GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/330

- re-enable the `test_qat`
- remove `example_input` from `DetMetaArchForTest`, since its `custom_prepare_fx` just create a tensor for avgpool.

Reviewed By: jerryzh168

Differential Revision: D37793260

fbshipit-source-id: ec7a825c61292d9c6d792f910a957c1c27832336

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/325

`prepare_for_quant_convert` is a confusing name because it only does `convert`, there's no "prepare" in it. It's actually for fx only, because eager mode always calls `torch.quantization.convert`, there's no way to customize it. So just call this `custom_convert_fx`. The new name is also unique in fbcode, so easy to do codemod later on.

This diff simply does the renaming by biggrep + replace.

Reviewed By: jerryzh168

Differential Revision: D37676717

fbshipit-source-id: e7d05eaafddc383dd432986267c945c8ebf94df4

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/321

Following up the bc-breaking change from fx graph mode quantization: https://github.com/pytorch/pytorch/pull/76496 that
added example_inputs to prepare_fx and prepare_qat_fx, we fixes the callsite related to mobile-vision and exposed
extra example_inputs in some apis

Reviewed By: wat3rBro

Differential Revision: D37163018

fbshipit-source-id: 9f0bb56659345d174a39b6d3bb4408caa553b88d

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/308

D37088095 made BC breaking changes in PyTorch, but missed
fixing a few callsites. This is the second diff to fix the affected
tests.

Reviewed By: jerryzh168, wat3rBro

Differential Revision: D37282853

fbshipit-source-id: 945c3628f53cefd2fbc2526630d1567a2b1cee25

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/298

Reviewed By: tglik, newstzpz

Differential Revision: D37152248

fbshipit-source-id: 58a6899c5f6465f36961a2ebf60a64f20509cec2

Summary:
X-link: https://github.com/pytorch/pytorch/pull/77608

X-link: https://github.com/pytorch/fx2trt/pull/76

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/249

X-link: https://github.com/fairinternal/ClassyVision/pull/104

X-link: https://github.com/pytorch/benchmark/pull/916

X-link: https://github.com/facebookresearch/ClassyVision/pull/791

X-link: https://github.com/facebookresearch/mobile-vision/pull/68

FX Graph Mode Quantization needs to know whether an fx node is a floating point Tensor before it can decide whether to
insert observer/fake_quantize module or not, since we only insert observer/fake_quantize module for floating point Tensors.
Currently we have some hacks to support this by defining some rules like NON_OBSERVABLE_ARG_DICT (https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/fx/utils.py#L496), but this approach is fragile and we do not plan to maintain it long term in the pytorch code base.

As we discussed in the design review, we'd need to ask users to provide sample args and sample keyword args
so that we can infer the type in a more robust way. This PR starts with changing the prepare_fx and prepare_qat_fx api to require user to either provide
example arguments thrugh example_inputs, Note this api doesn't support kwargs, kwargs can make https://github.com/pytorch/pytorch/pull/76496#discussion_r861230047 (comment) simpler, but
it will be rare, and even then we can still workaround with positional arguments, also torch.jit.trace(https://pytorch.org/docs/stable/generated/torch.jit.trace.html) and ShapeProp: https://github.com/pytorch/pytorch/blob/master/torch/fx/passes/shape_prop.py#L140 just have single positional args, we'll just use a single example_inputs argument for now.

If needed, we can extend the api with an optional example_kwargs. e.g. in case when there are a lot of arguments for forward and it makes more sense to
pass the arguments by keyword

BC-breaking Note:
Before:
```python
m = resnet18(...)
m = prepare_fx(m, qconfig_dict)
# or
m = prepare_qat_fx(m, qconfig_dict)
```
After:
```python
m = resnet18(...)
m = prepare_fx(m, qconfig_dict, example_inputs=(torch.randn(1, 3, 224, 224),))
# or
m = prepare_qat_fx(m, qconfig_dict, example_inputs=(torch.randn(1, 3, 224, 224),))
```

Reviewed By: vkuzo, andrewor14

Differential Revision: D35984526

fbshipit-source-id: 706c8df71722c9aa5082a6491734f0144f0dd670

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/245

At the moment D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go's runner still uses the OSS pattern 1 (see wiki), where the files get remapped. This does not work with D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go, and makes it necessary to use some renaming tricks.

