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8a3099e 7aae828 8a3099e 7aae828 8a3099e 7aae828 8a3099e 7aae828 8a3099e | 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 | """Inference for CodeBERT SQL error cross-encoder."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import List, Optional, Union
import numpy as np
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from src.codebert_formatting import format_cross_encoder_input, sql_queries_equivalent
from src.device_utils import get_device
from src.codebert_labels import load_codebert_labels, multihot_to_label_names
from src.hf_metrics import sigmoid
PROJECT_ROOT = Path(__file__).resolve().parent.parent
DEFAULT_MODEL_DIR = PROJECT_ROOT / "models" / "codebert-cross-encoder"
def _is_hub_id(model_dir: Union[str, Path]) -> bool:
text = str(model_dir)
local = Path(text)
return "/" in text and not local.exists()
class CodeBERTSQLErrorClassifier:
"""CodeBERT cross-encoder inference wrapper."""
def __init__(
self,
model_dir: Union[str, Path] = DEFAULT_MODEL_DIR,
threshold: float = 0.5,
device: Optional[str] = None,
):
self.hub_id = str(model_dir) if _is_hub_id(model_dir) else None
self.model_dir = Path(model_dir) if not self.hub_id else None
self.threshold = threshold
# MPS inference can be flaky for some ops; CPU is reliable on Mac
self.device = device or (
"cuda" if torch.cuda.is_available() else "cpu"
)
model_ref = self.hub_id or str(self.model_dir)
if self.hub_id:
self.label_list = load_codebert_labels()
self.max_length = 512
else:
config_path = self.model_dir / "label_config.json"
if config_path.exists():
with open(config_path) as f:
cfg = json.load(f)
self.label_list = cfg.get("labels", load_codebert_labels())
self.threshold = cfg.get("threshold", threshold)
self.max_length = cfg.get("max_length", 512)
else:
self.label_list = load_codebert_labels()
self.max_length = 512
self.tokenizer = AutoTokenizer.from_pretrained(model_ref)
self.model = AutoModelForSequenceClassification.from_pretrained(
model_ref
).to(self.device)
self.model.eval()
def predict(
self,
question: str,
schema: str,
student_sql: str,
correct_sql: str,
threshold: Optional[float] = None,
top_k: int = 5,
) -> dict:
thr = threshold if threshold is not None else self.threshold
if sql_queries_equivalent(student_sql, correct_sql):
return {
"error_labels": [],
"probabilities": {name: 0.0 for name in self.label_list},
"top_k": [
{"label": name, "probability": 0.0}
for name in self.label_list[:5]
],
"primary_label": "NO_ERROR",
"primary_confidence": 1.0,
"match_detected": True,
}
text = format_cross_encoder_input(
question=question,
schema=schema,
student_sql=student_sql,
correct_sql=correct_sql,
)
encoded = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
padding=True,
return_tensors="pt",
).to(self.device)
with torch.no_grad():
logits = self.model(**encoded).logits.cpu().numpy()[0]
probs = sigmoid(logits)
predicted = multihot_to_label_names(probs, self.label_list, threshold=thr)
ranked = sorted(
zip(self.label_list, probs.tolist()),
key=lambda x: x[1],
reverse=True,
)[:top_k]
top_label, top_prob = ranked[0]
if top_prob >= thr:
primary_label = top_label
primary_confidence = float(top_prob)
else:
primary_label = "NO_ERROR"
primary_confidence = 1.0 - float(top_prob)
return {
"error_labels": predicted,
"probabilities": {name: float(p) for name, p in ranked},
"top_k": [
{"label": name, "probability": float(p)} for name, p in ranked
],
"primary_label": primary_label,
"primary_confidence": primary_confidence,
"match_detected": False,
}
def predict_batch(
self,
examples: List[dict],
batch_size: int = 16,
) -> List[dict]:
results = []
for i in range(0, len(examples), batch_size):
chunk = examples[i : i + batch_size]
texts = [
format_cross_encoder_input(
question=x["question"],
schema=x["schema"],
student_sql=x["student_sql"],
correct_sql=x["correct_sql"],
)
for x in chunk
]
encoded = self.tokenizer(
texts,
truncation=True,
max_length=self.max_length,
padding=True,
return_tensors="pt",
).to(self.device)
with torch.no_grad():
logits = self.model(**encoded).logits.cpu().numpy()
for j, row in enumerate(logits):
probs = sigmoid(row)
results.append(
{
"error_labels": multihot_to_label_names(
probs, self.label_list, self.threshold
),
"primary_label": self.label_list[int(np.argmax(probs))],
"primary_confidence": float(np.max(probs)),
}
)
return results
def main() -> None:
parser = argparse.ArgumentParser(description="CodeBERT SQL error inference")
parser.add_argument("--model-dir", type=Path, default=DEFAULT_MODEL_DIR)
parser.add_argument("--question", type=str, required=True)
parser.add_argument("--schema", type=str, required=True)
parser.add_argument("--student-sql", type=str, required=True)
parser.add_argument("--correct-sql", type=str, required=True)
parser.add_argument("--threshold", type=float, default=0.5)
args = parser.parse_args()
clf = CodeBERTSQLErrorClassifier(args.model_dir, threshold=args.threshold)
result = clf.predict(
question=args.question,
schema=args.schema,
student_sql=args.student_sql,
correct_sql=args.correct_sql,
)
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
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