- 21 Jun, 2022 1 commit
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Andrew Or authored
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
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- 17 Jun, 2022 1 commit
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Yanghan Wang authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/298 Reviewed By: tglik, newstzpz Differential Revision: D37152248 fbshipit-source-id: 58a6899c5f6465f36961a2ebf60a64f20509cec2
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- 21 May, 2022 1 commit
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Jerry Zhang authored
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
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- 15 May, 2022 1 commit
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John Reese authored
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
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- 26 Apr, 2022 1 commit
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Yanghan Wang authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/221 Reviewed By: tglik Differential Revision: D35855051 fbshipit-source-id: f742dfbc91bb7a20f632a508743fa93e3a7e9aa9
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- 19 Apr, 2022 1 commit
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Yanghan Wang authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/210 Reviewed By: kimishpatel Differential Revision: D35631192 fbshipit-source-id: a713d86734c6937c16c7ced705171db9ea2f0894
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- 08 Mar, 2022 1 commit
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Yanghan Wang authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/187 Reviewed By: ananthsub, zhanghang1989 Differential Revision: D34650467 fbshipit-source-id: b9518e5dd673b709320b87e57a43d053eca3aabe
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- 05 Mar, 2022 1 commit
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Ananth Subramaniam authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/188 Reviewed By: tangbinh, wat3rBro Differential Revision: D34658350 fbshipit-source-id: 36e8c1e8c5dab97990b1d9a5b1a58667e0e3c455
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- 04 Mar, 2022 1 commit
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Binh Tang authored
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
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- 13 Jan, 2022 1 commit
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Tsahi Glik authored
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
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- 06 Oct, 2021 1 commit
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Supriya Rao authored
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
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- 17 May, 2021 1 commit
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Kai Zhang authored
Summary: Add dataset visualization so that we could visualize test results in Tensorboard. Reviewed By: zhanghang1989 Differential Revision: D28457363 fbshipit-source-id: 4c2fd9dce349c6fb9e1cec51c9138cf0abb45d7b
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- 21 Apr, 2021 1 commit
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Kai Zhang authored
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
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- 20 Apr, 2021 1 commit
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Kai Zhang authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/49 Reviewed By: wat3rBro Differential Revision: D27875007 fbshipit-source-id: 2f61a4a3de29f3583a54adc914ee5a7eb605a823
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- 17 Apr, 2021 1 commit
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Kai Zhang authored
Summary: Delegate FX quantization callback's customization to model. Reviewed By: wat3rBro Differential Revision: D27669212 fbshipit-source-id: 2715546cf03134896da6f95ecddaf8503ff95d0b
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- 14 Apr, 2021 1 commit
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Ananth Subramaniam authored
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
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- 30 Mar, 2021 1 commit
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Sam Tsai authored
Summary: Separate unit tests into individual folder based on functionality. Reviewed By: wat3rBro Differential Revision: D27132567 fbshipit-source-id: 9a8200be530ca14c7ef42191d59795b05b9800cc
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- 24 Mar, 2021 1 commit
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Kai Zhang authored
Summary: Given that the way to create D2 (https://github.com/facebookresearch/d2go/commit/465cdb842513eb910aa20fcedea1d2edd15dc7b7)go runner and Lightning task are different, get_class was introduced so that in application we could do: ``` if is Lightning: task_cls = get_class(classname) task = task_cls(cfg) else: runner = create_runner(classname) ``` It turns out that we could need to do that in many places: workflow, binaries. This diff revert `get_class` and return class in `create_runner` if the class is a Lightning module. Reviewed By: newstzpz Differential Revision: D26676595 fbshipit-source-id: c3ce2016d09fe073af4c2dd9f98eea4e59ca621b
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- 20 Mar, 2021 1 commit
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Yanghan Wang authored
Summary: Not d2go.tests is not a library for oss, move utils code to d2go.utils.testing Reviewed By: zhanghang1989 Differential Revision: D26706933 fbshipit-source-id: 85767b66bbb6c67db05e11823beb4840220b2aa3
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- 18 Mar, 2021 1 commit
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Ananth Subramaniam authored
Summary: `checkpoint_callback` is being phased out. Initially, it was a special way to configure checkpoints, but it makes more sense for those callbacks to be included in the general `callbacks` trainer argument. In 1.2.X, `checkpoint_callback` is expected to be a boolean value only. If `checkpoint_callback=False` **and** an instance of `ModelCheckpoint` is passed in the trainer's `callbacks` arguments, Lightning raises a [misconfiguration error](https://github.com/PyTorchLightning/pytorch-lightning/blob/2f6ce1ae7fff34d16d3707571f6a9a7b0fb0c50a/pytorch_lightning/trainer/connectors/callback_connector.py#L66-L70) Reviewed By: newstzpz Differential Revision: D27139315 fbshipit-source-id: 07ad5ea520583a2e46a9cb2a938f98968265c932
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- 11 Mar, 2021 1 commit
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Kai Zhang authored
Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/17 Use PyTorch Lightning checkpoint in the test. Reviewed By: zhanghang1989 Differential Revision: D26962697 fbshipit-source-id: abe635e374c3ada130243f0eaadff34204f04fa1
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- 03 Mar, 2021 1 commit
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facebook-github-bot authored
fbshipit-source-id: f4a8ba78691d8cf46e003ef0bd2e95f170932778
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