-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathmp_test_case2code_with_exe.py
More file actions
148 lines (130 loc) · 5.64 KB
/
mp_test_case2code_with_exe.py
File metadata and controls
148 lines (130 loc) · 5.64 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
import os
import json
from tqdm import tqdm
from ast_utils import *
from exec_utils import *
import argparse
from multiprocessing import Pool, Process, Queue
import re
import sys
with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'sandbox_prefix.py'), 'r') as f:
code_template = f.read()
code_insert_pos = code_template.find('### FUNCTION & EX DEFINITION ###')
def run_worker(data, model_obj):
pred_code = unwrap_code(model_obj['completions'])
if '<|NONSTOP|>' in pred_code:
model_obj['is_correct'] = 'Error: nonstop'
return model_obj
func_name = data['func_name']
code_to_run = code_template.replace('### FUNCTION & EX DEFINITION ###', pred_code + '\n\n' + data['example_str'])
code_to_run = code_to_run.replace('target_function_XXX', func_name)
r = execute_code_wrapped(code_to_run, f'exec_{os.getpid()}.py', 10, add_guard=False)
model_obj['exec_raw_output'] = r['result']
model_obj['exec_status'] = r['status']
gold_exec_output = [ex['return'] if 'return' in ex else ex['error'] for ex in data['example_outputs']]
model_obj['gold_exec_output'] = gold_exec_output
output_str = re.findall(r'############ \<\|EXAMPLE OUTPUT START\|\> ############\n(.*?)############ \<\|EXAMPLE OUTPUT END\|\> ############', r['result'], re.DOTALL)
if len(output_str) == 0:
model_obj['is_correct'] = 'Error: no output'
return model_obj
output_str = output_str[0].strip()
outputs = output_str.split('<|OUT|>')
outputs = [output.strip() for output in outputs if output.strip()]
processed_outputs = []
for o in outputs:
# error output
if '<|EXCEPTION|>' in o:
# processed_outputs.append({'error': o.replace('<|EXCEPTION|>', '', 1).strip()})
processed_outputs.append(o.strip())
else:
res = o.split('<|RETURN|>')
if len(res) != 2:
raise ValueError(f'Invalid output: {output_str}')
ret_val = res[1].strip()
processed_outputs.append(ret_val)
model_obj['processed_outputs'] = processed_outputs
if len(processed_outputs) != len(gold_exec_output):
model_obj['is_correct'] = 'Error: output length mismatch'
return model_obj
for r, g in zip(processed_outputs, gold_exec_output):
if r == g:
continue
model_obj['is_correct'] = 'Error: output mismatch'
return model_obj
model_obj['is_correct'] = 'Correct'
return model_obj
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str)
parser.add_argument('--model_output', type=str)
parser.add_argument('--output', type=str)
parser.add_argument('--passk', type=int, default=1)
parser.add_argument('--n_workers', type=int, default=6)
args = parser.parse_args()
args.output = os.path.abspath(args.output)
args.model_output = os.path.abspath(args.model_output)
args.data = os.path.abspath(args.data)
os.makedirs(args.output, exist_ok=True)
os.makedirs('./tmp_exec', exist_ok=True)
os.chdir('./tmp_exec')
score_path = os.path.join(args.output, os.path.basename(args.model_output))
if os.path.exists(score_path):
results = {}
with open(score_path, 'r', encoding='utf-8') as f:
for line in f:
obj = json.loads(line)
results[obj[0]['gen_answer_id']] = obj
else:
data = {}
with open(args.data, 'r', encoding='utf-8') as f:
for line in f:
obj = json.loads(line)
data[obj['id']] = obj
model_output = {}
with open(args.model_output, 'r', encoding='utf-8') as f:
for line in f:
obj = json.loads(line)
ex_id = obj['gen_answer_id']
if ex_id in model_output:
model_output[ex_id].append(obj)
else:
model_output[obj['gen_answer_id']] = [obj]
pool = Pool(args.n_workers)
results = {}
for key in data:
if key not in model_output:
results[key] = [{'is_correct': 'Error: no model output'}] * args.passk
else:
for idx in range(args.passk):
if idx < len(model_output[key]):
r = pool.apply_async(run_worker, (data[key], model_output[key][idx]))
if key not in results:
results[key] = [r]
else:
results[key].append(r)
else:
results[key].append({'is_correct': 'Error: no model output'})
for key in tqdm(results):
r_list = results[key]
new_r_list = []
for r in r_list:
if isinstance(r, dict):
new_r_list.append(r)
else:
new_r_list.append(r.get())
results[key] = new_r_list
pass_k = {i: 0 for i in range(args.passk)}
for key in results:
r_list = results[key]
for idx in range(len(r_list)):
if r_list[idx]['is_correct'] == 'Correct':
for j in range(idx, len(r_list)):
pass_k[j] += 1
break
for k in pass_k:
print(f'{args.model_output} Pass {k+1}: {pass_k[k]}/{len(results)}={round(pass_k[k] / len(results), 4)}')
with open(os.path.join(args.output, os.path.basename(args.model_output)), 'w', encoding='utf-8') as f:
for key in results:
f.write(json.dumps(results[key]) + '\n')
if __name__ == '__main__':
main()