train.py

import torch
from daedalus_mobile import DaedalusMobile
from tokenizer import DaedalusTokenizer
from config import config

def train(model, device, train_loader, optimizer):
    model.train()
    total_loss = 0
    for batch in train_loader:
        input_ids, attention_mask, labels = batch
        input_ids, attention_mask, labels = input_ids.to(device), attention_mask.to(device), labels.to(device)
        optimizer.zero_grad()
        loss = model.train_step((input_ids, attention_mask, labels))
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(train_loader)

def main():
    device = torch.device(config.device)
    model = DaedalusMobile(config)
    model.to(device)
    tokenizer = DaedalusTokenizer(config)
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=config.batch_size, shuffle=True)
    optimizer = model.configure_optimizers()
    for epoch in range(config.epochs):
        loss = train(model, device, train_loader, optimizer)
        print(f'Epoch {epoch+1}, Loss: {loss:.4f}')

if __name__ == '__main__':
    main()