This diff refactors the runner setup, to reduce the number of classes, and rely on fb_overwrite to add the correct fields to the config.

Reviewed By: wat3rBro

Differential Revision: D36316955

fbshipit-source-id: 4aaaece121b8df802f9395648c97a647fa7db857

Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: lisroach

Differential Revision: D36402205

fbshipit-source-id: a4efc688d02da80c6e96685aa8eb00411615a366

Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/221

Reviewed By: tglik

Differential Revision: D35855051

fbshipit-source-id: f742dfbc91bb7a20f632a508743fa93e3a7e9aa9

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/210

Reviewed By: kimishpatel

Differential Revision: D35631192

fbshipit-source-id: a713d86734c6937c16c7ced705171db9ea2f0894

Reviewed By: kazhang

Differential Revision: D34669519

fbshipit-source-id: 8cfee968104c823a55960f2730d8e888ac1f298e

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/187

Reviewed By: ananthsub, zhanghang1989

Differential Revision: D34650467

fbshipit-source-id: b9518e5dd673b709320b87e57a43d053eca3aabe

Reviewed By: tangbinh

Differential Revision: D34669294

fbshipit-source-id: c87bc1d4c589518f7c9fc21e6dfe27b03e700b6d

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/188

Reviewed By: tangbinh, wat3rBro

Differential Revision: D34658350

fbshipit-source-id: 36e8c1e8c5dab97990b1d9a5b1a58667e0e3c455

Summary:
### New commit log messages
- [7e2f9fbad Refactor codebase to use `trainer.loggers` over `trainer.logger` when needed (#11920)](https://github.com/PyTorchLightning/pytorch-lightning/pull/11920)

Reviewed By: edward-io

Differential Revision: D34583686

fbshipit-source-id: 98e557b761555c24ff296fff3ec6881d141fa777

Summary: We proactively remove references to the deprecated DDP accelerator to prepare for the breaking changes following the release of PyTorch Lighting 1.6 (see T112240890).

Differential Revision: D34295318

fbshipit-source-id: 7b2245ca9c7c2900f510722b33af8d8eeda49919

Summary:
Add support in the default lightning task to run a custom training step from Meta Arch if exists.
The goal is to allow custom training step without the need to inherit from the default lightning task class and override it. This will allow us to use a signle lightning task and still allow users to customize the training step. In the long run this will be further encapsulated in modeling hook, making it more modular and compositable with other custom code.

This change is a follow up from discussion in  https://fburl.com/diff/yqlsypys

Reviewed By: wat3rBro

Differential Revision: D33534624

fbshipit-source-id: 560f06da03f218e77ad46832be9d741417882c56

Summary: DDPPlugin has been renamed to DDPStrategy (as part of https://github.com/PyTorchLightning/pytorch-lightning/issues/10549), causing oss CI to fail. Simply skipping the import to unblock CI since DDP feature is not used in test.

Reviewed By: kazhang

Differential Revision: D33351636

fbshipit-source-id: 7a1881c8cd48d9ff17edd41137d27a976103fdde

Summary:
### New commit log messages
  fa0ed17f8 remove deprecated train_loop (#10482)

Reviewed By: kandluis

Differential Revision: D32454980

fbshipit-source-id: a35237dde06cc9ddac5373b75992ce88a6771c76

Summary:
In quantization callback, we prepare the model with FX quantization API and only use the prepared model in training.
However, when training in DDP, the parameters in the origin model still require grad, causing unused parameters RuntimeError.
Previously, Lightning trainer train the model with find_unused_param flag, but if user manually disable it, they will get the runtime error.

In this diff, the parameters in the origin model are frozen. We could consider deleting the origin model after preparation to save memory, but we might have to make some assumption on Lightning module structure, for example, `.model` is the origin model, so that we could `delattr(pl_module, "model")`.

