Move some tests to correct subfolder/file (#1312)

* move some tests to trainer file

* fix imports

tests/models/test_cpu.py

@@ -1,4 +1,3 @@
-import math
 import warnings
 
 import pytest
@@ -14,7 +13,6 @@ from tests.base import (
     LightTrainDataloader,
     LightningTestModel,
     LightTestMixin,
-    LightValidationMixin
 )
 
 
@@ -157,55 +155,6 @@ def test_running_test_without_val(tmpdir):
     tutils.assert_ok_model_acc(trainer)
 
 
-def test_disabled_validation():
-    """Verify that `val_percent_check=0` disables the validation loop unless `fast_dev_run=True`."""
-    tutils.reset_seed()
-
-    class CurrentModel(LightTrainDataloader, LightValidationMixin, TestModelBase):
-
-        validation_step_invoked = False
-        validation_end_invoked = False
-
-        def validation_step(self, *args, **kwargs):
-            self.validation_step_invoked = True
-            return super().validation_step(*args, **kwargs)
-
-        def validation_end(self, *args, **kwargs):
-            self.validation_end_invoked = True
-            return super().validation_end(*args, **kwargs)
-
-    hparams = tutils.get_default_hparams()
-    model = CurrentModel(hparams)
-
-    trainer_options = dict(
-        show_progress_bar=False,
-        max_epochs=2,
-        train_percent_check=0.4,
-        val_percent_check=0.0,
-        fast_dev_run=False,
-    )
-
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-
-    # check that val_percent_check=0 turns off validation
-    assert result == 1, 'training failed to complete'
-    assert trainer.current_epoch == 1
-    assert not model.validation_step_invoked, '`validation_step` should not run when `val_percent_check=0`'
-    assert not model.validation_end_invoked, '`validation_end` should not run when `val_percent_check=0`'
-
-    # check that val_percent_check has no influence when fast_dev_run is turned on
-    model = CurrentModel(hparams)
-    trainer_options.update(fast_dev_run=True)
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-
-    assert result == 1, 'training failed to complete'
-    assert trainer.current_epoch == 0
-    assert model.validation_step_invoked, 'did not run `validation_step` with `fast_dev_run=True`'
-    assert model.validation_end_invoked, 'did not run `validation_end` with `fast_dev_run=True`'
-
-
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
 def test_single_gpu_batch_parse():
     tutils.reset_seed()
@@ -405,63 +354,5 @@ def test_single_gpu_model(tmpdir):
     tutils.run_model_test(trainer_options, model)
 
 
-def test_nan_loss_detection(tmpdir):
-    test_step = 8
-
-    class InfLossModel(LightTrainDataloader, TestModelBase):
-
-        def training_step(self, batch, batch_idx):
-            output = super().training_step(batch, batch_idx)
-            if batch_idx == test_step:
-                if isinstance(output, dict):
-                    output['loss'] *= torch.tensor(math.inf)  # make loss infinite
-                else:
-                    output /= 0
-            return output
-
-    hparams = tutils.get_default_hparams()
-    model = InfLossModel(hparams)
-
-    # fit model
-    trainer = Trainer(
-        default_save_path=tmpdir,
-        max_steps=(test_step + 1),
-    )
-
-    with pytest.raises(ValueError, match=r'.*The loss returned in `training_step` is nan or inf.*'):
-        trainer.fit(model)
-        assert trainer.global_step == test_step
-
-    for param in model.parameters():
-        assert torch.isfinite(param).all()
-
-
-def test_nan_params_detection(tmpdir):
-    test_step = 8
-
-    class NanParamModel(LightTrainDataloader, TestModelBase):
-
-        def on_after_backward(self):
-            if self.global_step == test_step:
-                # simulate parameter that became nan
-                torch.nn.init.constant_(self.c_d1.bias, math.nan)
-
-    hparams = tutils.get_default_hparams()
-
-    model = NanParamModel(hparams)
-    trainer = Trainer(
-        default_save_path=tmpdir,
-        max_steps=(test_step + 1),
-    )
-
-    with pytest.raises(ValueError, match=r'.*Detected nan and/or inf values in `c_d1.bias`.*'):
-        trainer.fit(model)
-        assert trainer.global_step == test_step
-
-    # after aborting the training loop, model still has nan-valued params
-    params = torch.cat([param.view(-1) for param in model.parameters()])
-    assert not torch.isfinite(params).all()
-
-
 # if __name__ == '__main__':
 #     pytest.main([__file__])

tests/trainer/test_trainer.py

@@ -24,6 +24,7 @@ from tests.base import (
     LightValidationMultipleDataloadersMixin,
     LightTrainDataloader,
     LightTestDataloader,
+    LightValidationMixin,
 )
 
