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Update clip gradients signature for precision plugins #6764

Merged
merged 4 commits into from
Mar 31, 2021
Merged

Update clip gradients signature for precision plugins #6764

merged 4 commits into from
Mar 31, 2021

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kaushikb11
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@kaushikb11 kaushikb11 commented Mar 31, 2021 •
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What does this PR do?

Required for #6152. Fully Sharded DP model manages clipping of gradients. Ref

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codecov bot commented Mar 31, 2021 •
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Codecov Report

Merging #6764 (3a1a4b9) into master (495c385) will decrease coverage by 5%.
The diff coverage is 80%.

@@           Coverage Diff           @@
##           master   #6764    +/-   ##
=======================================
- Coverage      91%     87%    -5%     
=======================================
  Files         192     192            
  Lines       12182   12183     +1     
=======================================
- Hits        11146   10596   -550     
- Misses       1036    1587   +551

@kaushikb11 kaushikb11 added this to the 1.3 milestone Mar 31, 2021
tchaton
tchaton approved these changes Mar 31, 2021
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LGTM !

@kaushikb11 kaushikb11 merged commit a72a799 into Lightning-AI:master Mar 31, 2021
@kaushikb11 kaushikb11 deleted the update/clip branch March 31, 2021 11:36
carmocca
carmocca reviewed Mar 31, 2021
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tests/plugins/test_precision_plugin.py
Comment on lines +19 to +26
def test_precision_clip_gradients_signature():

expected_params_list = ['self', 'model', 'optimizer', 'clip_val', 'norm_type']

params = signature(PrecisionPlugin.clip_gradients).parameters
params_list = [param.name for param in params.values()]

assert params_list == expected_params_list
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This test seems pointless to me. Why do we test the function signature?

The premise of testing is to compare the actual outputs to expected outputs given some inputs.

But we have neither inputs nor outputs here

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The idea was that, without the changes in the PR, the test should fail.
Also, we already have tests to test the working of clip gradients & Precision Plugins.
The test is for this specific change. But yes, the redundancy of it is debatable.

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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@carmocca carmocca carmocca left review comments

@justusschock justusschock justusschock approved these changes

@tchaton tchaton tchaton approved these changes

@SeanNaren SeanNaren SeanNaren approved these changes

@awaelchli awaelchli Awaiting requested review from awaelchli

@williamFalcon williamFalcon Awaiting requested review from williamFalcon

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