Reviewed By: wat3rBro

Differential Revision: D31902368

fbshipit-source-id: 56eabb6b2296278529dd2b94d6aa4c9ec9e9ca6b

Summary:
Supported learnable qat.
* Added a config key `QUANTIZATION.QAT.FAKE_QUANT_METHOD` to specify the qat metod (`default` or `learnable`).
* Added a config key `QUANTIZATION.QAT.ENABLE_LEARNABLE_OBSERVER_ITER` to specify the start iteration for learnable observers (before that it is using static observers).
* Custom quantization code needs to call ` d2go.utils.qat_utils.get_qat_qconfig()` to get proper qconfig for learnable qat. An exception will raise if qat method is learnable but no learnable observers are used in the model.
* Set the weight decay for scale/zero_point to 0 for the optimizer automatically.
* The way to use larnable qat: enable static observers -> enable fake quant -> enable learnable observers -> freeze bn.

Differential Revision: D31370822

fbshipit-source-id: a5a5044a539d0d7fe1cc6b36e6821fc411ce752a

Summary:
Refactored qat related code.
* Moved `_prepare_model_for_qat` related code to a function.
* Moved `_setup_non_qat_to_qat_state_dict_map` related code to a function.
* Moved QATHook related code to the quantization file and implemented as a class.

Differential Revision: D31370819

fbshipit-source-id: 836550b2c8d68cd93a84d5877ad9cef6f0f0eb39

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/124

Update callsites from torch.quantization to torch.ao.quantization

Reviewed By: z-a-f, jerryzh168

Differential Revision: D31286125

fbshipit-source-id: ef24ca87d8db398c65bb5b89f035afe0423a5685

Summary: Add dataset visualization so that we could visualize test results in Tensorboard.

Reviewed By: zhanghang1989

Differential Revision: D28457363

fbshipit-source-id: 4c2fd9dce349c6fb9e1cec51c9138cf0abb45d7b

Synchronize PyTorchLightning/pytorch-lightning (revision 7b283e3c@master) to github/third-party/PyTorchLightning/pytorch-lightning

Summary:
# Manual
 - remove fixme's in `model_checkpoint.py`, `parameter_monitor.py`, `test_quantization.py`, and `speed_monitor.py` now that `Trainer` is properly annotated.
- update `test_quantization.py` to `trainer.train_loop.global_step` instead of `trainer.global_step` which is a read-only.
- update `loop_callback.py` to read from `train_loop` for `batch_idx` (which is no longer available).

# Automatic
### New commit log messages
  7b283e3c Bugfix/Multiple dataloaders (#7433)
  d7c44cc6 Docs: sync chlog 1.3.1 (#7478)
  fdf50a5e Mark certain Trainer APIs as protected (#7420)
  ad9118f0 remove trainer hidden state | sanity refactor [1 / n] (#7437)
  4a1134db Log epoch metrics before firing the `on_evaluation_end` hook (#7272)
  b65ae794 Automatically check `DataModule.has_{setup,teardown,prepare_data}` [2/2] (#7238)
  8660d8cf [pre-commit.ci] pre-commit autoupdate (#7475)
  f6fe715e Fix Sphinx argument deprecation (#7464)

Reviewed By: shuyingsunshine21

Differential Revision: D28353491

fbshipit-source-id: 98b87d99e2f09b47b07270858fcbdb5d5299730b

Synchronize PyTorchLightning/pytorch-lightning (revision 7fe8d184@master) to github/third-party/PyTorchLightning/pytorch-lightning

