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[bugfix] Add support for omegaconf and tpu #6741

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merged 2 commits into from
Mar 30, 2021
Merged

[bugfix] Add support for omegaconf and tpu #6741

merged 2 commits into from
Mar 30, 2021

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tchaton
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@tchaton tchaton commented Mar 30, 2021 •
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What does this PR do?

Fixes #<issue_number>

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@tchaton tchaton added this to the 1.2.x milestone Mar 30, 2021
@tchaton tchaton self-assigned this Mar 30, 2021
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codecov bot commented Mar 30, 2021 •
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Codecov Report

Merging #6741 (09ae9c1) into master (3c86193) will decrease coverage by 4%.
The diff coverage is 67%.

@@           Coverage Diff           @@
##           master   #6741    +/-   ##
=======================================
- Coverage      91%     87%    -4%     
=======================================
  Files         192     192            
  Lines       12231   12237     +6     
=======================================
- Hits        11184   10663   -521     
- Misses       1047    1574   +527

SeanNaren
SeanNaren reviewed Mar 30, 2021
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Nice! Could you add a test case covering this?

@tchaton tchaton added the bug Something isn't working label Mar 30, 2021
@tchaton tchaton merged commit bb92754 into master Mar 30, 2021
@tchaton tchaton deleted the tpu_fix_hydra branch March 30, 2021 15:21
@kaushikb11 kaushikb11 mentioned this pull request Apr 5, 2021
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kaushikb11 pushed a commit to kaushikb11/pytorch-lightning that referenced this pull request Apr 6, 2021
@tchaton @kaushikb11
[bugfix] Add support for omegaconf and tpu (Lightning-AI#6741)
553c3e5 
* fix_hydra

* update changelog

Co-authored-by: Your Name <[email protected]>
lexierule pushed a commit that referenced this pull request Apr 7, 2021
@kaushikb11 @Borda
@tchaton @akihironitta @sailordiary @rohitgr7 @carmocca @elimohl @awaelchli @karthikprasad @sadiqj @mibaumgartner @BloodAxe @ethanwharris @THasthika
Weekly Patch Release v.1.2.7 [full merge, no squash] (#6850)
b2c7345 
* update readme by v1.2.x (#6728)

* [bugfix] Add support for omegaconf and tpu (#6741)

* fix_hydra

* update changelog

Co-authored-by: Your Name <[email protected]>

* [docs] Update Bolts link (#6743)

* Update Bolts link

* Update Bolts link

* formt

Co-authored-by: Jirka Borovec <[email protected]>

* 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 <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>

* 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 <[email protected]>

* 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 <[email protected]>

* Make `LightningLocalFileSystem` protected

Co-authored-by: Carlos Mocholí <[email protected]>

* 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 <[email protected]>
Co-authored-by: thomas chaton <[email protected]>
Co-authored-by: Your Name <[email protected]>
Co-authored-by: Akihiro Nitta <[email protected]>
Co-authored-by: Yuan-Hang Zhang <[email protected]>
Co-authored-by: rohitgr7 <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>
Co-authored-by: Elizaveta Logacheva <[email protected]>
Co-authored-by: Adrian Wälchli <[email protected]>
Co-authored-by: Karthik Prasad <[email protected]>
Co-authored-by: Sadiq Jaffer <[email protected]>
Co-authored-by: Michael Baumgartner <[email protected]>
Co-authored-by: Eugene Khvedchenya <[email protected]>
Co-authored-by: Ethan Harris <[email protected]>
Co-authored-by: Tharindu Hasthika <[email protected]>
lexierule pushed a commit that referenced this pull request Apr 7, 2021
@tchaton @lexierule
[bugfix] Add support for omegaconf and tpu (#6741)
78bfc1d 
* fix_hydra

* update changelog

Co-authored-by: Your Name <[email protected]>
facebook-github-bot pushed a commit to facebookresearch/d2go that referenced this pull request Apr 14, 2021
@facebook-github-bot
Synchronize PyTorchLightning/pytorch-lightning (revision 0b843848@mas…
44e4108 
…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
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@justusschock justusschock justusschock approved these changes

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