Refactor base profilers 3/5

CHANGELOG.md

@@ -43,6 +43,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - 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 `setup` method to `BaseProfiler` to enable subclasses defining pre-profiling steps ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))
+
+
 - Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](https://github.com/PyTorchLightning/pytorch-lightning/pull/6120))
 
 
@@ -68,6 +74,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - 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 metrics in favor of `torchmetrics` ([#6505](https://github.com/PyTorchLightning/pytorch-lightning/pull/6505),
 
     [#6530](https://github.com/PyTorchLightning/pytorch-lightning/pull/6530),

pytorch_lightning/plugins/training_type/horovod.py

@@ -96,22 +96,21 @@ def start_training(self, trainer):
                 stack.enter_context(optimizer.skip_synchronize())
 
             # set up training routine
-            self._results = trainer.run_train()
+            self._results = trainer.run_stage()
 
         # Make sure all workers have finished training before returning to the user
         hvd.join()
 
     def start_evaluating(self, trainer):
         with ExitStack():
-            self._results = trainer.run_evaluate()
+            self._results = trainer.run_stage()
 
         # Make sure all workers have finished training before returning to the user
         hvd.join()
 
     def start_predicting(self, trainer):
         with ExitStack():
-            # set up training routine
-            self._results = trainer.run_predict()
+            self._results = trainer.run_stage()
 
         # Make sure all workers have finished training before returning to the user
         hvd.join()

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -132,15 +132,15 @@ def rpc_enabled(self) -> bool:
 
     def start_training(self, trainer: 'Trainer') -> None:
         # double dispatch to initiate the training loop
-        self._results = trainer.run_train()
+        self._results = trainer.run_stage()
 
     def start_evaluating(self, trainer: 'Trainer') -> None:
         # double dispatch to initiate the test loop
-        self._results = trainer.run_evaluate()
+        self._results = trainer.run_stage()
 
     def start_predicting(self, trainer: 'Trainer') -> None:
         # double dispatch to initiate the predicting loop
-        self._results = trainer.run_predict()
+        self._results = trainer.run_stage()
 
     def training_step(self, *args, **kwargs):
         return self.lightning_module.training_step(*args, **kwargs)