Remove trainer.fit return value [2/n] (#7237)

* `_fit_impl` refactor and types

* Fix return

* Remove return docstring

* Fixes

* Fixes

* Remove `trainer.fit` return value

* Update CHANGELOG

* flake8

* Undo results change

* Fix test

* Revert changes for a separate PR

* flake8

CHANGELOG.md

@@ -259,6 +259,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed legacy code to log or include metrics in the progress bar by returning them in a dict with the `"log"/"progress_bar"` magic keys. Use `self.log` instead ([#6734](https://github.com/PyTorchLightning/pytorch-lightning/pull/6734))
 
 
+- Removed `trainer.fit()` return value of `1`. It has no return now ([#7237](https://github.com/PyTorchLightning/pytorch-lightning/pull/7237))
+
+
 - Removed `optimizer_idx` argument from `training_step` in manual optimization ([#6093](https://github.com/PyTorchLightning/pytorch-lightning/pull/6093))
 
 

pytorch_lightning/trainer/trainer.py

@@ -415,7 +415,7 @@ def _launch(
         train_dataloader: Any = None,
         val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
         datamodule: Optional[LightningDataModule] = None,
-    ) -> Union[int, _EVALUATE_OUTPUT, _PREDICT_OUTPUT]:
+    ) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
         # set local properties on the model
         self.model_connector.copy_trainer_model_properties(model)
 
@@ -497,9 +497,7 @@ def _launch(
             self.state = TrainerState.FINISHED
         self._running_stage = None
 
-        # return 1 when finished
-        # used for testing or when we need to know that training succeeded
-        return self.accelerator.results or 1
+        return self.accelerator.results
 
     def pre_dispatch(self):
         self.accelerator.pre_dispatch(self)
@@ -836,7 +834,7 @@ def fit(
         train_dataloader: Any = None,
         val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
         datamodule: Optional[LightningDataModule] = None,
-    ) -> Optional[int]:
+    ) -> None:
         r"""
         Runs the full optimization routine.
 
@@ -857,15 +855,11 @@ def fit(
         self.state = TrainerState.FITTING
         self.training = True
 
-        results = self._launch(
-            model, train_dataloader=train_dataloader, val_dataloaders=val_dataloaders, datamodule=datamodule
-        )
+        self._launch(model, train_dataloader=train_dataloader, val_dataloaders=val_dataloaders, datamodule=datamodule)
 
         assert self.state.stopped
         self.training = False
 
-        return results
-
     def validate(
         self,
         model: Optional[LightningModule] = None,

pytorch_lightning/utilities/cli.py

@@ -261,7 +261,7 @@ def before_fit(self) -> None:
 
     def fit(self) -> None:
         """Runs fit of the instantiated trainer class and prepared fit keyword arguments"""
-        self.fit_result = self.trainer.fit(**self.fit_kwargs)
+        self.trainer.fit(**self.fit_kwargs)
 
     def after_fit(self) -> None:
         """Implement to run some code after fit has finished"""

tests/accelerators/test_common.py

@@ -44,8 +44,7 @@ def test_evaluate(tmpdir, trainer_kwargs):
         **trainer_kwargs
     )
 
-    result = trainer.fit(model, datamodule=dm)
-    assert result
+    trainer.fit(model, datamodule=dm)
     assert 'ckpt' in trainer.checkpoint_callback.best_model_path
 
     old_weights = model.layer_0.weight.clone().detach().cpu()

tests/accelerators/test_tpu_backend.py

@@ -94,8 +94,7 @@ def test_weight_tying_warning(tmpdir, capsys=None):
     trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
 
     with pytest.warns(UserWarning, match=r'The model layers do not match after moving to the target device.'):
-        result = trainer.fit(model)
-        assert result
+        trainer.fit(model)
 
 
 # @RunIf(tpu=True)
@@ -106,8 +105,7 @@ def test_weight_tying_warning(tmpdir, capsys=None):
 #     Ensure no warning for parameter mismatch is thrown.
 #     """
 
-#     # TODO (kaushikb11): Add `paramter_validation` specific to
-#     # TPU Accelerators
+#     # TODO (kaushikb11): Add `parameter_validation` specific to TPU Accelerators
 #     class Model(WeightSharingModule):
 
 #         def on_post_move_to_device(self):
@@ -117,8 +115,7 @@ def test_weight_tying_warning(tmpdir, capsys=None):
 #     trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
 
 #     with pytest.warns(UserWarning) as warnings:
-#         result = trainer.fit(model)
-#         assert result
+#         trainer.fit(model)
 
 #     assert not list(filter(lambda x: 'The model layers do not match' in str(x), warnings.list))
 #     assert len(trainer.test(model)) == 1

tests/callbacks/test_callback_hook_outputs.py

@@ -65,8 +65,7 @@ def on_train_epoch_end(self, outputs) -> None:
 
     assert any(isinstance(c, CB) for c in trainer.callbacks)
 
