docs: 2/3 enable Sphinx nitpicky [pytorch] part 2/n (#18602)

Author: Borda

Makefile

@@ -49,7 +49,7 @@ test: clean
 docs: clean
 	pip install -q awscli
 	aws s3 sync --no-sign-request s3://sphinx-packages/ dist/
-	pip install -e . --quiet -r requirements/pytorch/docs.txt -f dist/
+	pip install -e .[all] --quiet -r requirements/pytorch/docs.txt -f dist/
 	cd docs/source-pytorch && $(MAKE) html --jobs $(nproc)
 
 update:

docs/source-app/code_samples/convert_pl_to_app/train.py

@@ -7,7 +7,7 @@
 from torchvision import transforms as T
 from torchvision.datasets import MNIST
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 
 class LitAutoEncoder(pl.LightningModule):

docs/source-pytorch/cli/lightning_cli_advanced.rst

@@ -196,15 +196,15 @@ configuration files and automatic creation of objects, so you don't need to do i
     To somewhat overcome these limitations, there is a special key ``dict_kwargs`` that can be used
     to provide arguments that will not be validated during parsing, but will be used for class instantiation.
 
-    For example, then using the ``pytorch_lightning.profilers.PyTorchProfiler`` profiler,
+    For example, then using the ``lightning.pytorch.profilers.PyTorchProfiler`` profiler,
     the ``profile_memory`` argument has a type that is determined dynamically. As a result, it's not possible
     to know the expected type during parsing. To account for this, your config file should be set up like this:
 
     .. code:: yaml
 
         trainer:
           profiler:
-            class_path: pytorch_lightning.profilers.PyTorchProfiler
+            class_path: lightning.pytorch.profilers.PyTorchProfiler
             dict_kwargs:
               profile_memory: true
 

docs/source-pytorch/conf.py

@@ -94,7 +94,9 @@ def _load_py_module(name: str, location: str) -> ModuleType:
 assist_local.AssistantCLI.pull_docs_files(
     gh_user_repo="Lightning-AI/lightning-Habana",
     target_dir="docs/source-pytorch/integrations/hpu",
-    checkout="tags/1.0.0",
+    # todo: update after release
+    # checkout="tags/1.0.0",
+    checkout="tags/1.1.0.dev",
 )
 
 if not _FAST_DOCS_DEV:
@@ -323,14 +325,151 @@ def _load_py_module(name: str, location: str) -> ModuleType:
     "PIL": ("https://pillow.readthedocs.io/en/stable/", None),
     "torchmetrics": ("https://torchmetrics.readthedocs.io/en/stable/", None),
     "graphcore": ("https://docs.graphcore.ai/en/latest/", None),
-    "habana": ("https://lightning-ai.github.io/lightning-Habana/", None),
+    "lightning_habana": ("https://lightning-ai.github.io/lightning-Habana/", None),
+    "tensorboardX": ("https://tensorboardx.readthedocs.io/en/stable/", None),
+    # needed for referencing App from lightning scope
+    "lightning.app": ("https://lightning.ai/docs/app/stable/", None),
+    # needed for referencing Fabric from lightning scope
+    "lightning.fabric": ("https://lightning.ai/docs/fabric/stable/", None),
+    # TODO: these are missing objects.inv
+    # "comet_ml": ("https://www.comet.com/docs/v2/", None),
+    # "neptune": ("https://docs.neptune.ai/", None),
+    # "wandb": ("https://docs.wandb.ai//", None),
 }
-nitpicky = False  # TODO: to be continued
+nitpicky = True
+
 
