def validate(
    cfg: DictConfig,
    trainer: Trainer,
    task: tasks.FairseqTask,
    epoch_itr,
    subsets: List[str],
) -> List[Optional[float]]:
    """Evaluate the model on the validation set(s) and return the losses."""

    if cfg.dataset.fixed_validation_seed is not None:
        # set fixed seed for every validation
        utils.set_torch_seed(cfg.dataset.fixed_validation_seed)

    trainer.begin_valid_epoch(epoch_itr.epoch)
    valid_losses = []
    for subset_idx, subset in enumerate(subsets):
        logger.info('begin validation on "{}" subset'.format(subset))

        # Initialize data iterator
        itr = trainer.get_valid_iterator(subset).next_epoch_itr(
            shuffle=False, set_dataset_epoch=False  # use a fixed valid set
        )
        if cfg.common.tpu:
            itr = utils.tpu_data_loader(itr)
        progress = progress_bar.progress_bar(
            itr,
            log_format=cfg.common.log_format,
            log_interval=cfg.common.log_interval,
            epoch=epoch_itr.epoch,
            prefix=f"valid on '{subset}' subset",
            aim_repo=(
                cfg.common.aim_repo
                if distributed_utils.is_master(cfg.distributed_training)
                else None
            ),
            aim_run_hash=(
                cfg.common.aim_run_hash
                if distributed_utils.is_master(cfg.distributed_training)
                else None
            ),
            aim_param_checkpoint_dir=cfg.checkpoint.save_dir,
            tensorboard_logdir=(
                cfg.common.tensorboard_logdir
                if distributed_utils.is_master(cfg.distributed_training)
                else None
            ),
            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
            wandb_project=(
                cfg.common.wandb_project
                if distributed_utils.is_master(cfg.distributed_training)
                else None
            ),
            wandb_run_name=os.environ.get(
                "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
            ),
        )

        # create a new root metrics aggregator so validation metrics
        # don't pollute other aggregators (e.g., train meters)
        with metrics.aggregate(new_root=True) as agg:
            for i, sample in enumerate(progress):
                if (
                    cfg.dataset.max_valid_steps is not None
                    and i > cfg.dataset.max_valid_steps
                ):
                    break
                trainer.valid_step(sample)

        # log validation stats
        # only tracking the best metric on the 1st validation subset
        tracking_best = subset_idx == 0
        stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values(), tracking_best)

        if hasattr(task, "post_validate"):
            task.post_validate(trainer.get_model(), stats, agg)

        progress.print(stats, tag=subset, step=trainer.get_num_updates())

        valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
    return valid_losses
Evaluate the model on the validation set(s) and return the losses.
fairseq_cli/train.py