-    results = trainer.fit(model)
-    assert results
+    trainer.fit(model)
 
 
 def test_on_val_epoch_end_outputs(tmpdir):

tests/callbacks/test_prediction_writer.py

@@ -49,21 +49,18 @@ def write_on_epoch_end(self, *args, **kwargs):
 
     cb = CustomPredictionWriter("batch_and_epoch")
     trainer = Trainer(limit_predict_batches=4, callbacks=cb)
-    results = trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
+    trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
     assert cb.write_on_batch_end_called
     assert cb.write_on_epoch_end_called
-    assert results == 1
 
     cb = CustomPredictionWriter("batch")
     trainer = Trainer(limit_predict_batches=4, callbacks=cb)
-    results = trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
+    trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
     assert cb.write_on_batch_end_called
     assert not cb.write_on_epoch_end_called
-    assert results == 1
 
     cb = CustomPredictionWriter("epoch")
     trainer = Trainer(limit_predict_batches=4, callbacks=cb)
-    results = trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
+    trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
     assert not cb.write_on_batch_end_called
     assert cb.write_on_epoch_end_called
-    assert results == 1

tests/checkpointing/test_legacy_checkpoints.py

@@ -76,13 +76,11 @@ def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
 
     model = DummyModel.load_from_checkpoint(path_ckpt)
     trainer = Trainer(default_root_dir=tmpdir, max_epochs=6)
-    result = trainer.fit(model)
-    assert result
+    trainer.fit(model)
 
     # todo
     # model = DummyModel()
     # trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, resume_from_checkpoint=path_ckpt)
-    # result = trainer.fit(model)
-    # assert result
+    # trainer.fit(model)
 
     sys.path = orig_sys_paths

tests/checkpointing/test_model_checkpoint.py

@@ -127,8 +127,7 @@ def configure_optimizers(self):
         max_epochs=max_epochs,
         progress_bar_refresh_rate=0,
     )
-    results = trainer.fit(model)
-    assert results
+    trainer.fit(model)
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
 
     ckpt_files = list(Path(tmpdir).glob('*.ckpt'))
@@ -232,8 +231,7 @@ def configure_optimizers(self):
         progress_bar_refresh_rate=0,
         num_sanity_val_steps=0,
     )
-    results = trainer.fit(model)
-    assert results
+    trainer.fit(model)
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
 
     ckpt_files = list(Path(tmpdir).glob('*.ckpt'))

tests/core/test_datamodules.py

@@ -271,9 +271,8 @@ def test_train_loop_only(tmpdir):
     )
 
     # fit model
-    result = trainer.fit(model, datamodule=dm)
+    trainer.fit(model, datamodule=dm)
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
-    assert result
     assert trainer.callback_metrics['train_loss'] < 1.0
 
 
@@ -294,9 +293,8 @@ def test_train_val_loop_only(tmpdir):
     )
 
     # fit model
-    result = trainer.fit(model, datamodule=dm)
+    trainer.fit(model, datamodule=dm)
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
-    assert result
     assert trainer.callback_metrics['train_loss'] < 1.0
 
 
@@ -353,10 +351,9 @@ def test_full_loop(tmpdir):
     )
 
     # fit model
-    result = trainer.fit(model, dm)
+    trainer.fit(model, dm)
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
     assert dm.trainer is not None
-    assert result
 
     # validate
     result = trainer.validate(datamodule=dm)

tests/core/test_lightning_module.py

@@ -223,9 +223,7 @@ def optimizer_step(
         accumulate_grad_batches=1,
         limit_val_batches=0,
     )
-
-    results = trainer.fit(model)
-    assert results
+    trainer.fit(model)
 
 
 def test_toggle_untoggle_3_optimizers_shared_parameters(tmpdir):

tests/core/test_results.py

@@ -171,14 +171,11 @@ def test_dataloader(self):
     assert not prediction_file.exists()
 
     if do_train:
-        result = trainer.fit(model, dm)
+        trainer.fit(model, dm)
         assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
-        assert result
-        result = trainer.test(datamodule=dm)
-        # TODO: add end-to-end test
-        # assert result[0]['test_loss'] < 0.6
+        trainer.test(datamodule=dm)
     else:
-        result = trainer.test(model, datamodule=dm)
+        trainer.test(model, datamodule=dm)
 
     # check prediction file now exists and is of expected length
     assert prediction_file.exists()

tests/models/test_amp.py

@@ -174,7 +174,7 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
         callbacks=[checkpoint],
         logger=logger,
     )
-    _ = trainer.fit(model)
+    trainer.fit(model)
 