Summary:
### New commit log messages
  7fe8d184 Do not `shuffle` in `LightningDataModule.from_datasets` for `IterableDataset` (#7053)
  bab72255 [fix] Add barriers before and after setup hook is run (#7202)
  f920ba29 [bugfix] Metric not logged properly in manual optimization (#7228)
  e147127c [feat] Add better support for predict + ddp 2/3 (#7215)
  ca6c87ff Add back `clip_gradients(model)` (#7231)
  3b36d81c Fixed `num_sanity_val_steps` affecting reproducibility of training data shuffling (#7014)
  5cf9afa1 Add fairscale install msg for Sharded Plugins (#7213)
  52a5cee0 Set smarter default for DDP sharded for performance optimization (#6937)
  dd5ec75e Deprecate save_function from model checkpoint callback (#7201)
  ac7d6a35 Fix `NeptuneLogger.log_text(step=None)` (#7194)
  6be0a859 Update teardown for TPU acc (#7211)
  bc3f08b0 [fix] Add barrier to accelerator's teardown (#6814)
  68eac4d9 Enforce Lightning module as source of truth for automatic optimization (#7130)
  44d775fc Update Error message for ProfileConnector (#7204)
  31fcd7d0 Deprecate write_predictions on the LightningModule (#7066)
  591b9cee make bug_report_model minimal (#7191)
  b3fe8366 Move metrics_to_scalars to a dedicated utilities file (#7180)
  f58865aa Properly set `LightningModule.device` after model replacement (#7188)
  8439aead Update FairScale on CI (#7017)
  92af3632 Fix `lr_finder` suggesting too high learning rates (#7076)
  d534e53e add missing predict docs (#7150)

Reviewed By: kazhang

Differential Revision: D28032962

fbshipit-source-id: 18cd01e8ecc13fe25f0890ac0f4b20c3c3e1fed3

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/46

As titled. The test is flaky because the tensorboard logger might still be writing to temporary folder when we tear down the folder.

Reviewed By: ananthsub

Differential Revision: D27844504

fbshipit-source-id: 3987f9ec3cd05b2f193e75cd4d85109a46f4ee71

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/49

Reviewed By: wat3rBro

Differential Revision: D27875007

fbshipit-source-id: 2f61a4a3de29f3583a54adc914ee5a7eb605a823

Summary:
* Added a registry to register functions that could be used to register hooks for training.
  * TRAINER_HOOKS_REGISTRY: List of functions to add hooks for trainer, all functions in the registry will be called to add hooks
  * `func(hooks: List[HookBase]) -> None`

Reviewed By: zhanghang1989

Differential Revision: D27560806

fbshipit-source-id: fcfa02623bfd08508b6083db2d318d08f7e3c0b8

Summary: Delegate FX quantization callback's customization to model.

Reviewed By: wat3rBro

Differential Revision: D27669212

fbshipit-source-id: 2715546cf03134896da6f95ecddaf8503ff95d0b

Synchronize PyTorchLightning/pytorch-lightning (revision 0b843848@master) 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](https://github.com/PyTorchLightning/pytorch-lightning/pull/6667))

- Added `LightningCLI` class to provide simple reproducibility with minimum boilerplate training cli. ([#4492](https://github.com/PyTorchLightning/pytorch-lightning/pull/4492))

- Trigger warning when non-metric logged value with multi processes hasn't been reduced ([#6417](https://github.com/PyTorchLightning/pytorch-lightning/pull/6417))

- Added `gradient_clip_algorithm` argument to Trainer for gradient clipping by value ([#6123](https://github.com/PyTorchLightning/pytorch-lightning/pull/6123)).

- Added a way to print to terminal without breaking up the progress bar ([#5470](https://github.com/PyTorchLightning/pytorch-lightning/pull/5470))

- Added support to checkpoint after training steps in `ModelCheckpoint` callback ([#6146](https://github.com/PyTorchLightning/pytorch-lightning/pull/6146))

- Added `checkpoint` parameter to callback's `on_save_checkpoint` hook ([#6072](https://github.com/PyTorchLightning/pytorch-lightning/pull/6072))

- Added `RunningStage.SANITY_CHECKING` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Added `TrainerState.{FITTING,VALIDATING,TESTING,PREDICTING,TUNING}` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Added `Trainer.validate()` method to perform one evaluation epoch over the validation set ([#4948](https://github.com/PyTorchLightning/pytorch-lightning/pull/4948))

