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[BugFix] Resolve DDP hanging bug within ModelCheckpoint and val_loss #6004

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[BugFix] Resolve DDP hanging bug within ModelCheckpoint and val_loss #6004

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464a9b0
resolve bugs
tchaton Feb 16, 2021
153ac4c
update
tchaton Feb 16, 2021
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Merge branch 'master' into resolve_hangs
tchaton Feb 16, 2021
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update changelog
tchaton Feb 16, 2021
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Merge branch 'resolve_hangs' of https://github.com/PyTorchLightning/p…
tchaton Feb 16, 2021
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Merge branch 'master' into resolve_hangs
tchaton Feb 16, 2021
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Merge branch 'master' into resolve_hangs
tchaton Feb 17, 2021
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3 changes: 3 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -288,6 +288,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Fixed passing wrong strings for scheduler interval doesn't throw an error ([#5923](https://github.com/PyTorchLightning/pytorch-lightning/pull/5923))


- Fixed DDP hanging with ModelCheckpoint monitor None and `val_loss` being logged ([#6004](https://github.com/PyTorchLightning/pytorch-lightning/pull/6004))


- Fixed missing `process_dataloader` call for `TPUSpawn` when in distributed mode ([#6015](https://github.com/PyTorchLightning/pytorch-lightning/pull/6015))


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6 changes: 6 additions & 0 deletions pytorch_lightning/callbacks/model_checkpoint.py
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Expand Up @@ -554,6 +554,12 @@ def _save_top_k_checkpoints(self, trainer, pl_module, metrics):
epoch = metrics.get("epoch")
step = metrics.get("step")

# when `val_loss` is being logged and no ModelCheckpoint is being provided
# `val_loss` or `checkpoint_on` will be selected for monitor and need to be reduced to
# prevent processes divergence
if self.monitor in ("val_loss", "checkpoint_on"):
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Shouldn't we reduce it always?

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@tchaton tchaton Feb 16, 2021 •
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Metrics already perform reduction during compute or self.log.
I think the issue only happens with legacy metrics.
@awaelchli Should it be always be done ?

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I'm not sure about this. I thought the metrics that the checkpoint gets from the trainer/logger connector are already reduced? We shouldn't let the checkpoint have the responsibility to reduce metrics or to assume how. the mean is not always correct.

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@SeanNaren SeanNaren Feb 16, 2021 •
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can we get a test case of this legacy metric? It will help debug and figure out why this reduction is necessary
loss should always be reduced I think, so I'm a bit puzzled how this would fix an issue

current = trainer.training_type_plugin.reduce(current, reduce_op="mean")

if self.check_monitor_top_k(current):
self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)
elif self.verbose:
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