Fail the test when a DeprecationWarning is raised

pytorch_lightning/__init__.py

@@ -12,7 +12,6 @@
 # if root logger has handlers, propagate messages up and let root logger process them
 if not _root_logger.hasHandlers():
     _logger.addHandler(logging.StreamHandler())
-    _logger.propagate = False
 
 _PACKAGE_ROOT = os.path.dirname(__file__)
 _PROJECT_ROOT = os.path.dirname(_PACKAGE_ROOT)

pytorch_lightning/utilities/enums.py

@@ -103,13 +103,6 @@ def supported_types() -> List[str]:
 class DistributedType(LightningEnum, metaclass=_OnAccessEnumMeta):
     """Define type of training strategy.
 
-    >>> # you can match the type with string
-    >>> DistributedType.DDP == 'ddp'
-    True
-    >>> # which is case invariant
-    >>> DistributedType.DDP2 in ('ddp2', )
-    True
-
     Deprecated since v1.6.0 and will be removed in v1.8.0.
 
     Use `_StrategyType` instead.

setup.cfg

@@ -23,10 +23,17 @@ python_files =
     test_*.py
 # doctest_plus = disabled
 addopts =
-    --strict
+    --strict-markers
     --doctest-modules
     --color=yes
     --disable-pytest-warnings
+filterwarnings =
+    # error out on our deprecation warnings - ensures the code and tests are kept up-to-date
+    error::pytorch_lightning.utilities.warnings.LightningDeprecationWarning
+    # warnings from deprecated modules on import
+    # TODO: remove in 1.7
+    ignore::pytorch_lightning.utilities.warnings.LightningDeprecationWarning:pytorch_lightning.core.decorators
+    ignore::pytorch_lightning.utilities.warnings.LightningDeprecationWarning:pytorch_lightning.core.memory
 
 junit_duration_report = call
 

tests/accelerators/test_cpu.py

@@ -35,7 +35,7 @@ def setup_optimizers_in_pre_dispatch(self) -> bool:
             return delay_dispatch
 
     model = TestModel()
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, plugins=CustomPlugin(device=torch.device("cpu")))
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, strategy=CustomPlugin(device=torch.device("cpu")))
     trainer.fit(model)
 
 

tests/callbacks/test_gpu_stats_monitor.py

@@ -31,7 +31,8 @@
 def test_gpu_stats_monitor(tmpdir):
     """Test GPU stats are logged using a logger."""
     model = BoringModel()
-    gpu_stats = GPUStatsMonitor(intra_step_time=True)
+    with pytest.deprecated_call(match="GPUStatsMonitor` callback was deprecated in v1.5"):
+        gpu_stats = GPUStatsMonitor(intra_step_time=True)
     logger = CSVLogger(tmpdir)
     log_every_n_steps = 2
 
@@ -65,12 +66,13 @@ def test_gpu_stats_monitor(tmpdir):
 def test_gpu_stats_monitor_no_queries(tmpdir):
     """Test GPU logger doesn't fail if no "nvidia-smi" queries are to be performed."""
     model = BoringModel()
-    gpu_stats = GPUStatsMonitor(
-        memory_utilization=False,
-        gpu_utilization=False,
-        intra_step_time=True,
-        inter_step_time=True,
-    )
+    with pytest.deprecated_call(match="GPUStatsMonitor` callback was deprecated in v1.5"):
+        gpu_stats = GPUStatsMonitor(
+            memory_utilization=False,
+            gpu_utilization=False,
+            intra_step_time=True,
+            inter_step_time=True,
+        )
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
@@ -101,7 +103,8 @@ def test_gpu_stats_monitor_cpu_machine(tmpdir):
 def test_gpu_stats_monitor_no_logger(tmpdir):
     """Test GPUStatsMonitor with no logger in Trainer."""
     model = BoringModel()
-    gpu_stats = GPUStatsMonitor()
+    with pytest.deprecated_call(match="GPUStatsMonitor` callback was deprecated in v1.5"):
+        gpu_stats = GPUStatsMonitor()
 
     trainer = Trainer(default_root_dir=tmpdir, callbacks=[gpu_stats], max_epochs=1, gpus=1, logger=False)
 
@@ -113,7 +116,8 @@ def test_gpu_stats_monitor_no_logger(tmpdir):
 def test_gpu_stats_monitor_no_gpu_warning(tmpdir):
     """Test GPUStatsMonitor raises a warning when not training on GPU device."""
     model = BoringModel()
-    gpu_stats = GPUStatsMonitor()
+    with pytest.deprecated_call(match="GPUStatsMonitor` callback was deprecated in v1.5"):
+        gpu_stats = GPUStatsMonitor()
 
     trainer = Trainer(default_root_dir=tmpdir, callbacks=[gpu_stats], max_steps=1, gpus=None)
 

tests/callbacks/test_lambda_function.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 from functools import partial
 
