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9b2cded | 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 | """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
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"
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.model_dir = Path(model_dir)
self.threshold = threshold
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
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(self.model_dir)
self.model = AutoModelForSequenceClassification.from_pretrained(
self.model_dir
).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:
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)
thr = threshold if threshold is not None else self.threshold
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]
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": ranked[0][0],
"primary_confidence": float(ranked[0][1]),
}
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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