File size: 6,031 Bytes
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import os
import torch
import json
import time
from tqdm import tqdm
from utils.load_sft_dataset import SFTSQLGenerationDataset
from utils.db_utils import detect_special_char
from validator import Validator
from sql_agent import SQLAgent
class SQLGenerator():
def __init__(self, sql_llm_path, val_llm_path):
# load model
self.validator = Validator(val_llm_path, llm_path=val_llm_path, api_base=None)
if val_llm_path == sql_llm_path:
self.sql_agent = SQLAgent(None)
self.sql_agent.model = self.validator.model
self.sql_agent.tokenizer = self.validator.tokenizer
else:
self.sql_agent = SQLAgent(sql_llm_path)
self.model = self.validator.model
self.tokenizer = self.validator.tokenizer
def text2sql(self, data,
max_new_tokens,
num_beams=4,
num_return_sequences=4,
do_sample=False,
temperature=0.0,
n_turns=3):
print("-"*50)
print("Question:", data["question"])
print("True SQL:", data["sql"])
self.sql_agent.reset(data)
n_turn = 0
all_message_feedbacks = []
while n_turn <= n_turns:
generated_sqls = self.sql_agent.generate_sql(
max_new_tokens,
num_beams,
num_return_sequences,
do_sample=do_sample,
temperature=temperature
)
if len(generated_sqls) == 0:
break
generated_sqls = list(set(generated_sqls))
self.sql_agent.pick_best_sql(generated_sqls)
print('\n'.join([f"{i}: {generated_sql}" for i, generated_sql in enumerate(generated_sqls)]))
# get feedback from validator
str_schema = f"""{data["schema_sequence"]}
{data["content_sequence"]}"""
for generated_sql in generated_sqls:
feedbacks, message_feedbacks = self.validator.get_answer(schema=str_schema,
question=data["question"],
evidence=data["evidence"],
sql_query=generated_sql,
db_path=data["db_path"],
do_sample=do_sample,
temperature=temperature,
num_return_sequences=num_return_sequences)
# print('\n'.join([f"{i}: {message_feedback[-1]['content']}" for i, message_feedback in enumerate(message_feedbacks)]))
all_message_feedbacks.extend(message_feedbacks)
feedback = feedbacks[0]
if "Correct SQL" in feedback:
break
# update message
self.sql_agent.receive_feedback(feedback)
return all_message_feedbacks
def parse_option():
parser = argparse.ArgumentParser()
parser.add_argument('--sql_llm_path', type = str)
parser.add_argument('--val_llm_path', type = str)
parser.add_argument('--sic_path', type = str)
parser.add_argument('--table_num', type = int, default = 6)
parser.add_argument('--column_num', type = int, default = 10)
parser.add_argument('--dataset_path', type = str)
parser.add_argument('--max_tokens', type = int, default = 4096)
parser.add_argument('--max_new_tokens', type = int, default = 256)
parser.add_argument('--n_turns', type = int, default = 3)
parser.add_argument('--output_file', type = str, default = "log.json")
opt = parser.parse_args()
return opt
def post_process(sql, schema_items):
sql = sql.replace("\n", " ")
for table in schema_items:
for column_name in table["column_names"]:
if detect_special_char(column_name) and column_name in sql:
sql = sql.replace(column_name, "`"+column_name+"`")
while "``" in sql:
sql = sql.replace("``", "`")
return sql
if __name__ == "__main__":
opt = parse_option()
print(opt)
max_tokens = opt.max_tokens
max_new_tokens = opt.max_new_tokens
sql_generator = SQLGenerator(opt.sql_llm_path, opt.val_llm_path)
tokenizer = sql_generator.tokenizer
eval_set = SFTSQLGenerationDataset(
opt.dataset_path,
tokenizer,
max_tokens - max_new_tokens,
"eval",
opt.table_num,
opt.column_num,
opt.sic_path,
do_filter_schema = False
)
# TODO: current, we only support batch size = 1
# dataloader = DataLoader(eval_set, batch_size = 1)
os.makedirs(os.path.dirname(opt.output_file), exist_ok = True)
start_time = time.time()
predicted_sqls = []
if os.path.isfile(opt.output_file):
all_feedback_messages = json.load(open(opt.output_file))
else:
all_feedback_messages = []
for idata in tqdm(range(len(all_feedback_messages), len(eval_set.dataset))):
data = eval_set.dataset[idata]
message_feedbacks = sql_generator.text2sql(
data,
max_new_tokens,
num_beams=1,
num_return_sequences=3,
do_sample=True,
temperature=0.9,
n_turns=opt.n_turns
)
all_feedback_messages.append(message_feedbacks)
if idata % 10 == 0:
json.dump(all_feedback_messages, open(opt.output_file, "w"), indent = 2)
end_time = time.time()
print("LLM name: {} - {} | Total time: {}s | Example number: {} | Average time: {}s".format(
opt.sql_llm_path,
opt.val_llm_path,
end_time - start_time,
len(eval_set.dataset),
(end_time - start_time) / len(eval_set.dataset)
)
)
json.dump(all_feedback_messages, open(opt.output_file, "w"), indent = 2)
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