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print('| num. model params: {} (num. trained: {})'.format(
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sum(p.numel() for p in model.parameters()),
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sum(p.numel() for p in model.parameters() if p.requires_grad),
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))
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#Build trainer
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trainer = Trainer(args, task, model, criterion) #Initializing a trainer class
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print('| training on {} GPUs'.format(args.distributed_world_size))
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print('| max tokens per GPU = {} and max sentences per GPU = {}'.format(
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args.max_tokens,
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args.max_sentences,
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))
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#Load the latest checkpoint if one is available and restore the
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#corresponding train iterator
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extra_state, epoch_itr = checkpoint_utils.load_checkpoint(args, trainer) #Dataloader init, LR sheduler, Val loss comes to play
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#check checkpoint_utilities.py
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#Train until the learning rate gets too small
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max_epoch = args.max_epoch or math.inf
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max_update = args.max_update or math.inf
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lr = trainer.get_lr()
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train_meter = StopwatchMeter()
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train_meter.start()
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valid_losses = [None]
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valid_subsets = args.valid_subset.split(',')
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#not reset_optimizer to start from the where it left
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while lr > args.min_lr and epoch_itr.epoch < max_epoch and trainer.get_num_updates() < max_update:
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# train for one epoch
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train(args, trainer, task, epoch_itr)
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if not args.disable_validation and epoch_itr.epoch % args.validate_interval == 0:
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valid_losses = validate(args, trainer, task, epoch_itr, valid_subsets)
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else:
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valid_losses = [None]
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# only use first validation loss to update the learning rate
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lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
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# save checkpoint
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if epoch_itr.epoch % args.save_interval == 0:
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checkpoint_utils.save_checkpoint(args, trainer, epoch_itr, valid_losses[0])
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if ':' in getattr(args, 'data', ''):
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# sharded data: get train iterator for next epoch
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epoch_itr = trainer.get_train_iterator(epoch_itr.epoch)
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train_meter.stop()
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print('| done training in {:.1f} seconds'.format(train_meter.sum))
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def train(args, trainer, task, epoch_itr):
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"""Train the model for one epoch."""
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# Update parameters every N batches
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update_freq = args.update_freq[epoch_itr.epoch - 1] \
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if epoch_itr.epoch <= len(args.update_freq) else args.update_freq[-1]
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# Initialize data iterator
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itr = epoch_itr.next_epoch_itr(
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fix_batches_to_gpus=args.fix_batches_to_gpus,
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shuffle=(epoch_itr.epoch >= args.curriculum),
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)
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itr = iterators.GroupedIterator(itr, update_freq)
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progress = progress_bar.build_progress_bar(
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args, itr, epoch_itr.epoch, no_progress_bar='simple',
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)
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extra_meters = collections.defaultdict(lambda: AverageMeter())
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valid_subsets = args.valid_subset.split(',')
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max_update = args.max_update or math.inf
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for i, samples in enumerate(progress, start=epoch_itr.iterations_in_epoch):
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log_output = trainer.train_step(samples)
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if log_output is None:
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