Feature/amp #980
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Feature/amp #980
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…r in official Apex examples)
Replace .shape[0] with len() call which is more compatible with exotic input batches (like for fine-tuning Faster RCNN, which requires sending list of image tensors)
# Conflicts: # CHANGELOG.md # catalyst/core/callbacks/optimizer.py
# Conflicts: # catalyst/dl/utils/quantization.py
# Conflicts: # catalyst/callbacks/optimizer.py
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This pull request is now in conflicts. @BloodAxe, could you fix it? 🙏 |
and-kul
reviewed
Nov 4, 2020
| @@ -115,9 +115,12 @@ def _scheduler_step( | |||
| ): | |||
| if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): | |||
| scheduler.step(reduced_metric) | |||
| lr = scheduler.optimizer.param_groups[0]["lr"] | |||
There was a problem hiding this comment.
Hi @BloodAxe
Unfortunately, torch.optim.lr_scheduler.ReduceLROnPlateau doesn't inherit from _LRScheduler, so we cannot use neither get_last_lr() nor get_lr() for it
bagxi
approved these changes
Nov 8, 2020
Scitator
reviewed
Nov 9, 2020
Comment on lines
+375
to
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| if not self.use_fast_zero_grad: | ||
| maybe_recursive_call(self._optimizer, "zero_grad") | ||
| else: | ||
| maybe_recursive_call(self._optimizer, zero_grad) |
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@BloodAxe could you please merge with master and check the codestyle? I think that could be enough ;) |
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already merged with #1007 |
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Before submitting
Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
Did you read the contribution guide?
Did you check the code style?
catalyst-make-codestyle && catalyst-check-codestyle(pip install -U catalyst-codestyle).Did you make sure to update the docs? We use Google format for all the methods and classes.
Did you check the docs with
make check-docs?Did you write any new necessary tests?
Did you check that your code passes the unit tests
pytest .?Did you add your new functionality to the docs?
Did you update the CHANGELOG?
added fast_zero_grad support to AMPOptimizerCallback
scheduler callback now gets learning rate using get_last_lr() method from the scheduler object (As PyTorch suggests)
added supported
find_unused_parametersoption to use with DDP to detect whether there are any unused parameters of the model.Description
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Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
PS