- Added `LightningEnvironment` for Lightning-specific DDP ([#5915](https://github.com/PyTorchLightning/pytorch-lightning/pull/5915))

- Added `teardown()` hook to LightningDataModule ([#4673](https://github.com/PyTorchLightning/pytorch-lightning/pull/4673))

- Added `auto_insert_metric_name` parameter to `ModelCheckpoint` ([#6277](https://github.com/PyTorchLightning/pytorch-lightning/pull/6277))

- Added arg to `self.log` that enables users to give custom names when dealing with multiple dataloaders ([#6274](https://github.com/PyTorchLightning/pytorch-lightning/pull/6274))

- Added `teardown` method to `BaseProfiler` to enable subclasses defining post-profiling steps outside of `__del__` ([#6370](https://github.com/PyTorchLightning/pytorch-lightning/pull/6370))

- Added `setup` method to `BaseProfiler` to enable subclasses defining pre-profiling steps for every process ([#6633](https://github.com/PyTorchLightning/pytorch-lightning/pull/6633))

- Added no return warning to predict ([#6139](https://github.com/PyTorchLightning/pytorch-lightning/pull/6139))

- Added `Trainer.predict` config validation ([#6543](https://github.com/PyTorchLightning/pytorch-lightning/pull/6543))

- Added `AbstractProfiler` interface ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))

- Added support for including module names for forward in the autograd trace of `PyTorchProfiler` ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))

- Added support for the PyTorch 1.8.1 autograd profiler ([#6618](https://github.com/PyTorchLightning/pytorch-lightning/pull/6618))

- Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](https://github.com/PyTorchLightning/pytorch-lightning/pull/6120))

- Added `configure_sharded_model` hook ([#6679](https://github.com/PyTorchLightning/pytorch-lightning/pull/6679))

- Added support for `precision=64`, enabling training with double precision ([#6595](https://github.com/PyTorchLightning/pytorch-lightning/pull/6595))

- Added support for DDP communication hooks ([#6736](https://github.com/PyTorchLightning/pytorch-lightning/issues/6736))

- Added `artifact_location` argument to `MLFlowLogger` which will be passed to the `MlflowClient.create_experiment` call ([#6677](https://github.com/PyTorchLightning/pytorch-lightning/pull/6677))

- Added `model` parameter to precision plugins' `clip_gradients` signature ([#6764](https://github.com/PyTorchLightning/pytorch-lightning/pull/6764))

### Changed

- Renamed `pytorch_lightning.callbacks.swa` to `pytorch_lightning.callbacks.stochastic_weight_avg` ([#6259](https://github.com/PyTorchLightning/pytorch-lightning/pull/6259))

- Refactor `RunningStage` and `TrainerState` usage ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Changed `trainer.evaluating` to return `True` if validating or testing ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Changed `setup()` and `teardown()` stage argument to take any of `{fit,validate,test,predict}` ([#6386](https://github.com/PyTorchLightning/pytorch-lightning/pull/6386))

- Changed profilers to save separate report files per state and rank ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))

- Changed `PyTorchProfiler` to use `torch.autograd.profiler.record_function` to record functions ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))

### Deprecated

- `period` has been deprecated in favor of `every_n_val_epochs` in the `ModelCheckpoint` callback ([#6146](https://github.com/PyTorchLightning/pytorch-lightning/pull/6146))

- Deprecated `trainer.running_sanity_check` in favor of `trainer.sanity_checking` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Deprecated `Profiler(output_filename)` in favor of `dirpath` and `filename` ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))

- Deprecated `PytorchProfiler(profiled_functions)` in favor of `record_functions` ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))

