Document exceptions in callbacks (#5541)

* Add Raises: section to docstring

* Add Raises section to the docs

* Add raises section to the docs

* Apply suggestions from code review

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* fix

* Remove unnecessary instance check

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

pytorch_lightning/callbacks/early_stopping.py

@@ -54,6 +54,12 @@ class EarlyStopping(Callback):
         strict: whether to crash the training if `monitor` is
             not found in the validation metrics. Default: ``True``.
 
+    Raises:
+        MisconfigurationException:
+            If ``mode`` is none of ``"min"``, ``"max"``, and ``"auto"``.
+        RuntimeError:
+            If the metric ``monitor`` is not available.
+
     Example::
 
         >>> from pytorch_lightning import Trainer

pytorch_lightning/callbacks/finetuning.py

@@ -318,8 +318,12 @@ class BackboneFinetuning(BaseFinetuning):
         self.verbose = verbose
 
     def on_fit_start(self, trainer, pl_module):
-        if hasattr(pl_module, "backbone") and \
-           (isinstance(pl_module.backbone, Module) or isinstance(pl_module.backbone, Sequential)):
+        """
+        Raises:
+            MisconfigurationException:
+                If LightningModule has no nn.Module `backbone` attribute.
+        """
+        if hasattr(pl_module, "backbone") and isinstance(pl_module.backbone, Module):
             return
         raise MisconfigurationException("The LightningModule should have a nn.Module `backbone` attribute")
 

pytorch_lightning/callbacks/gpu_stats_monitor.py

@@ -48,6 +48,10 @@ class GPUStatsMonitor(Callback):
         temperature: Set to ``True`` to monitor the memory and gpu temperature in degree Celsius.
             Default: ``False``.
 
+    Raises:
+        MisconfigurationException:
+            If NVIDIA driver is not installed, not running on GPUs, or ``Trainer`` has no logger.
+
     Example::
 
         >>> from pytorch_lightning import Trainer

pytorch_lightning/callbacks/gradient_accumulation_scheduler.py

@@ -32,6 +32,13 @@ class GradientAccumulationScheduler(Callback):
     Args:
         scheduling: scheduling in format {epoch: accumulation_factor}
 
+    Raises:
+        TypeError:
+            If ``scheduling`` is an empty ``dict``,
+            or not all keys and values of ``scheduling`` are integers.
+        IndexError:
+            If ``minimal_epoch`` is less than 0.
+
     Example::
 
         >>> from pytorch_lightning import Trainer

pytorch_lightning/callbacks/lr_monitor.py

@@ -38,6 +38,10 @@ class LearningRateMonitor(Callback):
         log_momentum: option to also log the momentum values of the optimizer, if the optimizer
             has the ``momentum`` or ``betas`` attribute. Defaults to ``False``.
 
+    Raises:
+        MisconfigurationException:
+            If ``logging_interval`` is none of ``"step"``, ``"epoch"``, or ``None``.
+
     Example::
 
         >>> from pytorch_lightning import Trainer
@@ -77,6 +81,10 @@ class LearningRateMonitor(Callback):
         Called before training, determines unique names for all lr
         schedulers in the case of multiple of the same type or in
         the case of multiple parameter groups
+
+        Raises:
+            MisconfigurationException:
+                If ``Trainer`` has no ``logger``.
         """
         if not trainer.logger:
             raise MisconfigurationException(

pytorch_lightning/callbacks/model_checkpoint.py

@@ -115,6 +115,14 @@ class ModelCheckpoint(Callback):
         For example, you can change the default last checkpoint name by doing
         ``checkpoint_callback.CHECKPOINT_NAME_LAST = "{epoch}-last"``
 
+    Raises:
+        MisconfigurationException:
+            If ``save_top_k`` is neither ``None`` nor more than or equal to ``-1``,
+            if ``monitor`` is ``None`` and ``save_top_k`` is none of ``None``, ``-1``, and ``0``, or
+            if ``mode`` is none of ``"min"``, ``"max"``, and ``"auto"``.
+        ValueError:
+            If ``trainer.save_checkpoint`` is ``None``.
+
     Example::
 
         >>> from pytorch_lightning import Trainer

pytorch_lightning/callbacks/pruning.py

@@ -135,6 +135,14 @@ class ModelPruning(Callback):
 
             verbose: Verbosity level. 0 to disable, 1 to log overall sparsity, 2 to log per-layer sparsity
 
+        Raises:
+            MisconfigurationException:
+                If ``parameter_names`` is neither ``"weight"`` nor ``"bias"``,
+                if the provided ``pruning_fn`` is not supported,
+                if ``pruning_dim`` is not provided when ``"unstructured"``,
+                if ``pruning_norm`` is not provided when ``"ln_structured"``,
+                if ``pruning_fn`` is neither ``str`` nor :class:`torch.nn.utils.prune.BasePruningMethod`, or
+                if ``amount`` is none of ``int``, ``float`` and ``Callable``.
         """
 
         self._use_global_unstructured = use_global_unstructured
@@ -382,6 +390,11 @@ class ModelPruning(Callback):
         """
         This function is responsible of sanitizing ``parameters_to_prune`` and ``parameter_names``.
         If ``parameters_to_prune is None``, it will be generated with all parameters of the model.
+
+        Raises:
+            MisconfigurationException:
+                If ``parameters_to_prune`` doesn't exist in the model, or
+                if ``parameters_to_prune`` is neither a list of tuple nor ``None``.
         """
         parameters = parameter_names or ModelPruning.PARAMETER_NAMES