 
@@ -518,3 +519,110 @@ def test_testpass_overrides(tmpdir):
 
     model = LightningTestModel(hparams)
     Trainer().test(model)
+
+
+def test_disabled_validation():
+    """Verify that `val_percent_check=0` disables the validation loop unless `fast_dev_run=True`."""
+    tutils.reset_seed()
+
+    class CurrentModel(LightTrainDataloader, LightValidationMixin, TestModelBase):
+
+        validation_step_invoked = False
+        validation_end_invoked = False
+
+        def validation_step(self, *args, **kwargs):
+            self.validation_step_invoked = True
+            return super().validation_step(*args, **kwargs)
+
+        def validation_end(self, *args, **kwargs):
+            self.validation_end_invoked = True
+            return super().validation_end(*args, **kwargs)
+
+    hparams = tutils.get_default_hparams()
+    model = CurrentModel(hparams)
+
+    trainer_options = dict(
+        show_progress_bar=False,
+        max_epochs=2,
+        train_percent_check=0.4,
+        val_percent_check=0.0,
+        fast_dev_run=False,
+    )
+
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    # check that val_percent_check=0 turns off validation
+    assert result == 1, 'training failed to complete'
+    assert trainer.current_epoch == 1
+    assert not model.validation_step_invoked, '`validation_step` should not run when `val_percent_check=0`'
+    assert not model.validation_end_invoked, '`validation_end` should not run when `val_percent_check=0`'
+
+    # check that val_percent_check has no influence when fast_dev_run is turned on
+    model = CurrentModel(hparams)
+    trainer_options.update(fast_dev_run=True)
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    assert result == 1, 'training failed to complete'
+    assert trainer.current_epoch == 0
+    assert model.validation_step_invoked, 'did not run `validation_step` with `fast_dev_run=True`'
+    assert model.validation_end_invoked, 'did not run `validation_end` with `fast_dev_run=True`'
+
+
+def test_nan_loss_detection(tmpdir):
+    test_step = 8
+
+    class InfLossModel(LightTrainDataloader, TestModelBase):
+
+        def training_step(self, batch, batch_idx):
+            output = super().training_step(batch, batch_idx)
+            if batch_idx == test_step:
+                if isinstance(output, dict):
+                    output['loss'] *= torch.tensor(math.inf)  # make loss infinite
+                else:
+                    output /= 0
+            return output
+
+    hparams = tutils.get_default_hparams()
+    model = InfLossModel(hparams)
+
+    # fit model
+    trainer = Trainer(
+        default_save_path=tmpdir,
+        max_steps=(test_step + 1),
+    )
+
+    with pytest.raises(ValueError, match=r'.*The loss returned in `training_step` is nan or inf.*'):
+        trainer.fit(model)
+        assert trainer.global_step == test_step
+
+    for param in model.parameters():
+        assert torch.isfinite(param).all()
+
+
+def test_nan_params_detection(tmpdir):
+    test_step = 8
+
+    class NanParamModel(LightTrainDataloader, TestModelBase):
+
+        def on_after_backward(self):
+            if self.global_step == test_step:
+                # simulate parameter that became nan
+                torch.nn.init.constant_(self.c_d1.bias, math.nan)
+
+    hparams = tutils.get_default_hparams()
+
+    model = NanParamModel(hparams)
+    trainer = Trainer(
+        default_save_path=tmpdir,
+        max_steps=(test_step + 1),
+    )
+
+    with pytest.raises(ValueError, match=r'.*Detected nan and/or inf values in `c_d1.bias`.*'):
+        trainer.fit(model)
+        assert trainer.global_step == test_step
+
+    # after aborting the training loop, model still has nan-valued params
+    params = torch.cat([param.view(-1) for param in model.parameters()])
+    assert not torch.isfinite(params).all()