 nitpick_ignore = [
     ("py:class", "typing.Self"),
     # missing in generated API
     ("py:exc", "MisconfigurationException"),
+    # TODO: generated list of all existing ATM, need to be fixed
+    ("py:class", "AveragedModel"),
+    ("py:class", "CometExperiment"),
+    ("py:meth", "DataModule.__init__"),
+    ("py:class", "HPUAccelerator"),
+    ("py:class", "Tensor"),
+    ("py:class", "_PATH"),
+    ("py:func", "add_argument"),
+    ("py:func", "add_class_arguments"),
+    ("py:meth", "apply_to_collection"),
+    ("py:attr", "best_model_path"),
+    ("py:attr", "best_model_score"),
+    ("py:attr", "checkpoint_path"),
+    ("py:class", "comet_ml.ExistingExperiment"),
+    ("py:class", "comet_ml.Experiment"),
+    ("py:class", "comet_ml.OfflineExperiment"),
+    ("py:meth", "deepspeed.DeepSpeedEngine.backward"),
+    ("py:attr", "example_input_array"),
+    ("py:class", "jsonargparse._core.ArgumentParser"),
+    ("py:class", "jsonargparse._namespace.Namespace"),
+    ("py:class", "jsonargparse.core.ArgumentParser"),
+    ("py:class", "jsonargparse.namespace.Namespace"),
+    ("py:class", "lightning.fabric.accelerators.xla.XLAAccelerator"),
+    ("py:class", "lightning.fabric.loggers.csv_logs._ExperimentWriter"),
+    ("py:class", "lightning.fabric.loggers.logger._DummyExperiment"),
+    ("py:class", "lightning.fabric.plugins.precision.transformer_engine.TransformerEnginePrecision"),
+    ("py:class", "lightning.fabric.utilities.device_dtype_mixin._DeviceDtypeModuleMixin"),
+    ("py:func", "lightning.fabric.utilities.seed.seed_everything"),
+    ("py:class", "lightning.fabric.utilities.types.LRScheduler"),
+    ("py:class", "lightning.fabric.utilities.types.ReduceLROnPlateau"),
+    ("py:class", "lightning.fabric.utilities.types.Steppable"),
+    ("py:class", "lightning.fabric.wrappers._FabricOptimizer"),
+    ("py:meth", "lightning.pytorch.Callback.on_exception"),
+    ("py:class", "lightning.pytorch.LightningModule"),
+    ("py:meth", "lightning.pytorch.LightningModule.on_train_epoch_end"),
+    ("py:meth", "lightning.pytorch.LightningModule.on_validation_epoch_end"),
+    ("py:meth", "lightning.pytorch.LightningModule.save_hyperparameters"),
+    ("py:meth", "lightning.pytorch.LightningModule.test_step"),
+    ("py:meth", "lightning.pytorch.LightningModule.training_step"),
+    ("py:meth", "lightning.pytorch.LightningModule.validation_step"),
+    ("py:obj", "lightning.pytorch.accelerators.MPSAccelerator"),
+    ("py:meth", "lightning.pytorch.accelerators.accelerator.Accelerator.register_accelerators"),
+    ("py:paramref", "lightning.pytorch.callbacks.Checkpoint._sphinx_paramlinks_save_top_k"),
+    ("py:func", "lightning.pytorch.callbacks.RichProgressBar.configure_columns"),
+    ("py:meth", "lightning.pytorch.callbacks.callback.Callback.on_load_checkpoint"),
+    ("py:meth", "lightning.pytorch.callbacks.callback.Callback.on_save_checkpoint"),
+    ("py:class", "lightning.pytorch.callbacks.checkpoint.Checkpoint"),
+    ("py:meth", "lightning.pytorch.callbacks.progress.progress_bar.ProgressBar.get_metrics"),
+    ("py:class", "lightning.pytorch.callbacks.progress.rich_progress.RichProgressBarTheme"),
+    ("py:class", "lightning.pytorch.callbacks.progress.tqdm_progress.Tqdm"),
+    ("py:class", "lightning.pytorch.cli.ReduceLROnPlateau"),
+    ("py:meth", "lightning.pytorch.core.LightningDataModule.setup"),
+    ("py:meth", "lightning.pytorch.core.LightningModule.configure_model"),
+    ("py:meth", "lightning.pytorch.core.LightningModule.save_hyperparameters"),
+    ("py:meth", "lightning.pytorch.core.LightningModule.setup"),
+    ("py:meth", "lightning.pytorch.core.hooks.ModelHooks.on_after_batch_transfer"),
+    ("py:meth", "lightning.pytorch.core.hooks.ModelHooks.setup"),
+    ("py:meth", "lightning.pytorch.core.hooks.ModelHooks.transfer_batch_to_device"),
+    ("py:meth", "lightning.pytorch.core.mixins.hparams_mixin.HyperparametersMixin.save_hyperparameters"),
