Function build_optimizer

eval.py:132–181

(cfg, model)

def build_optimizer(cfg, model):
    if cfg is None:
        return None

    if cfg.get("filter_bias_norm_wd", False):
        params = param_groups_weight_decay(model, weight_decay=cfg.weight_decay)
    else:
        params = model.parameters()

    if cfg.name == "decoupled_adamw":
        return DecoupledAdamW(params, lr=cfg.lr, betas=list(cfg.betas), eps=cfg.eps, weight_decay=cfg.weight_decay)
    elif cfg.name == "adamw":
        print(
            "INFO: You might want to increase the weight decay because in AdamW it is scaled by the lr."
            f" Default weight decay is ``1e-2`` -> {cfg.weight_decay}. Default lr is `lr=1e-3` -> {cfg.lr}."
        )
        return AdamW(params, lr=cfg.lr, betas=list(cfg.betas), eps=cfg.eps, weight_decay=cfg.weight_decay)
    elif cfg.name == "stableadamw":
        try:
            if cfg.get("log_grad_norm", False):
                from src.optimizer import StableAdamW
            else:
                from optimi import StableAdamW
        except ImportError:
            raise ImportError("Install `pip install torch-optimi` to use the StableAdamW optimizer.")

        print(
            "INFO: You might want to increase the weight decay because in StableAdamW it is scaled by the lr."
            f" Default weight decay is ``1e-2`` -> {cfg.weight_decay}. Default lr is `lr=1e-3` -> {cfg.lr}."
        )
        return StableAdamW(params, lr=cfg.lr, betas=list(cfg.betas), eps=cfg.eps, weight_decay=cfg.weight_decay)
    elif cfg.name == "decoupled_stableadamw":
        try:
            if cfg.get("log_grad_norm", False):
                from src.optimizer import StableAdamW
            else:
                from optimi import StableAdamW
        except ImportError:
            raise ImportError("Install `pip install torch-optimi` to use the StableAdamW optimizer.")

        return StableAdamW(
            params,
            lr=cfg.lr,
            betas=list(cfg.betas),
            eps=cfg.eps,
            weight_decay=cfg.weight_decay,
            decouple_lr=True,
        )
    else:
        raise ValueError(f"Not sure how to build optimizer: {cfg.name}")


def build_model(cfg: DictConfig, num_labels: int, multiple_choice: bool = False, **kwargs):