examples/research_projects/codeparrot/scripts/codeparrot_training.py/evaluate
def evaluate(args):
    model.eval()
    losses = []
    for step, batch in enumerate(eval_dataloader):
        with torch.no_grad():
            outputs = model(batch, labels=batch)
        loss = outputs.loss.repeat(args.valid_batch_size)
        losses.append(accelerator.gather(loss))
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    losses = torch.cat(losses)
    loss = losses[: eval_dataloader.dataset.current_size].mean()
    try:
        perplexity = torch.exp(loss)
    except OverflowError:
        perplexity = float("inf")
    return loss.item(), perplexity.item()