+    ("py:class", "lightning.pytorch.loggers.Logger"),
+    ("py:func", "lightning.pytorch.loggers.logger.rank_zero_experiment"),
+    ("py:class", "lightning.pytorch.plugins.environments.cluster_environment.ClusterEnvironment"),
+    ("py:class", "lightning.pytorch.plugins.environments.slurm_environment.SLURMEnvironment"),
+    ("py:class", "lightning.pytorch.plugins.io.wrapper._WrappingCheckpointIO"),
+    ("py:func", "lightning.pytorch.seed_everything"),
+    ("py:class", "lightning.pytorch.serve.servable_module.ServableModule"),
+    ("py:class", "lightning.pytorch.serve.servable_module_validator.ServableModuleValidator"),
+    ("py:mod", "lightning.pytorch.strategies"),
+    ("py:class", "lightning.pytorch.strategies.SingleXLAStrategy"),
+    ("py:meth", "lightning.pytorch.strategies.ddp.DDPStrategy.configure_ddp"),
+    ("py:meth", "lightning.pytorch.strategies.ddp.DDPStrategy.setup_distributed"),
+    ("py:meth", "lightning.pytorch.trainer.trainer.Trainer.lightning_module"),
+    ("py:class", "lightning.pytorch.tuner.lr_finder._LRFinder"),
+    ("py:class", "lightning.pytorch.utilities.CombinedLoader"),
+    ("py:obj", "lightning.pytorch.utilities.deepspeed.ds_checkpoint_dir"),
+    ("py:obj", "lightning.pytorch.utilities.memory.is_cuda_out_of_memory"),
+    ("py:obj", "lightning.pytorch.utilities.memory.is_cudnn_snafu"),
+    ("py:obj", "lightning.pytorch.utilities.memory.is_oom_error"),
+    ("py:obj", "lightning.pytorch.utilities.memory.is_out_of_cpu_memory"),
+    ("py:func", "lightning.pytorch.utilities.rank_zero.rank_zero_only"),
+    ("py:class", "lightning.pytorch.utilities.types.LRSchedulerConfig"),
+    ("py:class", "lightning.pytorch.utilities.types.OptimizerLRSchedulerConfig"),
+    ("py:class", "lightning_habana.pytorch.plugins.precision.HPUPrecisionPlugin"),
+    ("py:class", "lightning_habana.pytorch.strategies.HPUParallelStrategy"),
+    ("py:class", "lightning_habana.pytorch.strategies.SingleHPUStrategy"),
+    ("py:obj", "logger.experiment"),
+    ("py:class", "mlflow.tracking.MlflowClient"),
+    ("py:attr", "model"),
+    ("py:meth", "move_data_to_device"),
+    ("py:class", "neptune.Run"),
+    ("py:class", "neptune.handler.Handler"),
+    ("py:meth", "on_after_batch_transfer"),
+    ("py:meth", "on_before_batch_transfer"),
+    ("py:meth", "on_save_checkpoint"),
+    ("py:meth", "optimizer_step"),
+    ("py:class", "out_dict"),
+    ("py:meth", "prepare_data"),
+    ("py:class", "pytorch_lightning.callbacks.device_stats_monitor.DeviceStatsMonitor"),
+    ("py:meth", "setup"),
+    ("py:meth", "test_step"),
+    ("py:meth", "toggle_optimizer"),
+    ("py:class", "torch.ScriptModule"),
+    ("py:class", "torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffload"),
+    ("py:class", "torch.distributed.fsdp.fully_sharded_data_parallel.MixedPrecision"),
+    ("py:class", "torch.distributed.fsdp.fully_sharded_data_parallel.ShardingStrategy"),
+    ("py:class", "torch.distributed.fsdp.sharded_grad_scaler.ShardedGradScaler"),
+    ("py:class", "torch.distributed.fsdp.wrap.ModuleWrapPolicy"),
+    ("py:func", "torch.inference_mode"),
+    ("py:meth", "torch.mean"),
+    ("py:func", "torch.nn.Module.eval"),
+    ("py:func", "torch.no_grad"),
+    ("py:class", "torch.optim.lr_scheduler.LRScheduler"),
+    ("py:meth", "torch.set_default_tensor_type"),
+    ("py:class", "torch.utils.data.DistributedSampler"),
+    ("py:class", "torch_xla.distributed.parallel_loader.MpDeviceLoader"),
+    ("py:func", "torch_xla.distributed.xla_multiprocessing.spawn"),
+    ("py:mod", "tqdm"),
+    ("py:meth", "training_step"),
+    ("py:meth", "transfer_batch_to_device"),
+    ("py:class", "types.FrameType"),
+    ("py:class", "typing.TypeGuard"),
+    ("py:meth", "untoggle_optimizer"),
+    ("py:meth", "validation_step"),
+    ("py:class", "wandb.Artifact"),
+    ("py:func", "wandb.init"),
+    ("py:class", "wandb.sdk.lib.RunDisabled"),
+    ("py:class", "wandb.wandb_run.Run"),
 ]
 
