Model weight not updated when training with pytorch lightning

I’m new in Pytorch Lightning. I made a very simple model using PL. I checked the weights of the model before and after training but They are exactly the same knowing that the loss decrease during training.

def main(args, df_train, df_dev, df_test) :
    """ main function"""
    # Wandb connect
    wandb_logger = WandbLogger(project="project name", name="Run name")

    # Tokenization
    [df_train, df_dev, df_test], params, tokenizer_qid, tokenizer_uid, tokenizer_qu_id, tokenizer_rank = apply_tokenization([df_train, df_dev, df_test])

    # Dataloadeers
    [train_loader, dev_loader, test_loader] =  list(map(lambda x : Dataset_SM(x).get_dataloader(args.batch_size), [df_train, df_dev, df_test]))
   # Model definition
    model = NCM(**params).to(device)
   # Weight before training
    WW = model.emb_qid.weight
    # Train & Eval
    es = EarlyStopping(monitor='dev_loss', patience=4)
    checkpoint_callback = ModelCheckpoint(dirpath=args.result_path)
    trainer = pl.Trainer(max_epochs=args.n_epochs, callbacks=[es, checkpoint_callback], val_check_interval=args.val_check_interval,
                         logger=wandb_logger, gpus=1), train_loader, dev_loader)
    trainer.save_checkpoint(args.result_path + "example.ckpt")
    loaded_model = NCM.load_from_checkpoint(checkpoint_path=args.result_path + "example.ckpt", **params)
    print(loaded_model.emb_qid.weight == WW)

Can someone tell me if I miss something ?