File size: 4,403 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 | from __future__ import annotations
import json
import sys
import torch
import trackio
from torch.nn import functional as F
from torch.utils.data import DataLoader
from train import (
ARTIFACT_DIR,
DATA_DIR,
evaluate,
load_split,
parameter_count,
seed_everything,
)
from transformers import AutoModelForSequenceClassification, AutoTokenizer
BASE_MODEL = "google/bert_uncased_L-2_H-128_A-2"
OUTPUT_DIR = ARTIFACT_DIR.parent / "protocol-guardian-pretrained-tinybert"
def main() -> None:
if not DATA_DIR.exists():
sys.exit("Generate the Protocol Guardian dataset first.")
seed_everything(2029)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
train_dataset = load_split("train", tokenizer)
validation_dataset = load_split("validation", tokenizer)
test_dataset = load_split("test", tokenizer)
model = AutoModelForSequenceClassification.from_pretrained(
BASE_MODEL,
num_labels=2,
id2label={0: "ROUTINE", 1: "HAZARDOUS"},
label2id={"ROUTINE": 0, "HAZARDOUS": 1},
)
loader = DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
generator=torch.Generator().manual_seed(2029),
)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)
epochs = 8
total_steps = epochs * len(loader)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=total_steps,
)
best_accuracy = -1.0
best_epoch = 0
best_state = None
trackio.init(
project="protocol-guardian",
name="google-tinybert-transfer-v1",
config={
"base_model": BASE_MODEL,
"parameters": parameter_count(model),
"epochs": epochs,
"train_examples": len(train_dataset),
"held_out_template_examples": len(test_dataset),
},
)
global_step = 0
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()
scheduler.step()
global_step += 1
running_loss += loss.item() * len(labels)
examples += len(labels)
validation = evaluate(model, validation_dataset)
trackio.log(
{
"epoch": epoch,
"global_step": global_step,
"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)
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
model.save_pretrained(OUTPUT_DIR, safe_serialization=True)
tokenizer.save_pretrained(OUTPUT_DIR)
summary = {
"model": "Protocol Guardian Pretrained TinyBERT",
"base_model": BASE_MODEL,
"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,
"comparison": {
"from_scratch_v1_accuracy": 0.6591666666666667,
"from_scratch_broader_curriculum_accuracy": 0.4075,
},
}
(OUTPUT_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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