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dc59b01 | 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 | from __future__ import annotations
import json
import subprocess
import sys
import argparse
import re
import sqlite3
from pathlib import Path
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel
from prompting import encode_prompt
# ---------------- PARSE ACC ----------------
def _parse_exec_accuracy(stdout: str) -> float | None:
for line in stdout.splitlines():
if line.strip().startswith("execution"):
try:
return float(line.split()[-1])
except:
return None
return None
# ---------------- CLEAN SQL ----------------
def clean_prediction(pred_sql: str) -> str:
pred_sql = pred_sql.strip()
if "SQL:" in pred_sql:
pred_sql = pred_sql.split("SQL:")[-1]
pred_sql = pred_sql.replace('"', "'")
pred_sql = re.sub(r"\s+", " ", pred_sql).strip()
if not pred_sql.endswith(";"):
pred_sql += ";"
return pred_sql
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--adapter", type=str, default="checkpoints/sft_t5")
parser.add_argument("--num_samples", type=int, default=1000)
args = parser.parse_args()
project_root = Path(__file__).resolve().parents[1]
adapter_dir = project_root / args.adapter
db_root = project_root / "data/database"
table_json = project_root / "data/tables.json"
dev_json = project_root / "data/dev.json"
gold_sql = project_root / "data/dev_gold.sql"
pred_path = project_root / "pred.sql"
if not adapter_dir.exists():
raise FileNotFoundError(f"Missing adapter dir: {adapter_dir}")
# ---------------- DEVICE ----------------
device = "mps" if torch.backends.mps.is_available() else (
"cuda" if torch.cuda.is_available() else "cpu"
)
print("Using device:", device)
# ---------------- LOAD MODEL ----------------
BASE_MODEL = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL).to(device)
model = PeftModel.from_pretrained(base, str(adapter_dir)).to(device)
model = model.merge_and_unload()
model.eval()
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
# ---------------- LOAD DATA ----------------
with dev_json.open() as f:
dev = json.load(f)[: args.num_samples]
print("Generating predictions...\n")
correct = 0
total = len(dev)
# ---------------- GENERATE + LIVE EXEC ----------------
with pred_path.open("w") as out_f, torch.no_grad():
for i, ex in enumerate(dev, start=1):
db_id = ex["db_id"]
question = ex["question"]
gold_query = ex["query"]
prompt_ids = encode_prompt(
tokenizer,
question,
db_id,
device=device,
max_input_tokens=512,
)
input_ids = prompt_ids.unsqueeze(0).to(device)
attention_mask = (input_ids != tokenizer.pad_token_id).long().to(device)
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=160,
num_beams=4,
do_sample=False,
early_stopping=True,
)
pred_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
pred_sql = clean_prediction(pred_sql)
out_f.write(pred_sql + "\n")
# -------- LIVE EXECUTION CHECK --------
try:
db_path = db_root / db_id / f"{db_id}.sqlite"
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute(pred_sql)
pred_rows = cursor.fetchall()
cursor.execute(gold_query)
gold_rows = cursor.fetchall()
conn.close()
if sorted(pred_rows) == sorted(gold_rows):
correct += 1
except Exception:
pass # execution failed
# 🔥 PRINT EVERY 10
if i % 10 == 0 or i == total:
current_acc = correct / i
print(f"{i}/{total} | Acc: {current_acc:.3f}")
print("\nGeneration finished.\n")
# ---------------- SPIDER EVAL ----------------
eval_script = project_root / "spider_eval/evaluation.py"
cmd = [
sys.executable,
str(eval_script),
"--gold", str(gold_sql),
"--pred", str(pred_path),
"--etype", "exec",
"--db", str(db_root),
"--table", str(table_json),
]
print("Running Spider evaluation...")
proc = subprocess.run(cmd, capture_output=True, text=True)
print(proc.stdout)
exec_acc = _parse_exec_accuracy(proc.stdout)
if exec_acc is not None:
print(f"\n🎯 Official Execution Accuracy: {exec_acc*100:.2f}%")
else:
print("Could not parse accuracy.")
if __name__ == "__main__":
main() |