+import pytest
+
 from pytorch_lightning import seed_everything, Trainer
 from pytorch_lightning.callbacks import Callback, LambdaCallback
 from tests.helpers.boring_model import BoringModel
@@ -46,7 +48,8 @@ def call(hook, *_, **__):
         limit_val_batches=1,
         callbacks=[LambdaCallback(**hooks_args)],
     )
-    trainer.fit(model)
+    with pytest.deprecated_call(match="on_keyboard_interrupt` callback hook was deprecated in v1.5"):
+        trainer.fit(model)
 
     ckpt_path = trainer.checkpoint_callback.best_model_path
 
@@ -60,8 +63,11 @@ def call(hook, *_, **__):
         limit_predict_batches=1,
         callbacks=[LambdaCallback(**hooks_args)],
     )
-    trainer.fit(model, ckpt_path=ckpt_path)
-    trainer.test(model)
-    trainer.predict(model)
+    with pytest.deprecated_call(match="on_keyboard_interrupt` callback hook was deprecated in v1.5"):
+        trainer.fit(model, ckpt_path=ckpt_path)
+    with pytest.deprecated_call(match="on_keyboard_interrupt` callback hook was deprecated in v1.5"):
+        trainer.test(model)
+    with pytest.deprecated_call(match="on_keyboard_interrupt` callback hook was deprecated in v1.5"):
+        trainer.predict(model)
 
     assert checker == hooks

tests/callbacks/test_stochastic_weight_avg.py

@@ -171,7 +171,7 @@ def test_swa_callback_scheduler_step(tmpdir, interval: str):
 
 def test_swa_warns(tmpdir, caplog):
     model = SwaTestModel(interval="step")
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, stochastic_weight_avg=True)
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, callbacks=StochasticWeightAveraging())
     with caplog.at_level(level=logging.INFO), pytest.warns(UserWarning, match="SWA is currently only supported"):
         trainer.fit(model)
     assert "Swapping scheduler `StepLR` for `SWALR`" in caplog.text
@@ -199,14 +199,19 @@ def configure_optimizers(self):
             return optimizer
 
     model = TestModel()
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        callbacks=StochasticWeightAveraging(swa_lrs=1e-3) if use_callbacks else None,
-        stochastic_weight_avg=stochastic_weight_avg,
-        limit_train_batches=4,
-        limit_val_batches=4,
-        max_epochs=2,
-    )
+    kwargs = {
+        "default_root_dir": tmpdir,
+        "callbacks": StochasticWeightAveraging(swa_lrs=1e-3) if use_callbacks else None,
+        "stochastic_weight_avg": stochastic_weight_avg,
+        "limit_train_batches": 4,
+        "limit_val_batches": 4,
+        "max_epochs": 2,
+    }
+    if stochastic_weight_avg:
+        with pytest.deprecated_call(match=r"stochastic_weight_avg=True\)` is deprecated in v1.5"):
+            trainer = Trainer(**kwargs)
+    else:
+        trainer = Trainer(**kwargs)
     trainer.fit(model)
     if use_callbacks or stochastic_weight_avg:
         assert sum(1 for cb in trainer.callbacks if isinstance(cb, StochasticWeightAveraging)) == 1

tests/core/test_datamodules.py

@@ -442,20 +442,24 @@ def test_hyperparameters_saving():
 
 
 def test_define_as_dataclass():
+    class BoringDataModule(LightningDataModule):
+        def __init__(self, foo=None):
+            super().__init__()
+
     # makes sure that no functionality is broken and the user can still manually make
     # super().__init__ call with parameters
     # also tests all the dataclass features that can be enabled without breaking anything
     @dataclass(init=True, repr=True, eq=True, order=True, unsafe_hash=True, frozen=False)
-    class BoringDataModule1(LightningDataModule):
+    class BoringDataModule1(BoringDataModule):
         batch_size: int
-        dims: int = 2
+        foo: int = 2
 
         def __post_init__(self):
-            super().__init__(dims=self.dims)
+            super().__init__(foo=self.foo)
 