     # correct result and ok accuracy
     assert trainer.state == TrainerState.FINISHED, 'amp + ddp model failed to complete'

tests/models/test_horovod.py

@@ -409,8 +409,7 @@ def configure_optimizers(self):
             limit_train_batches=0.2,
             accelerator='horovod'
         )
-        results = trainer.fit(model)
-        assert results == 1
+        trainer.fit(model)
 
     adjusted_lr1 = [pg['lr'] for pg in trainer.optimizers[0].param_groups][0]
     adjusted_lr2 = [pg['lr'] for pg in trainer.optimizers[1].param_groups][0]

tests/trainer/flags/test_min_max_epochs.py

@@ -30,9 +30,7 @@ def test_min_max_steps_epochs(tmpdir, min_epochs, max_epochs, min_steps, max_ste
         max_steps=max_steps,
         weights_summary=None,
     )
-
-    result = trainer.fit(model)
-    assert result == 1, "Training did not complete"
+    trainer.fit(model)
 
     # check training stopped at max_epochs or max_steps
     if trainer.max_steps and not trainer.max_epochs:

tests/trainer/optimization/test_optimizers.py

@@ -394,7 +394,7 @@ def test_lr_scheduler_strict(tmpdir):
     with pytest.warns(
         RuntimeWarning, match=r'ReduceLROnPlateau conditioned on metric .* which is not available but strict'
     ):
-        assert trainer.fit(model)
+        trainer.fit(model)
 
 
 def test_unknown_configure_optimizers_raises(tmpdir):

tests/trainer/test_dataloaders.py

@@ -983,8 +983,7 @@ def test_fit_multiple_train_loaders(tmpdir, multiple_trainloader_mode, num_train
         default_root_dir=tmpdir,
         multiple_trainloader_mode=multiple_trainloader_mode,
     )
-
-    assert 1 == trainer.fit(model)
+    trainer.fit(model)
     # verify the num_training_batches according to the multiple_trainloader_mode
     assert num_training_batches == trainer.num_training_batches
 

tests/trainer/test_trainer.py

@@ -759,10 +759,9 @@ def validation_epoch_end(self, *args, **kwargs):
     )
 
     trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
+    trainer.fit(model)
 
     # check that limit_val_batches=0 turns off validation
-    assert result == 1, "training failed to complete"
     assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
     assert trainer.current_epoch == 1
     assert not model.validation_step_invoked, "`validation_step` should not run when `limit_val_batches=0`"
@@ -1593,8 +1592,6 @@ def predict(
         assert len(results) == 2
         assert len(results[0]) == num_samples
         assert results[0][0].shape == torch.Size([1, 2])
-    else:
-        assert results == 1
 
 
 def test_trainer_predict_no_return(tmpdir):
@@ -1693,8 +1690,6 @@ def test_predict_return_predictions_cpu(return_predictions, precision, tmpdir):
         assert len(preds) == 1
         assert preds[0].shape == torch.Size([1, 2])
         assert preds[0].dtype == (torch.float64 if precision == 64 else torch.float32)
-    else:
-        assert preds == 1
 
 
 @pytest.mark.parametrize(
@@ -1735,7 +1730,7 @@ def training_epoch_end(self, *args, **kwargs):
         max_epochs=5,
         limit_train_batches=limit_train_batches,
     )
-    result = trainer.fit(model, train_loader)
+    trainer.fit(model, train_loader)
 
     params_string = f"""`limit_train_batches={limit_train_batches}`, `dataset_len={dataset_len}`
                         & `batch_size={batch_size}` as
@@ -1745,7 +1740,6 @@ def training_epoch_end(self, *args, **kwargs):
     else:
         error_string = f"should not run with {params_string}"
 
-    assert result == 1, "training failed to complete"
     assert trainer.state == TrainerState.FINISHED
     assert trainer.global_step == global_step
     assert trainer.num_training_batches == num_training_batches

tests/trainer/test_training_loop.py

@@ -172,9 +172,7 @@ def on_train_epoch_end(self, outputs):
         progress_bar_refresh_rate=0,
         weights_summary=None,
     )
-
-    result = trainer.fit(model)
-    assert result == 1, "Training did not complete"
+    trainer.fit(model)
 
 
 def test_training_starts_with_seed(tmpdir):

tests/utilities/test_cli.py

@@ -293,7 +293,6 @@ def test_lightning_cli_args(tmpdir):
     with mock.patch('sys.argv', ['any.py'] + cli_args):
         cli = LightningCLI(BoringModel, BoringDataModule, trainer_defaults={'callbacks': [LearningRateMonitor()]})
 
-    assert cli.fit_result == 1
     assert cli.config['seed_everything'] == 1234
     config_path = tmpdir / 'lightning_logs' / 'version_0' / 'config.yaml'
     assert os.path.isfile(config_path)
@@ -324,7 +323,6 @@ def test_lightning_cli_config_and_subclass_mode(tmpdir):
             trainer_defaults={'callbacks': LearningRateMonitor()}
         )
 
-    assert cli.fit_result == 1
     config_path = tmpdir / 'lightning_logs' / 'version_0' / 'config.yaml'
     assert os.path.isfile(config_path)
     with open(config_path) as f: