Weekly Patch Release v.1.2.7 [full merge, no squash]

CHANGELOG.md

@@ -4,6 +4,18 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [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

README.md

@@ -91,19 +91,6 @@ Lightning is rigurously tested across multiple GPUs, TPUs CPUs and against major
   </center>
 </details>
 
-<details>
-  <summary>Bleeding edge build status (1.2)</summary>
-
-  <center>
-
-  ![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.2-dev&event=push)
-  ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.2-dev&event=push)
-  ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.2-dev&event=push)
-  ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.2-dev&event=push)
-  ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.2-dev&event=push)
-  </center>
-</details>
-
 ---
 
 ## How To Use
@@ -132,22 +119,22 @@ pip install pytorch-lightning
   conda install pytorch-lightning -c conda-forge
   ```
 
-  #### Install stable - future 1.1.x
+  #### Install stable 1.2.x
 
-  the actual status of 1.1 [stable] is following:
+  the actual status of 1.2 [stable] is following:
 
-  ![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.1.x&event=push)
-  ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.1.x&event=push)
-  ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.1.x&event=push)
-  ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.1.x&event=push)
-  ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.1.x&event=push)
+  ![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.2.x&event=push)
+  ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.2.x&event=push)
+  ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.2.x&event=push)
+  ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.2.x&event=push)
+  ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.2.x&event=push)
 
   Install future release from the source
   ```bash
-  pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.1.x --upgrade
+  pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.2.x --upgrade
   ```
 
-  #### Install bleeding-edge - future 1.2
+  #### Install bleeding-edge - future 1.3
 
   Install nightly from the source (no guarantees)
   ```bash
@@ -356,27 +343,27 @@ class LitAutoEncoder(pl.LightningModule):
 - [MNIST on TPUs](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/06-mnist-tpu-training.ipynb)
 
 ###### Contrastive Learning
-- [BYOL](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol)
-- [CPC v2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#cpc-v2)
-- [Moco v2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#moco-v2)
-- [SIMCLR](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr)
+- [BYOL](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol)
+- [CPC v2](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#cpc-v2)
+- [Moco v2](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#moco-v2)
+- [SIMCLR](https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr)
 
 ###### NLP
 - [BERT](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb)
-- [GPT-2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
+- [GPT-2](https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
 
 
 ###### Reinforcement Learning
-- [DQN](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dqn-models)
-- [Dueling-DQN](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dueling-dqn)
-- [Reinforce](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#reinforce)
+- [DQN](https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dqn-models)
+- [Dueling-DQN](https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dueling-dqn)
+- [Reinforce](https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#reinforce)
 
 ###### Vision
 - [GAN](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/03-basic-gan.ipynb)
 
 ###### Classic ML
-- [Logistic Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#logistic-regression)
-- [Linear Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#linear-regression)
+- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/latest/classic_ml.html#logistic-regression)
+- [Linear Regression](https://lightning-bolts.readthedocs.io/en/latest/classic_ml.html#linear-regression)
 
 ---
 

docs/source/advanced/tpu.rst

@@ -64,8 +64,7 @@ To get a TPU on colab, follow these steps:
 
    .. code-block::
 
-        !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
-        !python pytorch-xla-env-setup.py --version 1.7 --apt-packages libomp5 libopenblas-dev
+        !pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl
 
 5. Once the above is done, install PyTorch Lightning (v 0.7.0+).
 

docs/source/common/lightning_module.rst

@@ -907,30 +907,6 @@ use_amp
 ~~~~~~~
 True if using Automatic Mixed Precision (AMP)
 
-------------
-
-use_ddp
-~~~~~~~
-True if using ddp
-
-------------
-
-use_ddp2
-~~~~~~~~
-True if using ddp2
-
-------------
-
-use_dp
-~~~~~~
-True if using dp
-
-------------
-
-use_tpu
-~~~~~~~
-True if using TPUs
-
 --------------
 
 automatic_optimization

docs/source/ecosystem/bolts.rst

@@ -1,11 +1,11 @@
 Bolts
 =====
-`PyTorch Lightning Bolts <https://pytorch-lightning-bolts.readthedocs.io/en/latest/>`_, is our official collection
+`PyTorch Lightning Bolts <https://lightning-bolts.readthedocs.io/en/latest/>`_, is our official collection
 of prebuilt models across many research domains.
 
 .. code-block:: bash
 
-    pip install pytorch-lightning-bolts
+    pip install lightning-bolts
 
 In bolts we have:
 

docs/source/extensions/callbacks.rst

@@ -71,10 +71,10 @@ Examples
 --------
 You can do pretty much anything with callbacks.
 
-- `Add a MLP to fine-tune self-supervised networks <https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_callbacks.html#sslonlineevaluator>`_.
-- `Find how to modify an image input to trick the classification result <https://pytorch-lightning-bolts.readthedocs.io/en/latest/vision_callbacks.html#confused-logit>`_.
-- `Interpolate the latent space of any variational model <https://pytorch-lightning-bolts.readthedocs.io/en/latest/variational_callbacks.html#latent-dim-interpolator>`_.
-- `Log images to Tensorboard for any model <https://pytorch-lightning-bolts.readthedocs.io/en/latest/vision_callbacks.html#tensorboard-image-generator>`_.
+- `Add a MLP to fine-tune self-supervised networks <https://lightning-bolts.readthedocs.io/en/latest/self_supervised_callbacks.html#sslonlineevaluator>`_.
+- `Find how to modify an image input to trick the classification result <https://lightning-bolts.readthedocs.io/en/latest/vision_callbacks.html#confused-logit>`_.
+- `Interpolate the latent space of any variational model <https://lightning-bolts.readthedocs.io/en/latest/variational_callbacks.html#latent-dim-interpolator>`_.
+- `Log images to Tensorboard for any model <https://lightning-bolts.readthedocs.io/en/latest/vision_callbacks.html#tensorboard-image-generator>`_.
 
 
 --------------
@@ -85,7 +85,7 @@ Lightning has a few built-in callbacks.
 
 .. note::
     For a richer collection of callbacks, check out our
-    `bolts library <https://pytorch-lightning-bolts.readthedocs.io/en/latest/callbacks.html>`_.
+    `bolts library <https://lightning-bolts.readthedocs.io/en/latest/callbacks.html>`_.
 
 .. currentmodule:: pytorch_lightning.callbacks
 

docs/source/index.rst

@@ -79,14 +79,14 @@ PyTorch Lightning Documentation
 
    ecosystem/pytorch_ecoystem
    ecosystem/community_examples
-   Autoencoder <https://pytorch-lightning-bolts.readthedocs.io/en/latest/autoencoders.html#autoencoders>
-   BYOL <https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol>
-   DQN <https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#deep-q-network-dqn>
-   GAN <https://pytorch-lightning-bolts.readthedocs.io/en/latest/gans.html#basic-gan>
-   GPT-2 <https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2>
-   Image-GPT <https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#image-gpt>
-   SimCLR <https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr>
-   VAE <https://pytorch-lightning-bolts.readthedocs.io/en/latest/autoencoders.html#basic-vae>
+   Autoencoder <https://lightning-bolts.readthedocs.io/en/latest/autoencoders.html#autoencoders>
+   BYOL <https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol>
+   DQN <https://lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#deep-q-network-dqn>
+   GAN <https://lightning-bolts.readthedocs.io/en/latest/gans.html#basic-gan>
+   GPT-2 <https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2>
+   Image-GPT <https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#image-gpt>
+   SimCLR <https://lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr>
+   VAE <https://lightning-bolts.readthedocs.io/en/latest/autoencoders.html#basic-vae>
 
 .. toctree::
    :maxdepth: 1

docs/source/starter/introduction_guide.rst

@@ -572,9 +572,7 @@ Next, install the required xla library (adds support for PyTorch on TPUs)
 
 .. code-block:: shell
 
-    !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
-
-    !python pytorch-xla-env-setup.py --version nightly --apt-packages libomp5 libopenblas-dev
+    !pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl
 
 In distributed training (multiple GPUs and multiple TPU cores) each GPU or TPU core will run a copy
 of this program. This means that without taking any care you will download the dataset N times which

notebooks/07-cifar10-baseline.ipynb

@@ -61,7 +61,7 @@
         "id": "ziAQCrE-TYWG"
       },
       "source": [
-        "! pip install pytorch-lightning pytorch-lightning-bolts -qU"
+        "! pip install pytorch-lightning lightning-bolts -qU"
       ],
       "execution_count": null,
       "outputs": []

pl_examples/README.md

@@ -1,6 +1,6 @@
 # Examples
 Our most robust examples showing all sorts of implementations
-can be found in our sister library [PyTorch-Lightning-Bolts](https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2).
+can be found in our sister library [PyTorch-Lightning-Bolts](https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2).
 
 ---
 
@@ -15,5 +15,5 @@ In this folder we add 3 simple examples:
 
 ## Domain examples
 This folder contains older examples. You should instead use the examples
-in [PyTorch-Lightning-Bolts](https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
+in [PyTorch-Lightning-Bolts](https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
 for advanced use cases.

pl_examples/basic_examples/conv_sequential_example.py

@@ -202,7 +202,7 @@ def instantiate_datamodule(args):
 if __name__ == "__main__":
     cli_lightning_logo()
 
-    assert _BOLTS_AVAILABLE, "Bolts is required for this example, install it via pip install pytorch-lightning-bolts"
+    assert _BOLTS_AVAILABLE, "Bolts is required for this example, install it via `pip install lightning-bolts`"
     assert _FAIRSCALE_PIPE_AVAILABLE, "FairScale and PyTorch 1.6 is required for this example."
 
     parser = ArgumentParser(description="Pipe Example")

pytorch_lightning/callbacks/finetuning.py

@@ -77,7 +77,7 @@ def finetune_function(self, pl_module, current_epoch, optimizer, optimizer_idx):
                 # When `current_epoch` is 10, feature_extractor will start training.
                 if current_epoch == self._unfreeze_at_epoch:
                     self.unfreeze_and_add_param_group(
-                        module=pl_module.feature_extractor,
+                        modules=pl_module.feature_extractor,
                         optimizer=optimizer,
                         train_bn=True,
                     )

pytorch_lightning/callbacks/progress.py

@@ -146,9 +146,10 @@ def total_val_batches(self) -> int:
         validation dataloader is of infinite size.
         """
         total_val_batches = 0
-        if not self.trainer.disable_validation:
-            is_val_epoch = (self.trainer.current_epoch) % self.trainer.check_val_every_n_epoch == 0
+        if self.trainer.enable_validation:
+            is_val_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
             total_val_batches = sum(self.trainer.num_val_batches) if is_val_epoch else 0
+
         return total_val_batches
 
     @property

pytorch_lightning/callbacks/pruning.py

@@ -422,7 +422,9 @@ def sanitize_parameters_to_prune(
         current_modules = [m for m in pl_module.modules() if not isinstance(m, _MODULE_CONTAINERS)]
 
         if parameters_to_prune is None:
-            parameters_to_prune = [(m, p) for p in parameters for m in current_modules if hasattr(m, p)]
+            parameters_to_prune = [
+                (m, p) for p in parameters for m in current_modules if getattr(m, p, None) is not None
+            ]
         elif (
             isinstance(parameters_to_prune, (list, tuple)) and len(parameters_to_prune) > 0
             and all(len(p) == 2 for p in parameters_to_prune)

pytorch_lightning/callbacks/swa.py

@@ -189,14 +189,15 @@ def on_train_epoch_start(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningMo
                 anneal_strategy=self._annealing_strategy,
                 last_epoch=trainer.max_epochs if self._annealing_strategy == "cos" else -1
             )
+            _scheduler_config = _get_default_scheduler_config()
+            assert _scheduler_config["interval"] == "epoch" and _scheduler_config["frequency"] == 1
+            _scheduler_config["scheduler"] = self._swa_scheduler
 
             if trainer.lr_schedulers:
                 lr_scheduler = trainer.lr_schedulers[0]["scheduler"]
                 rank_zero_warn(f"Swapping lr_scheduler {lr_scheduler} for {self._swa_scheduler}")
-                trainer.lr_schedulers[0]["scheduler"] = self._swa_scheduler
+                trainer.lr_schedulers[0] = _scheduler_config
             else:
-                _scheduler_config = _get_default_scheduler_config()
-                _scheduler_config["scheduler"] = self._swa_scheduler
                 trainer.lr_schedulers.append(_scheduler_config)
 
             self.n_averaged = torch.tensor(0, dtype=torch.long, device=pl_module.device)

pytorch_lightning/info.py

@@ -1,7 +1,7 @@
 import time
 
 _this_year = time.strftime("%Y")
-__version__ = '1.2.6'
+__version__ = '1.2.7'
 __author__ = 'William Falcon et al.'
 __author_email__ = '[email protected]'
 __license__ = 'Apache-2.0'

pytorch_lightning/plugins/training_type/ddp.py

@@ -257,12 +257,12 @@ def pre_dispatch(self):
         self.dist.rank = self.global_rank
         self.dist.device = self.root_device
 
-        if self.sync_batchnorm:
-            self.model = self.configure_sync_batchnorm(self.model)
-
         # move the model to the correct device
         self.model_to_device()
 
+        if self.sync_batchnorm:
+            self.model = self.configure_sync_batchnorm(self.model)
+
         self.configure_ddp()
 
         self.barrier()

pytorch_lightning/plugins/training_type/ddp_spawn.py

@@ -148,12 +148,12 @@ def new_process(self, process_idx, trainer, mp_queue):
         self.dist.rank = self.global_rank
         self.dist.device = self.root_device
 
-        if self.sync_batchnorm:
-            self.model = self.configure_sync_batchnorm(self.model)
-
         # move the model to the correct device
         self.model_to_device()
 
+        if self.sync_batchnorm:
+            self.model = self.configure_sync_batchnorm(self.model)
+
         self.configure_ddp()
 
         self.barrier()