     # asserts for the different dunder methods added by dataclass, when __init__ is implemented, i.e.
     # __repr__, __eq__, __lt__, __le__, etc.
-    assert BoringDataModule1(batch_size=64).dims == 2
+    assert BoringDataModule1(batch_size=64).foo == 2
     assert BoringDataModule1(batch_size=32)
     assert hasattr(BoringDataModule1, "__repr__")
     assert BoringDataModule1(batch_size=32) == BoringDataModule1(batch_size=32)
@@ -477,7 +481,8 @@ def test_inconsistent_prepare_data_per_node(tmpdir):
     with pytest.raises(MisconfigurationException, match="Inconsistent settings found for `prepare_data_per_node`."):
         model = BoringModel()
         dm = BoringDataModule()
-        trainer = Trainer(prepare_data_per_node=False)
+        with pytest.deprecated_call(match="prepare_data_per_node` with the trainer flag is deprecated"):
+            trainer = Trainer(prepare_data_per_node=False)
         trainer.model = model
         trainer.datamodule = dm
         trainer._data_connector.prepare_data()

tests/deprecated_api/test_remove_1-7.py

@@ -410,13 +410,6 @@ def test_v1_7_0_deprecated_max_steps_none(tmpdir):
 
 
 def test_v1_7_0_resume_from_checkpoint_trainer_constructor(tmpdir):
-    with pytest.deprecated_call(match=r"Setting `Trainer\(resume_from_checkpoint=\)` is deprecated in v1.5"):
-        trainer = Trainer(resume_from_checkpoint="a")
-    with pytest.deprecated_call(
-        match=r"trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7."
-    ):
-        _ = trainer.resume_from_checkpoint
-
     # test resume_from_checkpoint still works until v1.7 deprecation
     model = BoringModel()
     callback = OldStatefulCallback(state=111)
@@ -425,14 +418,22 @@ def test_v1_7_0_resume_from_checkpoint_trainer_constructor(tmpdir):
     ckpt_path = trainer.checkpoint_callback.best_model_path
 
     callback = OldStatefulCallback(state=222)
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, callbacks=[callback], resume_from_checkpoint=ckpt_path)
+    with pytest.deprecated_call(match=r"Setting `Trainer\(resume_from_checkpoint=\)` is deprecated in v1.5"):
+        trainer = Trainer(default_root_dir=tmpdir, max_steps=2, callbacks=[callback], resume_from_checkpoint=ckpt_path)
+    with pytest.deprecated_call(
+        match=r"trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7."
+    ):
+        _ = trainer.resume_from_checkpoint
     assert trainer.checkpoint_connector.resume_checkpoint_path is None
     assert trainer.checkpoint_connector.resume_from_checkpoint_fit_path == ckpt_path
     trainer.validate(model=model, ckpt_path=ckpt_path)
     assert callback.state == 222
     assert trainer.checkpoint_connector.resume_checkpoint_path is None
     assert trainer.checkpoint_connector.resume_from_checkpoint_fit_path == ckpt_path
-    trainer.fit(model)
+    with pytest.deprecated_call(
+        match=r"trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7."
+    ):
+        trainer.fit(model)
     assert callback.state == 111
     assert trainer.checkpoint_connector.resume_checkpoint_path is None
     assert trainer.checkpoint_connector.resume_from_checkpoint_fit_path is None
@@ -445,7 +446,8 @@ def test_v1_7_0_resume_from_checkpoint_trainer_constructor(tmpdir):
 
     # test fit(ckpt_path=) precedence over Trainer(resume_from_checkpoint=) path
     model = BoringModel()
-    trainer = Trainer(resume_from_checkpoint="trainer_arg_path")
+    with pytest.deprecated_call(match=r"Setting `Trainer\(resume_from_checkpoint=\)` is deprecated in v1.5"):
+        trainer = Trainer(resume_from_checkpoint="trainer_arg_path")
     with pytest.raises(FileNotFoundError, match="Checkpoint at fit_arg_ckpt_path not found. Aborting training."):
         trainer.fit(model, ckpt_path="fit_arg_ckpt_path")
 

tests/loggers/test_all.py

@@ -76,7 +76,9 @@ def test_loggers_fit_test_all(tmpdir, monkeypatch):
     with mock.patch("pytorch_lightning.loggers.neptune.neptune", new_callable=create_neptune_mock):
         _test_loggers_fit_test(tmpdir, NeptuneLogger)
 
-    with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"):
+    with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"), pytest.deprecated_call(
+        match="TestTubeLogger is deprecated since v1.5"
+    ):
         _test_loggers_fit_test(tmpdir, TestTubeLogger)
 
     with mock.patch("pytorch_lightning.loggers.wandb.wandb") as wandb:
@@ -176,7 +178,9 @@ def test_loggers_save_dir_and_weights_save_path_all(tmpdir, monkeypatch):
     ):
         _test_loggers_save_dir_and_weights_save_path(tmpdir, MLFlowLogger)
 
-    with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"):
+    with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"), pytest.deprecated_call(
+        match="TestTubeLogger is deprecated since v1.5"
+    ):
         _test_loggers_save_dir_and_weights_save_path(tmpdir, TestTubeLogger)
 
     with mock.patch("pytorch_lightning.loggers.wandb.wandb"):
@@ -247,7 +251,11 @@ def test_loggers_pickle_all(tmpdir, monkeypatch, logger_class):
     """
     _patch_comet_atexit(monkeypatch)
     try:
-        _test_loggers_pickle(tmpdir, monkeypatch, logger_class)
+        if logger_class is TestTubeLogger:
+            with pytest.deprecated_call(match="TestTubeLogger is deprecated since v1.5"):
+                _test_loggers_pickle(tmpdir, monkeypatch, logger_class)
+        else:
+            _test_loggers_pickle(tmpdir, monkeypatch, logger_class)
     except (ImportError, ModuleNotFoundError):
         pytest.xfail(f"pickle test requires {logger_class.__class__} dependencies to be installed.")
 
