remove extra kwargs from Trainer init (#1820)

* remove kwargs

* remove useless test

* rename unknown trainer flag

* trainer inheritance and test

* blank line

* test for unknown arg

* changelog

CHANGELOG.md

@@ -69,6 +69,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `hparam` logging with metrics ([#1647](https://github.com/PyTorchLightning/pytorch-lightning/pull/1647))
 
 
+- Fixed an issue with Trainer constructor silently ignoring unkown/misspelled arguments ([#1820](https://github.com/PyTorchLightning/pytorch-lightning/pull/1820))
+
 ## [0.7.5] - 2020-04-27
 
 ### Changed

pytorch_lightning/trainer/callback_hook.py

@@ -6,11 +6,10 @@ from pytorch_lightning.callbacks import Callback
 
 class TrainerCallbackHookMixin(ABC):
 
-    def __init__(self):
-        # this is just a summary on variables used in this abstract class,
-        # the proper values/initialisation should be done in child class
-        self.callbacks: List[Callback] = []
-        self.get_model: Callable = ...
+    # this is just a summary on variables used in this abstract class,
+    # the proper values/initialisation should be done in child class
+    callbacks: List[Callback] = []
+    get_model: Callable = ...
 
     def on_init_start(self):
         """Called when the trainer initialization begins, model has not yet been set."""

pytorch_lightning/trainer/trainer.py

@@ -142,7 +142,6 @@ class Trainer(
             use_amp=None,  # backward compatible, todo: remove in v0.9.0
             show_progress_bar=None,  # backward compatible, todo: remove in v0.9.0
             nb_sanity_val_steps=None,  # backward compatible, todo: remove in v0.8.0
-            **kwargs
     ):
         r"""
 
@@ -305,6 +304,7 @@ class Trainer(
                 Additionally, can be set to either `power` that estimates the batch size through
                 a power search or `binsearch` that estimates the batch size through a binary search.
         """
+        super().__init__()
 
         self.deterministic = deterministic
         torch.backends.cudnn.deterministic = self.deterministic

tests/trainer/test_dataloaders.py

@@ -289,7 +289,7 @@ def test_inf_train_dataloader(tmpdir, check_interval):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
-        train_check_interval=check_interval,
+        val_check_interval=check_interval
     )
     result = trainer.fit(model)
     # verify training completed
@@ -315,25 +315,6 @@ def test_inf_val_dataloader(tmpdir, check_interval):
     assert result == 1
 
 
-@pytest.mark.parametrize('check_interval', [50, 1.0])
-def test_inf_test_dataloader(tmpdir, check_interval):
-    """Test inf test data loader (e.g. IterableDataset)"""
-
-    model = EvalModelTemplate()
-    model.test_dataloader = model.test_dataloader__infinite
-
-    # logger file to get meta
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=1,
-        test_check_interval=check_interval,
-    )
-    result = trainer.fit(model)
-
-    # verify training completed
-    assert result == 1
-
-
 def test_error_on_zero_len_dataloader(tmpdir):
     """ Test that error is raised if a zero-length dataloader is defined """
 

tests/trainer/test_trainer.py

@@ -772,3 +772,41 @@ def test_trainer_config(trainer_kwargs, expected):
     assert trainer.on_gpu is expected["on_gpu"]
     assert trainer.single_gpu is expected["single_gpu"]
     assert trainer.num_processes == expected["num_processes"]
+
+
+def test_trainer_subclassing():
+    model = EvalModelTemplate()
+
+    # First way of pulling out args from signature is to list them
+    class TrainerSubclass(Trainer):
+
+        def __init__(self, custom_arg, *args, custom_kwarg='test', **kwargs):
+            super().__init__(*args, **kwargs)
+            self.custom_arg = custom_arg
+            self.custom_kwarg = custom_kwarg
+
+    trainer = TrainerSubclass(123, custom_kwarg='custom', fast_dev_run=True)
+    result = trainer.fit(model)
+    assert result == 1
+    assert trainer.custom_arg == 123
+    assert trainer.custom_kwarg == 'custom'
+    assert trainer.fast_dev_run
+
+    # Second way is to pop from the dict
+    # It's a special case because Trainer does not have any positional args
+    class TrainerSubclass(Trainer):
+
+        def __init__(self, **kwargs):
+            self.custom_arg = kwargs.pop('custom_arg', 0)
+            self.custom_kwarg = kwargs.pop('custom_kwarg', 'test')
+            super().__init__(**kwargs)
+
+    trainer = TrainerSubclass(custom_kwarg='custom', fast_dev_run=True)
+    result = trainer.fit(model)
+    assert result == 1
+    assert trainer.custom_kwarg == 'custom'
+    assert trainer.fast_dev_run
+
+    # when we pass in an unknown arg, the base class should complain
+    with pytest.raises(TypeError, match=r"__init__\(\) got an unexpected keyword argument 'abcdefg'") as e:
+        TrainerSubclass(abcdefg='unknown_arg')