File size: 5,349 Bytes
778d47d | 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 | import json
import pickle as pkl
import argparse
import numpy as np
import requests
from tqdm import tqdm
from copy import deepcopy
from multiprocessing import Pool, cpu_count
from data_processing.planner import SelectionAgentWithSchema
import os
parser = argparse.ArgumentParser()
parser.add_argument("--pred_file", type=str, default='logs/results-orpo-iter-2-bird-train-top-20-temperature-1.0.pkl')
parser.add_argument("--max_candidates", type=int, default=3)
parser.add_argument("--progress_file", type=str, default='temp/bird_selection_dpo.jsonl')
args = parser.parse_args()
# Initialize Selection Agent
selection_agent = SelectionAgentWithSchema()
def get_answer_selection(messages):
response = requests.post(
"http://192.168.1.108:8006/v1/completions",
json={
"model": 'selection',
"prompt": messages[0]['content'],
"max_tokens": 512,
"use_beam_search": False,
"n": 20,
"temperature": 1.0,
"stop": ['<|eot_id|>', '<|end|>', '<|end_header_id|>', '<|end_of_text|>', '<｜end▁of▁sentence｜>']
}
).json()
try:
return [x['text'] for x in response['choices']]
except:
print(response)
return []
selection_agent.get_answer = get_answer_selection
# Load predictions
preds = pkl.load(open(args.pred_file, 'rb'))
# Load progress from previous runs
processed_keys = {}
if os.path.exists(args.progress_file):
with open(args.progress_file, 'r', encoding='utf-8') as f:
for line in f:
sample = json.loads(line.strip())
key = (sample["db_id"], sample["question"])
processed_keys[key] = processed_keys.get(key, 0) + 1
# Expand preds 4 times and filter already processed ones
all_preds = preds * 4
filtered_preds = []
for sample in all_preds:
key = (sample["db_id"], sample["question"])
if processed_keys.get(key, 0) < 4:
filtered_preds.append(sample)
processed_keys[key] = processed_keys.get(key, 0) + 1 # Track count
def build_dpo_data(sample):
"""Process a single sample and return DPO data."""
sample = deepcopy(sample)
# Filter out samples with execution failures
valid_sqls, valid_results, valid_corrects = [], [], []
for i in range(min(len(sample['predict_sqls']), 20)):
if 'Execution failed' not in sample['pred_results'][i] and 'too much time' not in sample['pred_results'][i]:
valid_sqls.append(sample['predict_sqls'][i])
valid_results.append(sample['pred_results'][i])
valid_corrects.append(sample['is_execution_corrects'][i])
sample['predict_sqls'] = valid_sqls
sample['pred_results'] = valid_results
sample['is_execution_corrects'] = valid_corrects
# Shuffle valid results
indices = np.random.permutation(len(sample['predict_sqls'])).tolist()
sample['predict_sqls'] = [sample['predict_sqls'][i] for i in indices]
sample['pred_results'] = [sample['pred_results'][i] for i in indices]
sample['is_execution_corrects'] = [sample['is_execution_corrects'][i] for i in indices]
# Select a random number of candidates
n_candidates = np.random.randint(2, 6)
sample['predict_sqls'] = sample['predict_sqls'][:n_candidates]
sample['pred_results'] = sample['pred_results'][:n_candidates]
sample['is_execution_corrects'] = sample['is_execution_corrects'][:n_candidates]
sample['candidate_sqls'] = sample['predict_sqls']
sample['candidate_pred_results'] = sample['pred_results']
# Generate prompt and answers
prompt, answers = selection_agent.generate(sample)
dpo_data = {
'db_path': sample['db_path'],
'db_id': sample['db_id'],
'question': sample['question'],
'sql': sample['sql'],
'true_result': str(sample['true_result']).strip(),
'predict_sqls': sample['predict_sqls'],
'pred_results': [str(x).strip() for x in sample['pred_results']],
'is_execution_corrects': sample['is_execution_corrects'],
'reward_data': []
}
for answer in answers:
answer_index = selection_agent.extract_answer_index(answer)
if answer_index == -1 and sum(sample['is_execution_corrects']) > 0:
reward = 0
elif answer_index == -1 and sum(sample['is_execution_corrects']) == 0:
reward = 1
elif answer_index > len(sample['is_execution_corrects']):
reward = 0
elif answer_index > 0:
reward = int(sample['is_execution_corrects'][answer_index - 1])
else:
reward = -2
dpo_data['reward_data'].append({
'prompt': prompt,
'completion': answer,
'reward': reward
})
return dpo_data
if __name__ == "__main__":
num_processes = min(32, cpu_count()) # Use up to 32 processes
# Track progress and write every 50 samples
processed_count = 0
with Pool(num_processes) as pool, open(args.progress_file, 'a', encoding='utf-8') as f:
for dpo_data in tqdm(pool.imap_unordered(build_dpo_data, filtered_preds), total=len(filtered_preds)):
f.write(json.dumps(dpo_data, ensure_ascii=False) + "\n")
processed_count += 1
# Save every 50 samples
if processed_count % 50 == 0:
f.flush()
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