Enforce an epoch scheduler interval when using SWA #6588

mibaumgartner · 2021-03-18T21:15:27Z

What does this PR do?

Fixes #6571
Instead of replacing the scheduler, the whole scheduler dict is replaced now to enforce the correct behavior

As the SWA implementation is subject to change (according to the docstring) I wasn't sure I should add additional tests (e.g. there might be unexpected behavior of the learning rate when continuing training from an epoch after swa start epoch).

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exchange whole scheduler dict in SWA

tchaton

Hey @mibaumgartner,

Mind adding a test ?

Best,
T.C

pep8speaks · 2021-03-23T23:01:21Z

Hello @mibaumgartner! Thanks for updating this PR.

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2021-04-06 02:30:18 UTC

carmocca

LGTM, just a minor comment

tests/callbacks/test_stochastic_weight_avg.py

codecov · 2021-03-27T02:05:59Z

Codecov Report

Merging #6588 (e7f05a6) into master (7f91c5e) will decrease coverage by 5%.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master   #6588    +/-   ##
=======================================
- Coverage      91%     87%    -5%     
=======================================
  Files         192     192            
  Lines       12191   12192     +1     
=======================================
- Hits        11148   10596   -552     
- Misses       1043    1596   +553

Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>

* update readme by v1.2.x (#6728) * [bugfix] Add support for omegaconf and tpu (#6741) * fix_hydra * update changelog Co-authored-by: Your Name <you@example.com> * [docs] Update Bolts link (#6743) * Update Bolts link * Update Bolts link * formt Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Update logic for checking TPUs availability (#6767) * Update logic for checking TPUs availability * fix flake8 * add fix * resolve bug (#6781) * Fix validation progress counter with check_val_every_n_epoch > 1 (#5952) Co-authored-by: rohitgr7 <rohitgr1998@gmail.com> Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> * Remove extinct parameters from lightning_module.rst (#6801) Fixes #6800 * Update TPU docs for installation (#6794) * fix boolean check on iterable dataset when len not defined (#6828) * fix iterable dataset len check * update predict and validate * add validate to test * add changelog * add predict * Sanitize `None` params during pruning (#6836) * sanitize none params during pruning * amend * Fix `unfreeze_and_add_param_group` expects `modules` rather than `module` (#6822) * Enforce an epoch scheduler interval when using SWA (#6588) Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com> * Fix DPP + SyncBN (#6838) * Fix DPP + SyncBN Ensure that model is already on correct GPU before applying SyncBN conversion * Fix order of SyncBN for ddp_spawn * [Fix] TPU Training Type Plugin (#6816) * Fix support for symlink save_dir in TensorBoardLogger (#6730) * Add test for symlink support and initial fix * Respond to comment and add docstring * Update CHANGELOG.md * Simplify * Update pytorch_lightning/utilities/cloud_io.py Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> * Make `LightningLocalFileSystem` protected Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> * Fixed missing arguments in `lr_find` call (#6784) There seem to be 3 arguments missing in the `lr_find` call in the tunining.py file. * Update Changelog & version * Fix TPU tests for checkpoint Skip advanced profiler for torch > 1.8 Skip pytorch profiler for torch > 1.8 Fix save checkpoint logic for TPUs Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: thomas chaton <thomas@grid.ai> Co-authored-by: Your Name <you@example.com> Co-authored-by: Akihiro Nitta <nitta@akihironitta.com> Co-authored-by: Yuan-Hang Zhang <sailordiary@users.noreply.github.com> Co-authored-by: rohitgr7 <rohitgr1998@gmail.com> Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> Co-authored-by: Elizaveta Logacheva <elimohl@gmail.com> Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> Co-authored-by: Karthik Prasad <prasadkr@uci.edu> Co-authored-by: Sadiq Jaffer <sadiq@toao.com> Co-authored-by: Michael Baumgartner <m.baumgartner@ymail.com> Co-authored-by: Eugene Khvedchenya <ekhvedchenya@gmail.com> Co-authored-by: Ethan Harris <ewah1g13@soton.ac.uk> Co-authored-by: Tharindu Hasthika <tharindubathigama@gmail.com>

Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>

…ter) to github/third-party/PyTorchLightning/pytorch-lightning Summary: ### New commit log messages ## [UnReleased] - 2021-MM-DD ### Added - Added more explicit exception message when trying to execute `trainer.test()` or `trainer.validate()` with `fast_dev_run=True` ([#6667](Lightning-AI/pytorch-lightning#6667)) - Added `LightningCLI` class to provide simple reproducibility with minimum boilerplate training cli. ([#4492](Lightning-AI/pytorch-lightning#4492)) - Trigger warning when non-metric logged value with multi processes hasn't been reduced ([#6417](Lightning-AI/pytorch-lightning#6417)) - Added `gradient_clip_algorithm` argument to Trainer for gradient clipping by value ([#6123](Lightning-AI/pytorch-lightning#6123)). - Added a way to print to terminal without breaking up the progress bar ([#5470](Lightning-AI/pytorch-lightning#5470)) - Added support to checkpoint after training steps in `ModelCheckpoint` callback ([#6146](Lightning-AI/pytorch-lightning#6146)) - Added `checkpoint` parameter to callback's `on_save_checkpoint` hook ([#6072](Lightning-AI/pytorch-lightning#6072)) - Added `RunningStage.SANITY_CHECKING` ([#4945](Lightning-AI/pytorch-lightning#4945)) - Added `TrainerState.{FITTING,VALIDATING,TESTING,PREDICTING,TUNING}` ([#4945](Lightning-AI/pytorch-lightning#4945)) - Added `Trainer.validate()` method to perform one evaluation epoch over the validation set ([#4948](Lightning-AI/pytorch-lightning#4948)) - Added `LightningEnvironment` for Lightning-specific DDP ([#5915](Lightning-AI/pytorch-lightning#5915)) - Added `teardown()` hook to LightningDataModule ([#4673](Lightning-AI/pytorch-lightning#4673)) - Added `auto_insert_metric_name` parameter to `ModelCheckpoint` ([#6277](Lightning-AI/pytorch-lightning#6277)) - Added arg to `self.log` that enables users to give custom names when dealing with multiple dataloaders ([#6274](Lightning-AI/pytorch-lightning#6274)) - Added `teardown` method to `BaseProfiler` to enable subclasses defining post-profiling steps outside of `__del__` ([#6370](Lightning-AI/pytorch-lightning#6370)) - Added `setup` method to `BaseProfiler` to enable subclasses defining pre-profiling steps for every process ([#6633](Lightning-AI/pytorch-lightning#6633)) - Added no return warning to predict ([#6139](Lightning-AI/pytorch-lightning#6139)) - Added `Trainer.predict` config validation ([#6543](Lightning-AI/pytorch-lightning#6543)) - Added `AbstractProfiler` interface ([#6621](Lightning-AI/pytorch-lightning#6621)) - Added support for including module names for forward in the autograd trace of `PyTorchProfiler` ([#6349](Lightning-AI/pytorch-lightning#6349)) - Added support for the PyTorch 1.8.1 autograd profiler ([#6618](Lightning-AI/pytorch-lightning#6618)) - Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](Lightning-AI/pytorch-lightning#6120)) - Added `configure_sharded_model` hook ([#6679](Lightning-AI/pytorch-lightning#6679)) - Added support for `precision=64`, enabling training with double precision ([#6595](Lightning-AI/pytorch-lightning#6595)) - Added support for DDP communication hooks ([#6736](Lightning-AI/pytorch-lightning#6736)) - Added `artifact_location` argument to `MLFlowLogger` which will be passed to the `MlflowClient.create_experiment` call ([#6677](Lightning-AI/pytorch-lightning#6677)) - Added `model` parameter to precision plugins' `clip_gradients` signature ([#6764](Lightning-AI/pytorch-lightning#6764)) ### Changed - Renamed `pytorch_lightning.callbacks.swa` to `pytorch_lightning.callbacks.stochastic_weight_avg` ([#6259](Lightning-AI/pytorch-lightning#6259)) - Refactor `RunningStage` and `TrainerState` usage ([#4945](Lightning-AI/pytorch-lightning#4945)) - Changed `trainer.evaluating` to return `True` if validating or testing ([#4945](Lightning-AI/pytorch-lightning#4945)) - Changed `setup()` and `teardown()` stage argument to take any of `{fit,validate,test,predict}` ([#6386](Lightning-AI/pytorch-lightning#6386)) - Changed profilers to save separate report files per state and rank ([#6621](Lightning-AI/pytorch-lightning#6621)) - Changed `PyTorchProfiler` to use `torch.autograd.profiler.record_function` to record functions ([#6349](Lightning-AI/pytorch-lightning#6349)) ### Deprecated - `period` has been deprecated in favor of `every_n_val_epochs` in the `ModelCheckpoint` callback ([#6146](Lightning-AI/pytorch-lightning#6146)) - Deprecated `trainer.running_sanity_check` in favor of `trainer.sanity_checking` ([#4945](Lightning-AI/pytorch-lightning#4945)) - Deprecated `Profiler(output_filename)` in favor of `dirpath` and `filename` ([#6621](Lightning-AI/pytorch-lightning#6621)) - Deprecated `PytorchProfiler(profiled_functions)` in favor of `record_functions` ([#6349](Lightning-AI/pytorch-lightning#6349)) - Deprecated metrics in favor of `torchmetrics` ([#6505](Lightning-AI/pytorch-lightning#6505), [#6530](Lightning-AI/pytorch-lightning#6530), [#6540](Lightning-AI/pytorch-lightning#6540), [#6547](Lightning-AI/pytorch-lightning#6547), [#6515](Lightning-AI/pytorch-lightning#6515), [#6572](Lightning-AI/pytorch-lightning#6572), [#6573](Lightning-AI/pytorch-lightning#6573), [#6584](Lightning-AI/pytorch-lightning#6584), [#6636](Lightning-AI/pytorch-lightning#6636), [#6637](Lightning-AI/pytorch-lightning#6637), [#6649](Lightning-AI/pytorch-lightning#6649), [#6659](Lightning-AI/pytorch-lightning#6659), ) ### Removed - Removed support for passing a bool value to `profiler` argument of Trainer ([#6164](Lightning-AI/pytorch-lightning#6164)) - Removed no return warning from val/test step ([#6139](Lightning-AI/pytorch-lightning#6139)) - Removed passing a `ModelCheckpoint` instance to `Trainer(checkpoint_callback)` ([#6166](Lightning-AI/pytorch-lightning#6166)) - Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](Lightning-AI/pytorch-lightning#6163)) - Removed deprecated metrics ([#6161](Lightning-AI/pytorch-lightning#6161)) * from `pytorch_lightning.metrics.functional.classification` removed `to_onehot`, `to_categorical`, `get_num_classes`, `roc`, `multiclass_roc`, `average_precision`, `precision_recall_curve`, `multiclass_precision_recall_curve` * from `pytorch_lightning.metrics.functional.reduction` removed `reduce`, `class_reduce` - Removed deprecated `ModelCheckpoint` arguments `prefix`, `mode="auto"` ([#6162](Lightning-AI/pytorch-lightning#6162)) - Removed `mode='auto'` from `EarlyStopping` ([#6167](Lightning-AI/pytorch-lightning#6167)) - Removed legacy references for magic keys in the `Result` object ([#6016](Lightning-AI/pytorch-lightning#6016)) - Removed deprecated `LightningModule` `hparams` setter ([#6207](Lightning-AI/pytorch-lightning#6207)) - Removed legacy code to log or include metrics in the progress bar by returning them in a dict with the `"log"/"progress_bar"` magic keys. Use `self.log` instead ([#6734](Lightning-AI/pytorch-lightning#6734)) - Removed `optimizer_idx` argument from `training_step` in manual optimization ([#6093](Lightning-AI/pytorch-lightning#6093)) ### Fixed - Set better defaults for `rank_zero_only.rank` when training is launched with SLURM and torchelastic ([#6802](Lightning-AI/pytorch-lightning#6802)) - Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](Lightning-AI/pytorch-lightning#6011)) - Move lightning module to correct device type when using LightningDistributedWrapper ([#6070](Lightning-AI/pytorch-lightning#6070)) - Do not print top-k verbose log with `ModelCheckpoint(monitor=None)` ([#6109](Lightning-AI/pytorch-lightning#6109)) - Fixed csv extension check ([#6436](Lightning-AI/pytorch-lightning#6436)) - Fixed `ModelCheckpoint(monitor=None, save_last=True)` not saving checkpoints ([#6136](Lightning-AI/pytorch-lightning#6136)) - Fixed `ModelCheckpoint(save_top_k=0, save_last=True)` not saving the `last` checkpoint ([#6136](Lightning-AI/pytorch-lightning#6136)) - Fixed `.teardown(stage='fit')` getting called during `trainer.test` ([#6386](Lightning-AI/pytorch-lightning#6386)) - Fixed `.on_fit_{start,end}()` getting called during `trainer.test` ([#6386](Lightning-AI/pytorch-lightning#6386)) - Fixed LightningModule `all_gather` on cpu tensors ([#6416](Lightning-AI/pytorch-lightning#6416)) - Fixed torch distributed not available in setup hook for DDP ([#6506](Lightning-AI/pytorch-lightning#6506)) - Fixed `EarlyStopping` logic when `min_epochs` or `min_steps` requirement is not met ([#6705](Lightning-AI/pytorch-lightning#6705)) ## [1.2.7] - 2021-04-06 ### Fixed - Fixed resolve a bug with omegaconf and xm.save ([#6741](Lightning-AI/pytorch-lightning#6741)) - Fixed an issue with IterableDataset when __len__ is not defined ([#6828](Lightning-AI/pytorch-lightning#6828)) - Sanitize None params during pruning ([#6836](Lightning-AI/pytorch-lightning#6836)) - Enforce an epoch scheduler interval when using SWA ([#6588](Lightning-AI/pytorch-lightning#6588)) - Fixed TPU Colab hang issue, post training ([#6816](Lightning-AI/pytorch-lightning#6816)) - Fixed a bug where `TensorBoardLogger` would give a warning and not log correctly to a symbolic link `save_dir` ([#6730](Lightning-AI/pytorch-lightning#6730)) ## [1.2.6] - 2021-03-30 ### Changed - Changed the behavior of `on_epoch_start` to run at the beginning of validation & test epoch ([#6498](Lightning-AI/pytorch-lightning#6498)) ### Removed - Removed legacy code to include `step` dictionary returns in `callback_metrics`. Use `self.log_dict` instead. ([#6682](Lightning-AI/pytorch-lightning#6682)) ### Fixed - Fixed `DummyLogger.log_hyperparams` raising a `TypeError` when running with `fast_dev_run=True` ([#6398](Lightning-AI/pytorch-lightning#6398)) - Fixed error on TPUs when there was no `ModelCheckpoint` ([#6654](Lightning-AI/pytorch-lightning#6654)) - Fixed `trainer.test` freeze on TPUs ([#6654](Lightning-AI/pytorch-lightning#6654)) - Fixed a bug where gradients were disabled after calling `Trainer.predict` ([#6657](Lightning-AI/pytorch-lightning#6657)) - Fixed bug where no TPUs were detected in a TPU pod env ([#6719](Lightning-AI/pytorch-lightning#6719)) ## [1.2.5] - 2021-03-23 ### Changed - Update Gradient Clipping for the TPU Accelerator ([#6576](Lightning-AI/pytorch-lightning#6576)) - Refactored setup for typing friendly ([#6590](Lightning-AI/pytorch-lightning#6590)) ### Fixed - Fixed a bug where `all_gather` would not work correctly with `tpu_cores=8` ([#6587](Lightning-AI/pytorch-lightning#6587)) - Fixed comparing required versions ([#6434](Lightning-AI/pytorch-lightning#6434)) - Fixed duplicate logs appearing in console when using the python logging module ([#6275](Lightning-AI/pytorch-lightning#6275)) - Added Autocast in validation, test and predict modes for Native AMP ([#6565](Lightning-AI/pytorch-lightning#6565)) Reviewed By: shuyingsunshine21 Differential Revision: D27528929 fbshipit-source-id: 311c88f71461c2c79bbf185e28d7a6d683ccc26f

Update stochastic_weight_avg.py

ee8b1a2

exchange whole scheduler dict in SWA

mibaumgartner requested review from Borda, carmocca, tchaton and williamFalcon as code owners March 18, 2021 21:15

tchaton approved these changes Mar 19, 2021

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carmocca added this to the 1.2.x milestone Mar 21, 2021

carmocca added bug Something isn't working callback labels Mar 21, 2021

add test

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mibaumgartner requested review from awaelchli, justusschock and SeanNaren as code owners March 23, 2021 23:01

remove blank lines

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carmocca approved these changes Mar 23, 2021

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tests/callbacks/test_stochastic_weight_avg.py Outdated Show resolved Hide resolved

remove class and add interval parameter

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carmocca added the ready PRs ready to be merged label Mar 23, 2021

carmocca added 2 commits March 24, 2021 00:36

Merge branch 'master' into bugfix/SWA_scheduler_step

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CHANGELOG

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carmocca enabled auto-merge (squash) March 23, 2021 23:39

carmocca changed the title ~~Fix SWALR step~~ Enforce an epoch scheduler interval when using SWA Mar 23, 2021

mergify bot added the has conflicts label Mar 25, 2021

Merge branch 'master' into bugfix/SWA_scheduler_step

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mergify bot added the has conflicts label Mar 30, 2021

Merge branch 'master' into bugfix/SWA_scheduler_step

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Merge branch 'master' into bugfix/SWA_scheduler_step

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carmocca merged commit 6dc1078 into Lightning-AI:master Apr 6, 2021

kaushikb11 mentioned this pull request Apr 6, 2021

1.2.x cherries 🍒 #6083

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kaushikb11 pushed a commit to kaushikb11/pytorch-lightning that referenced this pull request Apr 6, 2021

Enforce an epoch scheduler interval when using SWA (Lightning-AI#6588)

31b2d2b

Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>

lexierule pushed a commit that referenced this pull request Apr 7, 2021

Enforce an epoch scheduler interval when using SWA (#6588)

9e5d84d

Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>

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Enforce an epoch scheduler interval when using SWA #6588

Enforce an epoch scheduler interval when using SWA #6588

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Enforce an epoch scheduler interval when using SWA #6588

Enforce an epoch scheduler interval when using SWA #6588

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