- Deprecated metrics in favor of `torchmetrics` ([#6505](https://github.com/PyTorchLightning/pytorch-lightning/pull/6505),
    [#6530](https://github.com/PyTorchLightning/pytorch-lightning/pull/6530),
    [#6540](https://github.com/PyTorchLightning/pytorch-lightning/pull/6540),
    [#6547](https://github.com/PyTorchLightning/pytorch-lightning/pull/6547),
    [#6515](https://github.com/PyTorchLightning/pytorch-lightning/pull/6515),
    [#6572](https://github.com/PyTorchLightning/pytorch-lightning/pull/6572),
    [#6573](https://github.com/PyTorchLightning/pytorch-lightning/pull/6573),
    [#6584](https://github.com/PyTorchLightning/pytorch-lightning/pull/6584),
    [#6636](https://github.com/PyTorchLightning/pytorch-lightning/pull/6636),
    [#6637](https://github.com/PyTorchLightning/pytorch-lightning/pull/6637),
    [#6649](https://github.com/PyTorchLightning/pytorch-lightning/pull/6649),
    [#6659](https://github.com/PyTorchLightning/pytorch-lightning/pull/6659),
)

### Removed

- Removed support for passing a bool value to `profiler` argument of Trainer ([#6164](https://github.com/PyTorchLightning/pytorch-lightning/pull/6164))

- Removed no return warning from val/test step ([#6139](https://github.com/PyTorchLightning/pytorch-lightning/pull/6139))

- Removed passing a `ModelCheckpoint` instance to `Trainer(checkpoint_callback)` ([#6166](https://github.com/PyTorchLightning/pytorch-lightning/pull/6166))

- Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](https://github.com/PyTorchLightning/pytorch-lightning/pull/6163))

- Removed deprecated metrics ([#6161](https://github.com/PyTorchLightning/pytorch-lightning/pull/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](https://github.com/PyTorchLightning/pytorch-lightning/pull/6162))

- Removed `mode='auto'` from `EarlyStopping` ([#6167](https://github.com/PyTorchLightning/pytorch-lightning/pull/6167))

- Removed legacy references for magic keys in the `Result` object ([#6016](https://github.com/PyTorchLightning/pytorch-lightning/pull/6016))

- Removed deprecated `LightningModule` `hparams` setter ([#6207](https://github.com/PyTorchLightning/pytorch-lightning/pull/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](https://github.com/PyTorchLightning/pytorch-lightning/pull/6734))

- Removed `optimizer_idx` argument from `training_step` in manual optimization ([#6093](https://github.com/PyTorchLightning/pytorch-lightning/pull/6093))

### Fixed

- Set better defaults for `rank_zero_only.rank` when training is launched with SLURM and torchelastic ([#6802](https://github.com/PyTorchLightning/pytorch-lightning/pull/6802/))

- Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))

- Move lightning module to correct device type when using LightningDistributedWrapper ([#6070](https://github.com/PyTorchLightning/pytorch-lightning/pull/6070))

- Do not print top-k verbose log with `ModelCheckpoint(monitor=None)` ([#6109](https://github.com/PyTorchLightning/pytorch-lightning/pull/6109))

- Fixed csv extension check ([#6436](https://github.com/PyTorchLightning/pytorch-lightning/pull/6436))

- Fixed `ModelCheckpoint(monitor=None, save_last=True)` not saving checkpoints ([#6136](https://github.com/PyTorchLightning/pytorch-lightning/pull/6136))

- Fixed `ModelCheckpoint(save_top_k=0, save_last=True)` not saving the `last` checkpoint ([#6136](https://github.com/PyTorchLightning/pytorch-lightning/pull/6136))

- Fixed `.teardown(stage='fit')` getting called during `trainer.test` ([#6386](https://github.com/PyTorchLightning/pytorch-lightning/pull/6386))

- Fixed `.on_fit_{start,end}()` getting called during `trainer.test` ([#6386](https://github.com/PyTorchLightning/pytorch-lightning/pull/6386))

- Fixed LightningModule `all_gather` on cpu tensors ([#6416](https://github.com/PyTorchLightning/pytorch-lightning/pull/6416))

- Fixed torch distributed not available in setup hook for DDP ([#6506](https://github.com/PyTorchLightning/pytorch-lightning/pull/6506))

- Fixed `EarlyStopping` logic when `min_epochs` or `min_steps` requirement is not met ([#6705](https://github.com/PyTorchLightning/pytorch-lightning/pull/6705))

