Spaces:
Sleeping
Sleeping
File size: 8,322 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | # import sys
# import os
# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# import json
# import subprocess
# import argparse
# from pathlib import Path
# import torch
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# from peft import PeftModel
# # IMPORTANT: must match training prompt format
# from prompting import build_prompt
# from schema_utils import get_schema as get_db_schema
# def _parse_exec_accuracy(stdout: str):
# for line in stdout.splitlines():
# if line.strip().startswith("execution"):
# parts = line.split()
# try:
# return float(parts[-1])
# except Exception:
# return None
# return None
# def main():
# parser = argparse.ArgumentParser()
# parser.add_argument("--adapter", type=str, default="checkpoints/best_rlhf_model")
# parser.add_argument("--num_samples", type=int, default=200)
# args = parser.parse_args()
# project_root = Path(__file__).resolve().parents[1]
# adapter_dir = project_root / args.adapter
# if not adapter_dir.exists():
# raise FileNotFoundError(f"Adapter not found: {adapter_dir}")
# 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 / "predictions_rl.txt"
# device = "mps" if torch.backends.mps.is_available() else "cpu"
# # ---- LOAD MODEL (CodeT5 + LoRA) ----
# base_model = "Salesforce/codet5-base"
# tokenizer = AutoTokenizer.from_pretrained(str(adapter_dir))
# base = AutoModelForSeq2SeqLM.from_pretrained(base_model).to(device)
# model = PeftModel.from_pretrained(base, str(adapter_dir)).to(device)
# # merge LoRA for faster inference
# model = model.merge_and_unload()
# model.eval()
# model.config.use_cache = True
# if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None:
# tokenizer.pad_token = tokenizer.eos_token
# # ---- LOAD DATA ----
# with dev_json.open() as f:
# dev = json.load(f)
# dev = dev[: args.num_samples]
# gen_kwargs = dict(
# max_new_tokens=120,
# do_sample=False,
# num_beams=1,
# pad_token_id=tokenizer.pad_token_id,
# eos_token_id=tokenizer.eos_token_id,
# )
# print(f"Generating {len(dev)} predictions...")
# 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"]
# db_path = db_root / db_id / f"{db_id}.sqlite"
# schema = get_db_schema(str(db_path))
# prompt = build_prompt(question, schema, use_schema=True)
# inputs = tokenizer(
# prompt,
# return_tensors="pt",
# truncation=True,
# max_length=512
# ).to(device)
# out = model.generate(**inputs, **gen_kwargs)
# pred_sql = tokenizer.decode(out[0], skip_special_tokens=True).strip()
# out_f.write(f"{pred_sql}\t{db_id}\n")
# if i % 20 == 0 or i == len(dev):
# print(f"{i}/{len(dev)} done")
# # ---- SPIDER OFFICIAL 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("\nRunning Spider execution evaluation...\n")
# proc = subprocess.run(cmd, capture_output=True, text=True)
# if proc.returncode != 0:
# print(proc.stdout)
# print(proc.stderr)
# sys.exit(proc.returncode)
# print(proc.stdout)
# acc = _parse_exec_accuracy(proc.stdout)
# if acc is not None:
# print(f"\nFINAL EXECUTION ACCURACY: {acc*100:.2f}%")
# else:
# print("Could not parse execution accuracy")
# if __name__ == "__main__":
# main()
import json
import sqlite3
import argparse
import time
from pathlib import Path
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel
# ---------------- PROMPT (FIXED TO PERFECTLY MATCH RLHF TRAINING) ----------------
def build_prompt(question, schema):
return f"translate English to SQL:\n\nSchema:\n{schema}\n\nQuestion:\n{question}\n\nSQL:"
# ---------------- LOAD SCHEMA (FIXED TO MATCH TRAINING FORMAT) ----------------
def load_schema(db_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
tables = cursor.execute(
"SELECT name FROM sqlite_master WHERE type='table';"
).fetchall()
schema = ""
for (table,) in tables:
cols = cursor.execute(f"PRAGMA table_info({table});").fetchall()
col_names = [c[1] for c in cols]
# Space-separated, not newline-separated, just like the RLHF script
schema += f"{table}({', '.join(col_names)}) "
conn.close()
return schema.strip()
# ---------------- EXECUTION CHECK WITH TIMEOUT ----------------
def execution_match(pred_sql, gold_sql, db_path):
try:
conn = sqlite3.connect(db_path)
# --- 5-SECOND TIMEOUT SO THE SCRIPT DOESN'T HANG ---
start_time = time.monotonic()
def timeout_handler():
return 1 if (time.monotonic() - start_time) > 5.0 else 0
conn.set_progress_handler(timeout_handler, 10000)
cur = conn.cursor()
cur.execute(pred_sql)
pred = cur.fetchall()
cur.execute(gold_sql)
gold = cur.fetchall()
conn.close()
return pred == gold
except Exception:
return False
# ---------------- MAIN ----------------
def main():
parser = argparse.ArgumentParser()
# 🎯 Set the default directly to your best RLHF model!
parser.add_argument("--adapter", type=str, default="checkpoints/rlhf_t5_best")
parser.add_argument("--num_samples", type=int, default=1000)
args = parser.parse_args()
project_root = Path(__file__).resolve().parents[1]
# Resolve adapter path safely
adapter_path = project_root / args.adapter
dev_json = project_root / "data" / "dev.json"
db_root = project_root / "data" / "database"
# 🎯 Added CUDA support
device = "mps" if torch.backends.mps.is_available() else ("cuda" if torch.cuda.is_available() else "cpu")
# load model
base_model = "t5-small"
print(f"Loading Base: {base_model}")
print(f"Loading Adapter: {adapter_path}")
tokenizer = AutoTokenizer.from_pretrained(str(adapter_path))
base = AutoModelForSeq2SeqLM.from_pretrained(base_model).to(device)
model = PeftModel.from_pretrained(base, str(adapter_path)).to(device)
model = model.merge_and_unload()
with open(dev_json) as f:
dev = json.load(f)[: args.num_samples]
correct = 0
print(f"Evaluating {len(dev)} examples...\n")
for i, ex in enumerate(dev, 1):
question = ex["question"]
db_id = ex["db_id"]
gold_sql = ex["query"]
db_path = db_root / db_id / f"{db_id}.sqlite"
schema = load_schema(db_path)
prompt = build_prompt(question, schema)
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=80,
do_sample=False,
num_beams=4,
)
pred_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
if "SQL:" in pred_sql:
pred_sql = pred_sql.split("SQL:")[-1].strip()
match = execution_match(pred_sql, gold_sql, db_path)
if match:
correct += 1
if i % 10 == 0:
print(f"{i}/{len(dev)} | Acc: {correct/i:.3f}")
print("\n=============================")
print(f"FINAL EXECUTION ACCURACY: {correct/len(dev)*100:.2f}%")
print("=============================")
if __name__ == "__main__":
main() |