Do not lose references of trainer in test

tests/tests_lite/plugins/collectives/test_torch_collective.py

@@ -1,7 +1,6 @@
 import contextlib
 import datetime
 import os
-import sys
 from functools import partial
 from unittest import mock
 
@@ -272,7 +271,6 @@ def _test_two_groups(strategy, left_collective, right_collective):
 
 
 @skip_distributed_unavailable
+@pytest.mark.skip(reason="TODO(carmocca): causing hangs in CI")
 def test_two_groups():
-    if sys.platform == "win32" and (sys.version_info.major, sys.version_info.minor) == (3, 10):
-        pytest.skip("Unresolved hang")
     collective_launch(_test_two_groups, [torch.device("cpu")] * 3, num_groups=2)

tests/tests_pytorch/utilities/test_parsing.py

@@ -36,7 +36,7 @@
 unpicklable_function = lambda: None
 
 
-def model_cases():
+def model_and_trainer_cases():
     class TestHparamsNamespace(LightningModule):
         learning_rate = 1
 
@@ -64,44 +64,45 @@ class TestModel4(LightningModule):  # fail case
         batch_size = 1
 
     model4 = TestModel4()
-    trainer = Trainer()
-    model4.trainer = trainer
+    trainer1 = Trainer()
+    model4.trainer = trainer1
     datamodule = LightningDataModule()
     datamodule.batch_size = 8
-    trainer.datamodule = datamodule
+    trainer1.datamodule = datamodule
 
     model5 = LightningModule()
-    model5.trainer = trainer
+    model5.trainer = trainer1
 
     class TestModel6(LightningModule):  # test for datamodule w/ hparams w/o attribute (should use datamodule)
         hparams = TestHparamsDict
 
     model6 = TestModel6()
-    model6.trainer = trainer
+    model6.trainer = trainer1
 
     TestHparamsDict2 = {"batch_size": 2}
 
     class TestModel7(LightningModule):  # test for datamodule w/ hparams w/ attribute (should use datamodule)
         hparams = TestHparamsDict2
 
     model7 = TestModel7()
-    model7.trainer = trainer
+    model7.trainer = trainer1
 
     class TestDataModule8(LightningDataModule):  # test for hparams dict
         hparams = TestHparamsDict2
 
     model8 = TestModel1()
-    trainer = Trainer()
-    model8.trainer = trainer
+    trainer2 = Trainer()
+    model8.trainer = trainer2
     datamodule = TestDataModule8()
-    trainer.datamodule = datamodule
+    trainer2.datamodule = datamodule
 
-    return model1, model2, model3, model4, model5, model6, model7, model8
+    return (model1, model2, model3, model4, model5, model6, model7, model8), (trainer1, trainer2)
 
 
 def test_lightning_hasattr():
     """Test that the lightning_hasattr works in all cases."""
-    model1, model2, model3, model4, model5, model6, model7, model8 = models = model_cases()
+    models, _ = model_and_trainer_cases()
+    model1, model2, model3, model4, model5, model6, model7, model8 = models
     assert lightning_hasattr(model1, "learning_rate"), "lightning_hasattr failed to find namespace variable"
     assert lightning_hasattr(model2, "learning_rate"), "lightning_hasattr failed to find hparams namespace variable"
     assert lightning_hasattr(model3, "learning_rate"), "lightning_hasattr failed to find hparams dict variable"
@@ -121,12 +122,12 @@ def test_lightning_hasattr():
 
 def test_lightning_getattr():
     """Test that the lightning_getattr works in all cases."""
-    models = model_cases()
+    models, _ = model_and_trainer_cases()
+    *__, model5, model6, model7, model8 = models
     for i, m in enumerate(models[:3]):
         value = lightning_getattr(m, "learning_rate")
         assert value == i, "attribute not correctly extracted"
 
-    model5, model6, model7, model8 = models[4:]
     assert lightning_getattr(model5, "batch_size") == 8, "batch_size not correctly extracted"
     assert lightning_getattr(model6, "batch_size") == 8, "batch_size not correctly extracted"
     assert lightning_getattr(model7, "batch_size") == 8, "batch_size not correctly extracted"
@@ -142,12 +143,12 @@ def test_lightning_getattr():
 
 def test_lightning_setattr(tmpdir):
     """Test that the lightning_setattr works in all cases."""
-    models = model_cases()
+    models, _ = model_and_trainer_cases()
+    *__, model5, model6, model7, model8 = models
     for m in models[:3]:
         lightning_setattr(m, "learning_rate", 10)
         assert lightning_getattr(m, "learning_rate") == 10, "attribute not correctly set"
 
-    model5, model6, model7, model8 = models[4:]
     lightning_setattr(model5, "batch_size", 128)
     lightning_setattr(model6, "batch_size", 128)
     lightning_setattr(model7, "batch_size", 128)