 # -- Options for todo extension ----------------------------------------------

docs/source-pytorch/extensions/logging.rst

@@ -135,7 +135,7 @@ The :meth:`~lightning.pytorch.core.LightningModule.log` method has a few options
 * ``on_epoch``: Automatically accumulates and logs at the end of the epoch.
 * ``prog_bar``: Logs to the progress bar (Default: ``False``).
 * ``logger``: Logs to the logger like ``Tensorboard``, or any other custom logger passed to the :class:`~lightning.pytorch.trainer.trainer.Trainer` (Default: ``True``).
-* ``reduce_fx``: Reduction function over step values for end of epoch. Uses :meth:`torch.mean` by default and is not applied when a :class:`torchmetrics.Metric` is logged.
+* ``reduce_fx``: Reduction function over step values for end of epoch. Uses :func:`torch.mean` by default and is not applied when a :class:`torchmetrics.Metric` is logged.
 * ``enable_graph``: If True, will not auto detach the graph.
 * ``sync_dist``: If True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.
 * ``sync_dist_group``: The DDP group to sync across.

docs/source-pytorch/visualize/logging_advanced.rst

@@ -208,9 +208,9 @@ Whether the value will be logged only on rank 0. This will prevent synchronizati
 
 reduce_fx
 =========
-**Default:** :meth:`torch.mean`
+**Default:** :func:`torch.mean`
 
-Reduction function over step values for end of epoch. Uses :meth:`torch.mean` by default and is not applied when a :class:`torchmetrics.Metric` is logged.
+Reduction function over step values for end of epoch. Uses :func:`torch.mean` by default and is not applied when a :class:`torchmetrics.Metric` is logged.
 
 .. code-block:: python
 

docs/source-pytorch/visualize/logging_expert.rst

@@ -112,7 +112,7 @@ To build your own progress bar, subclass :class:`~lightning.pytorch.callbacks.Pr
 *******************************
 Integrate an experiment manager
 *******************************
-To create an integration between a custom logger and Lightning, subclass :class:`~lightning.pytorch.loggers.base.LightningLoggerBase`
+To create an integration between a custom logger and Lightning, subclass :class:`~lightning.pytorch.loggers.Logger`
 
 .. code-block:: python
 

src/lightning/pytorch/callbacks/checkpoint.py

@@ -4,7 +4,7 @@
 class Checkpoint(Callback):
     r"""This is the base class for model checkpointing.
 
-    Expert users may want to subclass it in case of writing custom :class:`~lightning.pytorch.callbacksCheckpoint`
+    Expert users may want to subclass it in case of writing custom :class:`~lightning.pytorch.callbacks.Checkpoint`
     callback, so that the trainer recognizes the custom class as a checkpointing callback.
 
     """

src/lightning/pytorch/callbacks/early_stopping.py

@@ -11,7 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-r"""Early Stopping ^^^^^^^^^^^^^^
+r"""
+Early Stopping
+^^^^^^^^^^^^^^
 
 Monitor a metric and stop training when it stops improving.
 

src/lightning/pytorch/callbacks/finetuning.py

@@ -11,7 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-r"""Finetuning Callback ^^^^^^^^^^^^^^^^^^^^ Freeze and unfreeze models for finetuning purposes."""
+r"""
+Finetuning Callback
+^^^^^^^^^^^^^^^^^^^^
+
+Freeze and unfreeze models for finetuning purposes.
+"""
 import logging
 from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Union