File size: 5,887 Bytes
aad0df8 | 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 | from __future__ import annotations
import json
import random
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import trackio
from model import build_model, parameter_count
from sklearn.metrics import (
accuracy_score,
confusion_matrix,
f1_score,
precision_score,
recall_score,
)
from torch.nn import functional as F
from torch.utils.data import DataLoader, TensorDataset
from transformers import PreTrainedTokenizerFast
PROJECT_DIR = Path(__file__).resolve().parent
ROOT_DIR = PROJECT_DIR.parents[1]
TOKENIZER_DIR = ROOT_DIR / "projects" / "snip-0.4m" / "artifacts" / "snip-0.4m-base"
DATA_DIR = PROJECT_DIR / "data"
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "protocol-guardian-bert"
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def load_split(name: str, tokenizer) -> TensorDataset:
frame = pd.read_parquet(DATA_DIR / f"{name}.parquet")
encoded = tokenizer(
frame["text"].tolist(),
padding="max_length",
truncation=True,
max_length=64,
return_tensors="pt",
add_special_tokens=True,
)
return TensorDataset(
encoded["input_ids"],
encoded["attention_mask"],
torch.tensor(frame["label"].to_numpy(dtype=np.int64, copy=True)),
)
@torch.inference_mode()
def evaluate(model, dataset: TensorDataset) -> dict:
model.eval()
loader = DataLoader(dataset, batch_size=256, shuffle=False)
labels, predictions, losses = [], [], []
for input_ids, attention_mask, targets in loader:
logits = model(
input_ids=input_ids,
attention_mask=attention_mask,
).logits
losses.append(F.cross_entropy(logits, targets, reduction="sum").item())
labels.extend(targets.tolist())
predictions.extend(logits.argmax(dim=1).tolist())
return {
"loss": float(sum(losses) / len(labels)),
"accuracy": float(accuracy_score(labels, predictions)),
"precision": float(precision_score(labels, predictions)),
"recall": float(recall_score(labels, predictions)),
"f1": float(f1_score(labels, predictions)),
"confusion_matrix": confusion_matrix(labels, predictions).tolist(),
"examples": len(labels),
}
def main() -> None:
seed_everything(2026)
tokenizer = PreTrainedTokenizerFast.from_pretrained(TOKENIZER_DIR)
train_dataset = load_split("train", tokenizer)
validation_dataset = load_split("validation", tokenizer)
test_dataset = load_split("test", tokenizer)
model = build_model(len(tokenizer), tokenizer.pad_token_id)
loader = DataLoader(
train_dataset,
batch_size=96,
shuffle=True,
generator=torch.Generator().manual_seed(2026),
)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.0015, weight_decay=0.01)
epochs = 10
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
best_accuracy = -1.0
best_epoch = 0
best_state = None
trackio.init(
project="protocol-guardian",
name="tiny-bert-from-scratch-v1",
config={
"parameters": parameter_count(model),
"train_examples": len(train_dataset),
"validation_examples": len(validation_dataset),
"held_out_template_examples": len(test_dataset),
"epochs": epochs,
},
)
for epoch in range(1, epochs + 1):
model.train()
running_loss = 0.0
examples = 0
for input_ids, attention_mask, labels in loader:
logits = model(
input_ids=input_ids,
attention_mask=attention_mask,
).logits
loss = F.cross_entropy(logits, labels)
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
running_loss += loss.item() * len(labels)
examples += len(labels)
scheduler.step()
validation = evaluate(model, validation_dataset)
trackio.log(
{
"epoch": epoch,
"train_loss": running_loss / examples,
"validation_loss": validation["loss"],
"validation_accuracy": validation["accuracy"],
"validation_f1": validation["f1"],
"learning_rate": scheduler.get_last_lr()[0],
}
)
if validation["accuracy"] > best_accuracy:
best_accuracy = validation["accuracy"]
best_epoch = epoch
best_state = {
key: value.detach().cpu().clone()
for key, value in model.state_dict().items()
}
trackio.finish()
if best_state is None:
sys.exit("Training did not produce a checkpoint.")
model.load_state_dict(best_state)
test = evaluate(model, test_dataset)
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
model.save_pretrained(ARTIFACT_DIR, safe_serialization=True)
tokenizer.save_pretrained(ARTIFACT_DIR)
summary = {
"model": "Protocol Guardian Tiny BERT",
"parameters": parameter_count(model),
"best_epoch": best_epoch,
"best_validation_accuracy": best_accuracy,
"test_split": "1200 examples from entirely held-out command templates",
"test": test,
"limitations": [
"Synthetic English command corpus",
"Binary research classifier only",
"Not a replacement for deterministic authorization and safety logic",
],
}
(ARTIFACT_DIR / "training_summary.json").write_text(
json.dumps(summary, indent=2),
encoding="utf-8",
)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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