drop unused variable in API (#6308)

* drop unused pl model in ckpt

* irelevant

* on_evaluation_batch_start

* evaluation_epoch_end

* attach_datamodule

Author: Borda

pytorch_lightning/callbacks/model_checkpoint.py

@@ -239,7 +239,7 @@ def save_checkpoint(self, trainer, pl_module):
             self._save_top_k_checkpoints(trainer, pl_module, monitor_candidates)
 
         # Mode 2: save the last checkpoint
-        self._save_last_checkpoint(trainer, pl_module, monitor_candidates)
+        self._save_last_checkpoint(trainer, monitor_candidates)
 
     def __validate_init_configuration(self):
         if self.save_top_k is not None and self.save_top_k < -1:
@@ -291,8 +291,7 @@ def _del_model(self, filepath: str):
             self._fs.rm(filepath)
             log.debug(f"Removed checkpoint: {filepath}")
 
-    def _save_model(self, filepath: str, trainer, pl_module):
-        # Todo: required argument `pl_module` is not used
+    def _save_model(self, filepath: str, trainer):
         # in debugging, track when we save checkpoints
         trainer.dev_debugger.track_checkpointing_history(filepath)
 
@@ -481,7 +480,7 @@ def _monitor_candidates(self, trainer):
         monitor_candidates.update(step=trainer.global_step, epoch=trainer.current_epoch)
         return monitor_candidates
 
-    def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
+    def _save_last_checkpoint(self, trainer, ckpt_name_metrics):
         should_save_last = self.monitor is None or self.save_last
         if not should_save_last:
             return
@@ -505,9 +504,9 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
 
         if trainer.training_type_plugin.rpc_enabled:
             # RPCPlugin manages saving all model states
-            trainer.training_type_plugin.rpc_save_model(self._save_model, last_filepath, trainer, pl_module)
+            trainer.training_type_plugin.rpc_save_model(self._save_model, last_filepath, trainer)
         else:
-            self._save_model(last_filepath, trainer, pl_module)
+            self._save_model(last_filepath, trainer)
         if (
             self.last_model_path and self.last_model_path != last_filepath
             and (self.save_top_k != -1 or self.save_last) and trainer.is_global_zero
@@ -574,7 +573,7 @@ def _update_best_and_save(
                 f"Epoch {epoch:d}, global step {step:d}: {self.monitor} reached {current:0.5f}"
                 f' (best {self.best_model_score:0.5f}), saving model to "{filepath}" as top {k}'
             )
-        self._save_model(filepath, trainer, pl_module)
+        self._save_model(filepath, trainer)
 
         if del_filepath is not None and filepath != del_filepath:
             self._del_model(del_filepath)

pytorch_lightning/plugins/training_type/rpc.py

@@ -63,7 +63,7 @@ def init_rpc_connection(self, global_rank: int, world_size: int) -> None:
         rpc._set_rpc_timeout(self.rpc_timeout_sec)
         self._is_rpc_initialized = True
 
-    def rpc_save_model(self, save_model_fn, last_filepath, trainer, pl_module) -> None:
+    def rpc_save_model(self, save_model_fn, last_filepath, trainer) -> None:
         """
         Override to save model to disk.
         This is required as the main process will be required to handle aggregating model states from RPC processes.
@@ -72,7 +72,6 @@ def rpc_save_model(self, save_model_fn, last_filepath, trainer, pl_module) -> No
             save_model_fn: The saving function to save final model.
             last_filepath: The filepath to save the model to.
             trainer: The trainer object.
-            pl_module: The LightningModule.
         """
         raise NotImplementedError
 

pytorch_lightning/plugins/training_type/rpc_sequential.py

@@ -266,17 +266,17 @@ def configure_ddp(self):
             self._model.require_backward_grad_sync = False
 
     @rank_zero_only
-    def rpc_save_model(self, save_model_fn, last_filepath, trainer, pl_module) -> None:
+    def rpc_save_model(self, save_model_fn, last_filepath, trainer) -> None:
         model = self.lightning_module
         if not hasattr(model.sequential_module, "foreach_worker"):
             return
-        current_layers = pl_module.sequential_module
+        current_layers = model.sequential_module
         model.sequential_module.foreach_worker(
             save_layers_on_all_rank_zero_workers, {"gpus_per_model": self.gpus_per_model}, include_self=True
         )
-        pl_module.sequential_module = load_sequential_from_saved_layers(self.gpus_per_model)
-        save_model_fn(last_filepath, trainer, pl_module)
-        pl_module.sequential_module = current_layers
+        model.sequential_module = load_sequential_from_saved_layers(self.gpus_per_model)
+        save_model_fn(last_filepath, trainer)
+        model.sequential_module = current_layers
 
     def worker_optimizer_step(self, model: LightningModule, opt_idx: int, *args, **kwargs) -> None:
         model.sequential_module.foreach_worker(

pytorch_lightning/trainer/connectors/data_connector.py

@@ -81,7 +81,7 @@ def attach_data(self, model, train_dataloader, val_dataloaders, datamodule):
 
