remove InternalDebugger

.github/CONTRIBUTING.md

@@ -316,8 +316,6 @@ def test_explain_what_is_being_tested(tmpdir):
     Test description about text reason to be
     """
 
-    # os.environ["PL_DEV_DEBUG"] = '1' # [OPTIONAL] When activated, you can use internal trainer.dev_debugger
-
     class ExtendedModel(BoringModel):
         ...
 

CHANGELOG.md

@@ -351,6 +351,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed deprecated properties `DeepSpeedPlugin.cpu_offload*` in favor of `offload_optimizer`, `offload_parameters` and `pin_memory` ([#9244](https://github.com/PyTorchLightning/pytorch-lightning/pull/9244))
 
 
+- Removed `pytorch_lightning.utilities.debugging.InternalDebugger` ([#9680](https://github.com/PyTorchLightning/pytorch-lightning/pull/9680))
+
+
 ### Fixed
 
 

pyproject.toml

@@ -75,7 +75,6 @@ module = [
     "pytorch_lightning.utilities.argparse",
     "pytorch_lightning.utilities.cli",
     "pytorch_lightning.utilities.cloud_io",
-    "pytorch_lightning.utilities.debugging",
     "pytorch_lightning.utilities.device_dtype_mixin",
     "pytorch_lightning.utilities.device_parser",
     "pytorch_lightning.utilities.distributed",

pytorch_lightning/trainer/data_loading.py

@@ -38,7 +38,6 @@
     FastForwardSampler,
 )
 from pytorch_lightning.utilities.data import has_iterable_dataset, has_len
-from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
 from pytorch_lightning.utilities.model_helpers import is_overridden
@@ -64,7 +63,6 @@ class TrainerDataLoadingMixin(ABC):
     distributed_sampler_kwargs: dict
     accelerator: Accelerator
     accelerator_connector: AcceleratorConnector
-    dev_debugger: InternalDebugger
     call_hook: Callable
 
     def _worker_check(self, dataloader: DataLoader, name: str) -> None:
@@ -304,9 +302,6 @@ def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -
                     self.train_dataloader, SequentialSampler(self.train_dataloader.dataset), mode=RunningStage.TRAINING
                 )
 
-        # debugging
-        self.dev_debugger.track_load_dataloader_call("train_dataloader", dataloaders=[self.train_dataloader])
-
         # automatically add samplers
         self.train_dataloader = apply_to_collection(
             self.train_dataloader, DataLoader, self.auto_add_sampler, shuffle=True, mode=RunningStage.TRAINING
@@ -385,7 +380,6 @@ def _reset_eval_dataloader(
         assert mode.evaluating or mode == RunningStage.PREDICTING
 
         # always get the loaders first so we can count how many there are
-        loader_name = f"{mode.dataloader_prefix}_dataloader"
         dataloaders = self.request_dataloader(mode, model=model)
 
         if not isinstance(dataloaders, list):
@@ -397,8 +391,6 @@ def _reset_eval_dataloader(
             train_dataloader = self.request_dataloader(RunningStage.TRAINING, model=model)
             dataloaders = [deepcopy(train_dataloader) for _ in range(len(dataloaders))]
 
-        self.dev_debugger.track_load_dataloader_call(loader_name, dataloaders=dataloaders)
-
         for loader_i in range(len(dataloaders)):
             loader = dataloaders[loader_i]
 

pytorch_lightning/trainer/trainer.py

@@ -86,7 +86,6 @@
     parse_env_variables,
 )
 from pytorch_lightning.utilities.cloud_io import get_filesystem
-from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.distributed import distributed_available
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
@@ -379,7 +378,6 @@ def __init__(
         gpu_ids, tpu_cores = self._parse_devices(gpus, auto_select_gpus, tpu_cores)
 
         # init connectors
-        self.dev_debugger = InternalDebugger(self)
         self.config_validator = ConfigValidator(self)
         self.data_connector = DataConnector(self, multiple_trainloader_mode)
         self.optimizer_connector = OptimizerConnector(self)

tests/deprecated_api/test_remove_1-6.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Test deprecated functionality which will be removed in v1.6.0."""
+from unittest.mock import Mock, call
+
 import pytest
 
 from pytorch_lightning import Trainer
@@ -89,9 +91,17 @@ def test_v1_6_0_ddp_spawn_sync_batchnorm():
 
 
 def test_v1_6_0_reload_dataloaders_every_epoch(tmpdir):
-
     model = BoringModel()
 
+    tracker = Mock()
+    model.train_dataloader = Mock(wraps=model.train_dataloader)
+    model.val_dataloader = Mock(wraps=model.val_dataloader)
+    model.test_dataloader = Mock(wraps=model.test_dataloader)
+
+    tracker.attach_mock(model.train_dataloader, "train_dataloader")
+    tracker.attach_mock(model.val_dataloader, "val_dataloader")
+    tracker.attach_mock(model.test_dataloader, "test_dataloader")
+
     with pytest.deprecated_call(match="`reload_dataloaders_every_epoch` is deprecated in v1.4 and will be removed"):
         trainer = Trainer(
             default_root_dir=tmpdir,
@@ -103,11 +113,10 @@ def test_v1_6_0_reload_dataloaders_every_epoch(tmpdir):
     trainer.fit(model)
     trainer.test()
 
-    # verify the sequence
-    calls = trainer.dev_debugger.dataloader_sequence_calls
-    expected_sequence = ["val_dataloader"] + ["train_dataloader", "val_dataloader"] * 3 + ["test_dataloader"]
-    for call, expected in zip(calls, expected_sequence):
-        assert call["name"] == expected
+    expected_sequence = (
+        [call.val_dataloader()] + [call.train_dataloader(), call.val_dataloader()] * 3 + [call.test_dataloader()]
+    )
+    assert tracker.mock_calls == expected_sequence
 
 
 def test_v1_6_0_tbptt_reduce_fx(tmpdir):