text stringlengths 1 93.6k |
|---|
continue
|
# log mid-epoch stats
|
stats = get_training_stats(trainer)
|
for k, v in log_output.items():
|
if k in ['loss', 'nll_loss', 'ntokens', 'nsentences', 'sample_size']:
|
continue # these are already logged above
|
if 'loss' in k or k == 'accuracy':
|
extra_meters[k].update(v, log_output['sample_size'])
|
else:
|
extra_meters[k].update(v)
|
stats[k] = extra_meters[k].avg
|
progress.log(stats, tag='train', step=stats['num_updates'])
|
# ignore the first mini-batch in words-per-second calculation
|
if i == 0:
|
trainer.get_meter('wps').reset()
|
num_updates = trainer.get_num_updates()
|
if (
|
not args.disable_validation
|
and args.save_interval_updates > 0
|
and num_updates % args.save_interval_updates == 0
|
and num_updates > 0
|
):
|
valid_losses = validate(args, trainer, task, epoch_itr, valid_subsets)
|
checkpoint_utils.save_checkpoint(args, trainer, epoch_itr, valid_losses[0])
|
if num_updates >= max_update:
|
break
|
# log end-of-epoch stats
|
stats = get_training_stats(trainer)
|
for k, meter in extra_meters.items():
|
stats[k] = meter.avg
|
progress.print(stats, tag='train', step=stats['num_updates'])
|
# reset training meters
|
for k in [
|
'train_loss', 'train_nll_loss', 'wps', 'ups', 'wpb', 'bsz', 'gnorm', 'clip',
|
]:
|
meter = trainer.get_meter(k)
|
if meter is not None:
|
meter.reset()
|
def get_training_stats(trainer):
|
stats = collections.OrderedDict()
|
stats['loss'] = trainer.get_meter('train_loss')
|
if trainer.get_meter('train_nll_loss').count > 0:
|
nll_loss = trainer.get_meter('train_nll_loss')
|
stats['nll_loss'] = nll_loss
|
else:
|
nll_loss = trainer.get_meter('train_loss')
|
stats['ppl'] = utils.get_perplexity(nll_loss.avg)
|
stats['wps'] = trainer.get_meter('wps')
|
stats['ups'] = trainer.get_meter('ups')
|
stats['wpb'] = trainer.get_meter('wpb')
|
stats['bsz'] = trainer.get_meter('bsz')
|
stats['num_updates'] = trainer.get_num_updates()
|
stats['lr'] = trainer.get_lr()
|
stats['gnorm'] = trainer.get_meter('gnorm')
|
stats['clip'] = trainer.get_meter('clip')
|
stats['oom'] = trainer.get_meter('oom')
|
if trainer.get_meter('loss_scale') is not None:
|
stats['loss_scale'] = trainer.get_meter('loss_scale')
|
stats['wall'] = round(trainer.get_meter('wall').elapsed_time)
|
stats['train_wall'] = trainer.get_meter('train_wall')
|
return stats
|
def validate(args, trainer, task, epoch_itr, subsets):
|
"""Evaluate the model on the validation set(s) and return the losses."""
|
valid_losses = []
|
for subset in subsets:
|
# Initialize data iterator
|
itr = task.get_batch_iterator(
|
dataset=task.dataset(subset),
|
max_tokens=args.max_tokens_valid,
|
max_sentences=args.max_sentences_valid,
|
max_positions=utils.resolve_max_positions(
|
task.max_positions(),
|
trainer.get_model().max_positions(),
|
),
|
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
|
required_batch_size_multiple=args.required_batch_size_multiple,
|
seed=args.seed,
|
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