## [1.2.7] - 2021-04-06

### Fixed

- Fixed resolve a bug with omegaconf and xm.save ([#6741](https://github.com/PyTorchLightning/pytorch-lightning/pull/6741))
- Fixed an issue with IterableDataset when __len__ is not defined ([#6828](https://github.com/PyTorchLightning/pytorch-lightning/pull/6828))
- Sanitize None params during pruning ([#6836](https://github.com/PyTorchLightning/pytorch-lightning/pull/6836))
- Enforce an epoch scheduler interval when using SWA ([#6588](https://github.com/PyTorchLightning/pytorch-lightning/pull/6588))
- Fixed TPU Colab hang issue, post training ([#6816](https://github.com/PyTorchLightning/pytorch-lightning/pull/6816))
- Fixed a bug where `TensorBoardLogger` would give a warning and not log correctly to a symbolic link `save_dir` ([#6730](https://github.com/PyTorchLightning/pytorch-lightning/pull/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](https://github.com/PyTorchLightning/pytorch-lightning/pull/6498))

### Removed

- Removed legacy code to include `step` dictionary returns in `callback_metrics`. Use `self.log_dict` instead. ([#6682](https://github.com/PyTorchLightning/pytorch-lightning/pull/6682))

### Fixed

- Fixed `DummyLogger.log_hyperparams` raising a `TypeError` when running with `fast_dev_run=True` ([#6398](https://github.com/PyTorchLightning/pytorch-lightning/pull/6398))
- Fixed error on TPUs when there was no `ModelCheckpoint` ([#6654](https://github.com/PyTorchLightning/pytorch-lightning/pull/6654))
- Fixed `trainer.test` freeze on TPUs ([#6654](https://github.com/PyTorchLightning/pytorch-lightning/pull/6654))
- Fixed a bug where gradients were disabled after calling `Trainer.predict` ([#6657](https://github.com/PyTorchLightning/pytorch-lightning/pull/6657))
- Fixed bug where no TPUs were detected in a TPU pod env ([#6719](https://github.com/PyTorchLightning/pytorch-lightning/pull/6719))

## [1.2.5] - 2021-03-23

### Changed

- Update Gradient Clipping for the TPU Accelerator ([#6576](https://github.com/PyTorchLightning/pytorch-lightning/pull/6576))
- Refactored setup for typing friendly ([#6590](https://github.com/PyTorchLightning/pytorch-lightning/pull/6590))

### Fixed

- Fixed a bug where `all_gather` would not work correctly with `tpu_cores=8` ([#6587](https://github.com/PyTorchLightning/pytorch-lightning/pull/6587))
- Fixed comparing required versions ([#6434](https://github.com/PyTorchLightning/pytorch-lightning/pull/6434))
- Fixed duplicate logs appearing in console when using the python logging module ([#6275](https://github.com/PyTorchLightning/pytorch-lightning/pull/6275))
- Added Autocast in validation, test and predict modes for Native AMP ([#6565](https://github.com/PyTorchLightning/pytorch-lightning/pull/6565))

Reviewed By: shuyingsunshine21

Differential Revision: D27528929

fbshipit-source-id: 311c88f71461c2c79bbf185e28d7a6d683ccc26f

Summary: `checkpoint_callback` now only accepts boolean values: https://github.com/PyTorchLightning/pytorch-lightning/blob/19e67d18c472c3a03dec4dd9bfcef031e9ca8719/pytorch_lightning/trainer/connectors/callback_connector.py#L65-L73

Reviewed By: shuyingsunshine21

Differential Revision: D27682178

fbshipit-source-id: 9e863aad7a23a76dee8ae5df9f5a78e7a94bfe8a

Reviewed By: newstzpz

Differential Revision: D27255960

fbshipit-source-id: 1699ff23d2bc610dffc0215a90a7c1c17e3783c3

Summary: Separate unit tests into individual folder based on functionality.

Reviewed By: wat3rBro

Differential Revision: D27132567

fbshipit-source-id: 9a8200be530ca14c7ef42191d59795b05b9800cc