Spaces:
Running
Running
File size: 7,061 Bytes
4464055 dbd1ddd 4464055 dbd1ddd 1e55fa2 dbd1ddd 4464055 adb1e77 dbd1ddd 4464055 adb1e77 dbd1ddd adb1e77 1e55fa2 adb1e77 4464055 adb1e77 4464055 1e55fa2 4464055 dbd1ddd 4464055 dbd1ddd 4464055 dbd1ddd 4464055 dbd1ddd 4464055 adb1e77 4464055 dbd1ddd 4464055 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from datetime import datetime
import json
import os
from pathlib import Path
import re
import sys
import time
import tempfile
from zoneinfo import ZoneInfo # Python 3.9+ 自带，无需安装
pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../"))
from google import genai
from google.genai import types
from project_settings import environment, project_path
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
# default="gemini-2.5-pro", # The model does not support setting thinking_budget to 0.
default="gemini-2.5-flash",
# default="gemini-2.5-flash-lite-preview-06-17",
# default="llama-4-maverick-17b-128e-instruct-maas",
# default="llama-4-scout-17b-16e-instruct-maas",
type=str
)
parser.add_argument(
"--eval_dataset_name",
# default="agent-lingoace-zh-80-chat.jsonl",
# default="agent-bingoplus-ph-200-chat.jsonl",
default="agent-cod-zh-70-chat.jsonl",
type=str
)
parser.add_argument(
"--eval_dataset_dir",
default=(project_path / "data/dataset").as_posix(),
type=str
)
parser.add_argument(
"--eval_data_dir",
default=(project_path / "data/eval_data").as_posix(),
type=str
)
parser.add_argument(
"--client",
default="shenzhen_sase",
type=str
)
parser.add_argument(
"--service",
default="google_potent_veld_462405_t3",
type=str
)
parser.add_argument(
"--create_time_str",
default="null",
# default="20250731_162116",
type=str
)
parser.add_argument(
"--interval",
default=1,
type=int
)
args = parser.parse_args()
return args
def main():
args = get_args()
service = environment.get(args.service, dtype=json.loads)
project_id = service["project_id"]
google_application_credentials = Path(tempfile.gettempdir()) / f"llm_eval_system/{project_id}.json"
google_application_credentials.parent.mkdir(parents=True, exist_ok=True)
with open(google_application_credentials.as_posix(), "w", encoding="utf-8") as f:
content = json.dumps(service, ensure_ascii=False, indent=4)
f.write(f"{content}\n")
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = google_application_credentials.as_posix()
eval_dataset_dir = Path(args.eval_dataset_dir)
eval_dataset_dir.mkdir(parents=True, exist_ok=True)
eval_data_dir = Path(args.eval_data_dir)
eval_data_dir.mkdir(parents=True, exist_ok=True)
if args.create_time_str == "null":
tz = ZoneInfo("Asia/Shanghai")
now = datetime.now(tz)
create_time_str = now.strftime("%Y%m%d_%H%M%S")
# create_time_str = "20250729-interval-5"
else:
create_time_str = args.create_time_str
eval_dataset = eval_dataset_dir / args.eval_dataset_name
output_file = eval_data_dir / f"gemini_google/google/{args.model_name}/{args.client}/{args.service}/{create_time_str}/{args.eval_dataset_name}.raw"
output_file.parent.mkdir(parents=True, exist_ok=True)
client = genai.Client(
vertexai=True,
project=project_id,
# location="global",
location="us-east5",
)
generate_content_config = types.GenerateContentConfig(
top_p=0.95,
temperature=0.6,
# max_output_tokens=1,
response_modalities=["TEXT"],
thinking_config=types.ThinkingConfig(
thinking_budget=0
)
)
total = 0
# finished
finished_idx_set = set()
if os.path.exists(output_file.as_posix()):
with open(output_file.as_posix(), "r", encoding="utf-8") as f:
for row in f:
row = json.loads(row)
idx = row["idx"]
total = row["total"]
finished_idx_set.add(idx)
print(f"finished count: {len(finished_idx_set)}")
with open(eval_dataset.as_posix(), "r", encoding="utf-8") as fin, open(output_file.as_posix(), "a+", encoding="utf-8") as fout:
for row in fin:
row = json.loads(row)
idx = row["idx"]
prompt = row["prompt"]
response = row["response"]
if idx in finished_idx_set:
continue
finished_idx_set.add(idx)
# prompt
splits = prompt[::-1].split("\n\n", maxsplit=1)
conversation = splits[0]
system_prompt = splits[1]
conversation = conversation[::-1].strip()
system_prompt = system_prompt[::-1].strip()
pattern = "^(Client|Assistant): (.*?)(?=\n(?:Client|Assistant):)"
match = re.findall(pattern=pattern, string=conversation, flags=re.I|re.DOTALL|re.MULTILINE)
messages_ = list()
for m in match:
role = m[0].lower()
content = m[1]
if role in ("client", "Client"):
role = "user"
elif role in ("assistant", "Assistant"):
role = "assistant"
else:
raise AssertionError
messages_.append({
"role": role,
"content": content
})
messages = [
{"role": "system", "content": system_prompt},
*messages_
]
# print(json.dumps(messages, ensure_ascii=False, indent=4))
# exit(0)
contents = [
types.Content(
role="user" if m["role"] == "user" else "model",
parts=[
types.Part.from_text(text=m["content"])
]
)
for m in messages
]
time.sleep(args.interval)
print(f"sleep: {args.interval}")
time_begin = time.time()
llm_response: types.GenerateContentResponse = client.models.generate_content(
model=args.model_name,
contents=contents,
config=generate_content_config,
)
time_cost = time.time() - time_begin
print(f"time_cost: {time_cost}")
try:
prediction = llm_response.candidates[0].content.parts[0].text
except TypeError as e:
print(f"request failed, error type: {type(e)}, error text: {str(e)}")
continue
total += 1
row_ = {
"idx": idx,
"prompt": prompt,
"response": response,
"prediction": prediction,
"total": total,
"time_cost": time_cost,
}
row_ = json.dumps(row_, ensure_ascii=False)
fout.write(f"{row_}\n")
fout.flush()
return
if __name__ == "__main__":
main()
|