@@ -327,7 +335,11 @@ def test_logger_created_on_rank_zero_only(tmpdir, monkeypatch, logger_class):
     """Test that loggers get replaced by dummy loggers on global rank > 0."""
     _patch_comet_atexit(monkeypatch)
     try:
-        _test_logger_created_on_rank_zero_only(tmpdir, logger_class)
+        if logger_class is TestTubeLogger:
+            with pytest.deprecated_call(match="TestTubeLogger is deprecated since v1.5"):
+                _test_logger_created_on_rank_zero_only(tmpdir, logger_class)
+        else:
+            _test_logger_created_on_rank_zero_only(tmpdir, logger_class)
     except (ImportError, ModuleNotFoundError):
         pytest.xfail(f"multi-process test requires {logger_class.__class__} dependencies to be installed.")
 
@@ -385,7 +397,9 @@ def test_logger_with_prefix_all(tmpdir, monkeypatch):
         logger.experiment.add_scalar.assert_called_once_with("tmp-test", 1.0, 0)
 
     # TestTube
-    with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"):
+    with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"), pytest.deprecated_call(
+        match="TestTubeLogger is deprecated since v1.5"
+    ):
         logger = _instantiate_logger(TestTubeLogger, save_dir=tmpdir, prefix=prefix)
         logger.log_metrics({"test": 1.0}, step=0)
         logger.experiment.log.assert_called_once_with({"tmp-test": 1.0}, global_step=0)

tests/models/test_hooks.py

@@ -487,7 +487,8 @@ def training_step(self, batch, batch_idx):
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
     ]
-    trainer.fit(model)
+    with pytest.deprecated_call(match="on_train_dataloader` is deprecated in v1.5"):
+        trainer.fit(model)
     saved_ckpt = {
         "callbacks": ANY,
         "epoch": 1,
@@ -589,7 +590,8 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
         enable_model_summary=False,
         callbacks=[HookedCallback([])],
     )
-    trainer.fit(model)
+    with pytest.deprecated_call(match="on_keyboard_interrupt` callback hook was deprecated in v1.5"):
+        trainer.fit(model)
     best_model_path = trainer.checkpoint_callback.best_model_path
 
     # resume from checkpoint with HookedModel
@@ -611,7 +613,8 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
     ]
-    trainer.fit(model, ckpt_path=best_model_path)
+    with pytest.deprecated_call(match="on_train_dataloader` is deprecated in v1.5"):
+        trainer.fit(model, ckpt_path=best_model_path)
     saved_ckpt = {
         "callbacks": ANY,
         "epoch": 2,  # TODO: wrong saved epoch
@@ -706,7 +709,8 @@ def test_trainer_model_hook_system_eval(tmpdir, batches, verb, noun, dataloader,
         dict(name="Callback.on_init_end", args=(trainer,)),
     ]
     fn = getattr(trainer, verb)
-    fn(model, verbose=False)
+    with pytest.deprecated_call(match=f"on_{dataloader}_dataloader` is deprecated in v1.5"):
+        fn(model, verbose=False)
     hooks = [
         dict(name="train", args=(False,)),
         dict(name=f"on_{noun}_model_eval"),
@@ -750,7 +754,8 @@ def test_trainer_model_hook_system_predict(tmpdir):
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),
     ]
-    trainer.predict(model)
+    with pytest.deprecated_call(match="on_predict_dataloader` is deprecated in v1.5"):
+        trainer.predict(model)
     expected = [
         dict(name="Callback.on_init_start", args=(trainer,)),
         dict(name="Callback.on_init_end", args=(trainer,)),

tests/plugins/test_amp_plugins.py

@@ -266,7 +266,7 @@ def test_precision_selection_raises(monkeypatch):
     with mock.patch("torch.cuda.device_count", return_value=1), pytest.raises(
         MisconfigurationException, match="Sharded plugins are not supported with apex"
     ):
-        Trainer(amp_backend="apex", precision=16, gpus=1, accelerator="ddp_fully_sharded")
+        Trainer(amp_backend="apex", precision=16, gpus=1, strategy="ddp_fully_sharded")
 
     import pytorch_lightning.plugins.precision.apex_amp as apex