Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

In dist training, update loss running avg every step, sync on log #2340

Merged
merged 2 commits into from
Nov 21, 2024
Merged

In dist training, update loss running avg every step, sync on log #2340

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1897f86
In dist training, update loss running avg every step, only sync on lo…
rwightman Nov 20, 2024
77e3922
Improve the parsable results dump at end of train, stop excessive out…
rwightman Nov 21, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
39 changes: 27 additions & 12 deletions train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -930,20 +930,29 @@ def main():
# step LR for next epoch
lr_scheduler.step(epoch + 1, latest_metric)

results.append({
latest_results = {
'epoch': epoch,
'train': train_metrics,
'validation': eval_metrics,
})
}
if eval_metrics is not None:
latest_results['validation'] = eval_metrics
results.append(latest_results)

except KeyboardInterrupt:
pass

results = {'all': results}
if best_metric is not None:
results['best'] = results['all'][best_epoch - start_epoch]
# log best metric as tracked by checkpoint saver
_logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))
print(f'--result\n{json.dumps(results, indent=4)}')

if utils.is_primary(args):
# for parsable results display, dump top-10 summaries to avoid excess console spam
display_results = sorted(
results,
key=lambda x: x.get('validation', x.get('train')).get(eval_metric, 0),
reverse=decreasing_metric,
)
print(f'--result\n{json.dumps(display_results[-10:], indent=4)}')


def train_one_epoch(
Expand Down Expand Up @@ -1042,8 +1051,7 @@ def _backward(_loss):
loss = _forward()
_backward(loss)

if not args.distributed:
losses_m.update(loss.item() * accum_steps, input.size(0))
losses_m.update(loss.item() * accum_steps, input.size(0))
update_sample_count += input.size(0)

if not need_update:
Expand All @@ -1068,16 +1076,18 @@ def _backward(_loss):
lrl = [param_group['lr'] for param_group in optimizer.param_groups]
lr = sum(lrl) / len(lrl)

loss_avg, loss_now = losses_m.avg, losses_m.val
if args.distributed:
reduced_loss = utils.reduce_tensor(loss.data, args.world_size)
losses_m.update(reduced_loss.item() * accum_steps, input.size(0))
# synchronize current step and avg loss, each process keeps its own running avg
loss_avg = utils.reduce_tensor(loss.new([loss_avg]), args.world_size).item()
loss_now = utils.reduce_tensor(loss.new([loss_now]), args.world_size).item()
update_sample_count *= args.world_size

if utils.is_primary(args):
_logger.info(
f'Train: {epoch} [{update_idx:>4d}/{updates_per_epoch} '
f'({100. * (update_idx + 1) / updates_per_epoch:>3.0f}%)] '
f'Loss: {losses_m.val:#.3g} ({losses_m.avg:#.3g}) '
f'Loss: {loss_now:#.3g} ({loss_avg:#.3g}) '
f'Time: {update_time_m.val:.3f}s, {update_sample_count / update_time_m.val:>7.2f}/s '
f'({update_time_m.avg:.3f}s, {update_sample_count / update_time_m.avg:>7.2f}/s) '
f'LR: {lr:.3e} '
Expand Down Expand Up @@ -1106,7 +1116,12 @@ def _backward(_loss):
if hasattr(optimizer, 'sync_lookahead'):
optimizer.sync_lookahead()

return OrderedDict([('loss', losses_m.avg)])
loss_avg = losses_m.avg
if args.distributed:
# synchronize avg loss, each process keeps its own running avg
loss_avg = torch.tensor([loss_avg], device=device, dtype=torch.float32)
loss_avg = utils.reduce_tensor(loss_avg, args.world_size).item()
return OrderedDict([('loss', loss_avg)])


def validate(
Expand Down
Loading