         # set up the passed in dataloaders (if needed)
         self.attach_dataloaders(model, train_dataloader, val_dataloaders)
-        self.attach_datamodule(model, datamodule, 'fit')
+        self.attach_datamodule(model, datamodule)
 
     def __enforce_datamodule_dataloader_override(self, train_dataloader, val_dataloaders, datamodule):
         # If you supply a datamodule you can't supply train_dataloader or val_dataloaders
@@ -112,8 +112,7 @@ def attach_dataloaders(
         if predict_dataloaders is not None:
             model.predict_dataloader = _PatchDataLoader(predict_dataloaders)
 
-    def attach_datamodule(self, model, datamodule: Optional[LightningDataModule], stage: str) -> None:
-        # Todo: required argument `stage` is not used
+    def attach_datamodule(self, model, datamodule: Optional[LightningDataModule]) -> None:
 
         # We use datamodule if it's been provided on .fit or .test, otherwise we check model for it
         datamodule = datamodule or getattr(model, 'datamodule', None)

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -101,8 +101,7 @@ def check_logging_in_callbacks(self, hook_fx_name, on_step: bool = None, on_epoc
             current_hook_fx_name=hook_fx_name, on_step=on_step, on_epoch=on_epoch
         )
 
-    def on_evaluation_batch_start(self, testing, batch, dataloader_idx, num_dataloaders):
-        # Todo: required argument `testing` is not used
+    def on_evaluation_batch_start(self, batch, dataloader_idx, num_dataloaders):
         model = self.trainer.lightning_module
         # set dataloader_idx only if multiple ones
         model._current_dataloader_idx = dataloader_idx if num_dataloaders > 1 else None
@@ -260,8 +259,7 @@ def track_metrics_deprecated(self, deprecated_eval_results):
         self._track_callback_metrics(deprecated_eval_results)
         self.__process_eval_epoch_end_results_and_log_legacy(deprecated_eval_results)
 
-    def evaluation_epoch_end(self, testing):
-        # Todo: required argument `testing` is not used
+    def evaluation_epoch_end(self):
         # reset dataloader idx
         model_ref = self.trainer.lightning_module
         model_ref._current_dataloader_idx = None

pytorch_lightning/trainer/connectors/slurm_connector.py

@@ -28,8 +28,6 @@ def register_slurm_signal_handlers(self):
             signal.signal(signal.SIGTERM, self.term_handler)
 
     def sig_handler(self, signum, frame):  # pragma: no-cover
-        # Todo: required argument `signum` is not used
-        # Todo: required argument `frame` is not used
         if self.trainer.is_global_zero:
             # save weights
             log.info('handling SIGUSR1')
@@ -59,7 +57,5 @@ def sig_handler(self, signum, frame):  # pragma: no-cover
             # close experiment to avoid issues
             self.trainer.logger.close()
 
-    def term_handler(self, signum, frame):
-        # Todo: required argument `signum` is not used
-        # Todo: required argument `frame` is not used
+    def term_handler(self, signum, frame):  # pragma: no-cover
         log.info("bypassing sigterm")

pytorch_lightning/trainer/evaluation_loop.py

@@ -181,7 +181,7 @@ def evaluation_step_end(self, *args, **kwargs):
 
     def evaluation_epoch_end(self):
         # unset dataloder_idx in model
-        self.trainer.logger_connector.evaluation_epoch_end(self.trainer.testing)
+        self.trainer.logger_connector.evaluation_epoch_end()
 
         # call the model epoch end
         deprecated_results = self.__run_eval_epoch_end(self.num_dataloaders)
@@ -283,9 +283,7 @@ def _convert_to_numpy(v):
 
     def on_evaluation_batch_start(self, batch, batch_idx, dataloader_idx):
         # set dataloader_idx to model and track batch_size
-        self.trainer.logger_connector.on_evaluation_batch_start(
-            self.trainer.testing, batch, dataloader_idx, self.num_dataloaders
-        )
+        self.trainer.logger_connector.on_evaluation_batch_start(batch, dataloader_idx, self.num_dataloaders)
 
         if self.trainer.testing:
             self.trainer.call_hook('on_test_batch_start', batch, batch_idx, dataloader_idx)

pytorch_lightning/trainer/trainer.py

@@ -880,7 +880,7 @@ def test(
             )
 
         # Attach datamodule to get setup/prepare_data added to model before the call to it below
-        self.data_connector.attach_datamodule(model or self.lightning_module, datamodule, 'test')
+        self.data_connector.attach_datamodule(model or self.lightning_module, datamodule)
 
         if model is not None:
             results = self.__test_given_model(model, test_dataloaders)
@@ -989,7 +989,7 @@ def predict(
 
         if datamodule is not None:
             # Attach datamodule to get setup/prepare_data added to model before the call to it below
-            self.data_connector.attach_datamodule(model, datamodule, 'predict')
+            self.data_connector.attach_datamodule(model, datamodule)
 
         # attach data
         if dataloaders is not None:

tests/plugins/test_rpc_plugin.py

@@ -57,7 +57,7 @@ def __init__(self, **kwargs):
         self.rpc_save_model_count = 0
         self.worker_optimizer_step_count = 0
 
-    def rpc_save_model(self, save_model_fn, last_filepath, trainer, pl_module) -> None:
+    def rpc_save_model(self, save_model_fn, last_filepath, trainer) -> None:
         self.rpc_save_model_count += 1
 
     def barrier(self, name: Optional[str] = None) -> None: