Add warning_cache.deprecation and set warning stacklevel [1/2]

pytorch_lightning/callbacks/model_checkpoint.py

@@ -650,10 +650,10 @@ def _add_backward_monitor_support(self, trainer: 'pl.Trainer') -> None:
             self.save_top_k = 1
 
         if deprecation_warning:
-            warning_cache.warn(
+            warning_cache.deprecation(
                 "Relying on `self.log('val_loss', ...)` to set the ModelCheckpoint monitor is deprecated in v1.2"
                 " and will be removed in v1.4. Please, create your own `mc = ModelCheckpoint(monitor='your_monitor')`"
-                " and use it as `Trainer(callbacks=[mc])`.", DeprecationWarning
+                " and use it as `Trainer(callbacks=[mc])`.",
             )
 
     def _validate_monitor_key(self, trainer: 'pl.Trainer') -> None:

pytorch_lightning/core/lightning.py

@@ -168,9 +168,10 @@ def example_input_array(self, example: Any) -> None:
 
     @property
     def datamodule(self) -> Any:
-        rank_zero_deprecation(
+        warning_cache.deprecation(
             "The `LightningModule.datamodule` property is deprecated in v1.3 and will be removed in v1.5."
-            " Access the datamodule through using `self.trainer.datamodule` instead."
+            " Access the datamodule through using `self.trainer.datamodule` instead.",
+            stacklevel=5,
         )
         return self._datamodule
 
@@ -223,10 +224,10 @@ def _apply_batch_transfer_handler(
         if is_param_in_hook_signature(self.transfer_batch_to_device, 'dataloader_idx'):
             batch = self.transfer_batch_to_device(batch, device, dataloader_idx)
         else:
-            warning_cache.warn(
+            warning_cache.deprecation(
                 "`transfer_batch_to_device` hook signature has changed in v1.4."
                 " `dataloader_idx` parameter has been added to it. Support for"
-                " the old signature will be removed in v1.6", DeprecationWarning
+                " the old signature will be removed in v1.6"
             )
             batch = self.transfer_batch_to_device(batch, device)
 

pytorch_lightning/loggers/wandb.py

@@ -137,9 +137,9 @@ def __init__(
             )
 
         if sync_step is not None:
-            warning_cache.warn(
+            warning_cache.deprecation(
                 "`WandbLogger(sync_step=(True|False))` is deprecated in v1.2.1 and will be removed in v1.5."
-                " Metrics are now logged separately and automatically synchronized.", DeprecationWarning
+                " Metrics are now logged separately and automatically synchronized."
             )
 
         super().__init__()

pytorch_lightning/loops/training_batch_loop.py

@@ -488,10 +488,10 @@ def build_train_args(self, batch: Any, batch_idx: int, opt_idx: int, hiddens: Te
         if len(self.trainer.optimizers) > 1:
             if self.trainer.has_arg("training_step", "optimizer_idx"):
                 if not self.trainer.lightning_module.automatic_optimization:
-                    self.warning_cache.warn(
+                    self.warning_cache.deprecation(
                         "`training_step` hook signature has changed in v1.3."
                         " `optimizer_idx` argument has been removed in case of manual optimization. Support for"
-                        " the old signature will be removed in v1.5", DeprecationWarning
+                        " the old signature will be removed in v1.5",
                     )
                 args.append(opt_idx)
             elif not self.trainer.has_arg(
@@ -682,10 +682,10 @@ def _build_kwargs(self, batch: Any, batch_idx: int, opt_idx: int, hiddens: Optio
             has_opt_idx_in_train_step = is_param_in_hook_signature(training_step_fx, "optimizer_idx")
             if has_opt_idx_in_train_step:
                 if not lightning_module.automatic_optimization:
-                    self.warning_cache.warn(
+                    self.warning_cache.deprecation(
                         "`training_step` hook signature has changed in v1.3."
                         " `optimizer_idx` argument has been removed in case of manual optimization. Support for"
-                        " the old signature will be removed in v1.5", DeprecationWarning
+                        " the old signature will be removed in v1.5",
                     )
                 step_kwargs['optimizer_idx'] = opt_idx
             elif not has_opt_idx_in_train_step and lightning_module.automatic_optimization:

pytorch_lightning/loops/training_epoch_loop.py

@@ -231,10 +231,10 @@ def _on_train_epoch_end_hook(self, processed_epoch_output: List[List[STEP_OUTPUT
             if is_overridden(hook_name, model_ref):
                 hook_fx = getattr(model_ref, hook_name)
                 if is_param_in_hook_signature(hook_fx, "outputs"):
-                    self.warning_cache.warn(
+                    self.warning_cache.deprecation(
                         "The signature of `ModelHooks.on_train_epoch_end` has changed in v1.3."
                         " `outputs` parameter has been deprecated."
-                        " Support for the old signature will be removed in v1.5", DeprecationWarning
+                        " Support for the old signature will be removed in v1.5",
                     )
                     model_ref.on_train_epoch_end(processed_epoch_output)
                 else:

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -15,7 +15,6 @@
 import json
 import logging
 import os
-import warnings
 from collections import OrderedDict
 from pathlib import Path
 from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
@@ -30,7 +29,7 @@
 from pytorch_lightning.trainer.optimizers import _get_default_scheduler_config
 from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.utilities.apply_func import apply_to_collection
-from pytorch_lightning.utilities.distributed import rank_zero_info, rank_zero_only
+from pytorch_lightning.utilities.distributed import _warn, rank_zero_info, rank_zero_only
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _DEEPSPEED_AVAILABLE
 
@@ -260,10 +259,11 @@ def __init__(
             )
 
         if cpu_offload or cpu_offload_params or cpu_offload_use_pin_memory:
-            warnings.warn(
+            _warn(
                 "The usage of `cpu_offload`, `cpu_offload_params`, and `cpu_offload_use_pin_memory` "
                 "is deprecated since v1.4 and will be removed in v1.5."
-                " From now on use `offload_optimizer`, `offload_parameters` and `pin_memory`.", DeprecationWarning
+                " From now on use `offload_optimizer`, `offload_parameters` and `pin_memory`.",
+                category=DeprecationWarning
             )
             offload_optimizer = cpu_offload
             offload_parameters = cpu_offload_params

pytorch_lightning/profiler/base.py

@@ -19,7 +19,7 @@
 from pathlib import Path
 from typing import Any, Callable, Dict, Optional, TextIO, Union
 
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_deprecation
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 
 log = logging.getLogger(__name__)
@@ -63,10 +63,9 @@ def __init__(
         self.dirpath = dirpath
         self.filename = filename
         if output_filename is not None:
-            rank_zero_warn(
+            rank_zero_deprecation(
                 "`Profiler` signature has changed in v1.3. The `output_filename` parameter has been removed in"
                 " favor of `dirpath` and `filename`. Support for the old signature will be removed in v1.5",
-                DeprecationWarning
             )
             filepath = Path(output_filename)
             self.dirpath = filepath.parent

pytorch_lightning/profiler/pytorch.py

@@ -24,7 +24,7 @@
 from torch.autograd.profiler import record_function
 
 from pytorch_lightning.profiler.base import BaseProfiler
-from pytorch_lightning.utilities.distributed import rank_zero_warn
+from pytorch_lightning.utilities.distributed import rank_zero_deprecation, rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _KINETO_AVAILABLE
 
@@ -349,9 +349,9 @@ def __deprecation_check(
             record_functions = set()
 
         if profiled_functions is not None:
-            rank_zero_warn(
+            rank_zero_deprecation(
                 "`PyTorchProfiler.profiled_functions` has been renamed to"
-                " `record_functions` in v1.3 and will be removed in v1.5", DeprecationWarning
+                " `record_functions` in v1.3 and will be removed in v1.5",
             )
             if not record_functions:
                 record_functions |= set(profiled_functions)

pytorch_lightning/trainer/callback_hook.py

@@ -97,10 +97,10 @@ def on_train_epoch_end(self, outputs: EPOCH_OUTPUT):
         """
         for callback in self.callbacks:
             if is_param_in_hook_signature(callback.on_train_epoch_end, "outputs"):
-                warning_cache.warn(
+                warning_cache.deprecation(
                     "The signature of `Callback.on_train_epoch_end` has changed in v1.3."
                     " `outputs` parameter has been removed."
-                    " Support for the old signature will be removed in v1.5", DeprecationWarning
+                    " Support for the old signature will be removed in v1.5"
                 )
                 callback.on_train_epoch_end(self, self.lightning_module, outputs)
             else:

pytorch_lightning/utilities/device_parser.py

@@ -16,7 +16,7 @@
 
 import torch
 
-from pytorch_lightning.utilities import _TPU_AVAILABLE, rank_zero_warn
+from pytorch_lightning.utilities import _TPU_AVAILABLE, rank_zero_deprecation
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _compare_version
 
@@ -121,12 +121,11 @@ def _normalize_parse_gpu_string_input(s: Union[int, str, List[int]]) -> Union[in
     else:
         num_gpus = int(s.strip())
         if _compare_version("pytorch_lightning", operator.lt, "1.5"):
-            rank_zero_warn(
+            rank_zero_deprecation(
                 f"Parsing of the Trainer argument gpus='{s}' (string) will change in the future."
                 " In the current version of Lightning, this will select"
                 f" CUDA device with index {num_gpus}, but from v1.5 it will select gpus"
                 f" {list(range(num_gpus))} (same as gpus={s} (int)).",
-                DeprecationWarning,
             )
             return [num_gpus]
         return num_gpus

pytorch_lightning/utilities/warnings.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from pytorch_lightning.utilities.distributed import rank_zero_warn
+from pytorch_lightning.utilities.distributed import rank_zero_deprecation, rank_zero_warn
 
 
 class WarningCache(set):
@@ -20,3 +20,8 @@ def warn(self, m, *args, **kwargs):
         if m not in self:
             self.add(m)
             rank_zero_warn(m, *args, **kwargs)
+
+    def deprecation(self, m, *args, **kwargs):
+        if m not in self:
+            self.add(m)
+            rank_zero_deprecation(m, *args, **kwargs)

tests/deprecated_api/test_remove_1-5.py

@@ -369,8 +369,10 @@ def test_v1_5_0_datamodule_setter():
     datamodule = BoringDataModule()
     with no_deprecated_call(match="The `LightningModule.datamodule`"):
         model.datamodule = datamodule
-    with pytest.deprecated_call(match="The `LightningModule.datamodule`"):
-        _ = model.datamodule
+    from pytorch_lightning.core.lightning import warning_cache
+    warning_cache.clear()
+    _ = model.datamodule
+    assert any("The `LightningModule.datamodule`" in w for w in warning_cache)
 
 
 def test_v1_5_0_trainer_tbptt_steps(tmpdir):