 7.2% |
| DeepSeek-Coder-Instruct | 6.7B | 44.4% | 12.1% | 9.1% | 19.4% |
| DeepSeek-Coder-Instruct + CoT | 6.7B | 44.4% | 17.6% | 4.5% | 21.1% |
| DeepSeek-Coder-Instruct | 33B | 57.8% | 22.0% | 9.1% | 27.8% |
| DeepSeek-Coder-Instruct + CoT | 33B | 53.3% | 25.3% | 11.4% | 28.9% |

View File

@ -0,0 +1,84 @@
from vllm import LLM, SamplingParams
import json
from transformers import AutoTokenizer
from pathlib import Path
version = "20240121-Jul"
def generate_batch(examples, tokenizer, llm, model: str):
stop = None
if model == 'deepseekcoder-instruct':
prompts = [
tokenizer.apply_chat_template([{'role': 'user', 'content': ex['prompt_sft'] }], tokenize=False, add_generation_prompt=True)
for ex in examples
]
else:
raise NotImplementedError()
# Create a sampling params object.
sampling_params = SamplingParams(
temperature=0.0,
# top_p=0.95,
max_tokens=1024,
stop=stop
)
print("Sample prompt: {}".format(prompts[0]))
outputs = llm.generate(prompts, sampling_params)
for i in range(len(examples)):
examples[i]['output'] = outputs[i].outputs[0].text
return examples
def generate_main(data_path: str, model_name_or_path: str, saved_path: str, model_type: str='deepseekcoder-instruct', cot: bool=False):
examples = [json.loads(x) for x in open(data_path).readlines()]
def _convert_for_sft(ex):
ex['prompt_sft'] = ex["prompt_sft"] + "\nYou need first write a step-by-step outline and then write the code."
return ex
if cot:
examples = [_convert_for_sft(x) for x in examples]
saved_path = saved_path.replace(".jsonl", ".cot.jsonl")
print(model_type)
print("Model `{}`, COT = {}:{}".format(model_type, cot, model_name_or_path))
print("Saved path: {}".format(saved_path))
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
print("load tokenizer {} from {} over.".format(tokenizer.__class__, model_name_or_path))
# Create an LLM.
llm = LLM(
model=model_name_or_path,
pipeline_parallel_size=1,
tensor_parallel_size=8,
max_num_seqs=512,
max_num_batched_tokens=8192,
max_model_len=4096,
gpu_memory_utilization=0.85,
trust_remote_code=True
)
generated_examples = generate_batch(examples, tokenizer, llm, model_type)
print("Generate all over!!!")
with open(saved_path, 'w', encoding='utf-8') as fw:
for ex in generated_examples:
fw.write(json.dumps(ex) + '\n')
print("Save {} processed examples into {} over!".format(len(generated_examples), saved_path))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', type=str, default=Path(__file__).parent.joinpath(f"data/{version}.jsonl").as_posix())
parser.add_argument('--model_name_or_path', type=str, default='deepseek-ai/deepseek-coder-7b-instruct')
parser.add_argument('--saved_path', type=str, default=f'output/{version}.deepseek-coder-7b-instruct.jsonl')
parser.add_argument('--cot', action='store_true', default=False)
args = parser.parse_args()
generate_main(
data_path=args.data_path,
model_name_or_path=args.model_name_or_path,
saved_path=args.saved_path,
cot=args.cot,
)