Merge branch 'master' into ci/drop-1.6

Author: carmocca

CHANGELOG.md

@@ -72,6 +72,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed deprecated `has_prepared_data`, `has_setup_fit`, `has_setup_validate`, `has_setup_test`, `has_setup_predict`, `has_teardown_fit`, `has_teardown_validate`, `has_teardown_test` and `has_teardown_predict` datamodule lifecycle properties  ([#10350](https://github.com/PyTorchLightning/pytorch-lightning/pull/10350))
 
 
+- Removed deprecated `every_n_val_epochs` parameter of ModelCheckpoint ([#10366](https://github.com/PyTorchLightning/pytorch-lightning/pull/10366))
+
+
+- Removed deprecated property `configure_slurm_dpp` from accelerator connector ([#10370](https://github.com/PyTorchLightning/pytorch-lightning/pull/10370))
+
+
 - Removed deprecated arguments `num_nodes` and `sync_batchnorm` from `DDPPlugin`, `DDPSpawnPlugin`, `DeepSpeedPlugin` ([#10357](https://github.com/PyTorchLightning/pytorch-lightning/pull/10357))
 
 
@@ -85,6 +91,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `apply_to_collection(defaultdict)` ([#10316](https://github.com/PyTorchLightning/pytorch-lightning/issues/10316))
 
 
+- Fixed interception of `__init__` arguments for sub-classed DataLoader re-instantiation in Lite ([#10334](https://github.com/PyTorchLightning/pytorch-lightning/issues/10334))
+
+
 - Fixed failure when `DataLoader(batch_size=None)` is passed ([#10345](https://github.com/PyTorchLightning/pytorch-lightning/issues/10345))
 
 

pytorch_lightning/callbacks/model_checkpoint.py

@@ -33,7 +33,7 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.types import _METRIC, _PATH, STEP_OUTPUT
@@ -123,12 +123,6 @@ class ModelCheckpoint(Callback):
             where both values for ``every_n_epochs`` and ``check_val_every_n_epoch`` evenly divide E.
         save_on_train_epoch_end: Whether to run checkpointing at the end of the training epoch.
             If this is ``False``, then the check runs at the end of the validation.
-        every_n_val_epochs: Number of epochs between checkpoints.
-
-            .. warning::
-               This argument has been deprecated in v1.4 and will be removed in v1.6.
-
-            Use ``every_n_epochs`` instead.
 
     Note:
         For extra customization, ModelCheckpoint includes the following attributes:
@@ -214,7 +208,6 @@ def __init__(
         train_time_interval: Optional[timedelta] = None,
         every_n_epochs: Optional[int] = None,
         save_on_train_epoch_end: Optional[bool] = None,
-        every_n_val_epochs: Optional[int] = None,
     ):
         super().__init__()
         self.monitor = monitor
@@ -233,13 +226,6 @@ def __init__(
         self.best_model_path = ""
         self.last_model_path = ""
 
-        if every_n_val_epochs is not None:
-            rank_zero_deprecation(
-                "`ModelCheckpoint(every_n_val_epochs)` is deprecated in v1.4 and will be removed in v1.6."
-                " Please use `every_n_epochs` instead."
-            )
-            every_n_epochs = every_n_val_epochs
-
         self.__init_monitor_mode(mode)
         self.__init_ckpt_dir(dirpath, filename)
         self.__init_triggers(every_n_train_steps, every_n_epochs, train_time_interval)

pytorch_lightning/lite/wrappers.py

@@ -14,6 +14,7 @@
 import functools
 import inspect
 from contextlib import contextmanager
+from itertools import chain
 from typing import Any, Callable, Dict, Generator, Iterable, Iterator, Optional, Set, Sized, Type, Union
 
 import torch
@@ -109,7 +110,7 @@ def wrapper(module: Any, *args: Any, **kwargs: Dict[str, Any]) -> None:
         params = dict(inspect.signature(module._old_init).parameters)
         params.pop("args")
         params.pop("kwargs")
-        for init_name, init_arg in zip(params, args):
+        for init_name, init_arg in chain(zip(params, args), kwargs.items()):
             setattr(module, init_name, init_arg)
         f(module, *args, **kwargs)
 
@@ -118,15 +119,15 @@ def wrapper(module: Any, *args: Any, **kwargs: Dict[str, Any]) -> None:
 
 # https://stackoverflow.com/a/63851681/9201239
 def _get_all_subclasses(cls: Type[Any]) -> Set[Type[Any]]:
-    subclass_list = []
+    subclasses = set()
 
     def recurse(cl: Type[Any]) -> None:
         for subclass in cl.__subclasses__():
-            subclass_list.append(subclass)
+            subclasses.add(subclass)
             recurse(subclass)
 
     recurse(cls)
-    return set(subclass_list)
+    return subclasses
 
 
 def _enable_class(cls: Type[Any]) -> None:

pytorch_lightning/loggers/wandb.py

@@ -490,7 +490,6 @@ def _scan_and_log_checkpoints(self, checkpoint_callback: "ReferenceType[ModelChe
                             "save_top_k",
                             "save_weights_only",
                             "_every_n_train_steps",
-                            "_every_n_val_epochs",
                         ]
                         # ensure it does not break if `ModelCheckpoint` args change
                         if hasattr(checkpoint_callback, k)

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -990,12 +990,6 @@ def update_device_type_if_training_type_plugin_passed(self) -> None:
                 elif self.has_gpu:
                     self._device_type = DeviceType.GPU
 
-    def configure_slurm_ddp(self) -> None:
-        rank_zero_deprecation(
-            "`AcceleratorConnector.configure_slurm_ddp()` was deprecated in v1.5 and will be removed in v1.6."
-        )
-        self._configure_slurm_ddp()
-
     def _configure_slurm_ddp(self):
         # extract SLURM flag vars
         # whenever we have the correct number of tasks, we let slurm manage processes

tests/deprecated_api/test_remove_1-6.py

@@ -19,7 +19,6 @@
 from torch.optim import Optimizer
 
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 from pytorch_lightning.plugins import PrecisionPlugin
 from pytorch_lightning.plugins.training_type import DDPPlugin
@@ -167,11 +166,6 @@ def test_v1_6_0_deprecated_disable_validation():
         _ = trainer.disable_validation
 
 
-def test_v1_6_0_every_n_val_epochs():
-    with pytest.deprecated_call(match="use `every_n_epochs` instead"):
-        _ = ModelCheckpoint(every_n_val_epochs=1)
-
-
 def test_v1_6_0_deprecated_hpc_load(tmpdir):
     model = BoringModel()
     trainer = Trainer(default_root_dir=tmpdir, max_steps=1)
@@ -267,12 +261,6 @@ def test_v1_6_0_deprecated_accelerator_pass_through_functions():
         accelerator.on_train_batch_start(batch=None, batch_idx=0)
 
 
-def test_v1_6_0_configure_slurm_ddp():
-    trainer = Trainer()
-    with pytest.deprecated_call(match=r"`AcceleratorConnector.configure_slurm_ddp\(\)` was deprecated in v1.5"):
-        trainer._accelerator_connector.configure_slurm_ddp()
-
-
 def test_v1_6_0_master_params():
     with pytest.deprecated_call(match="`PrecisionPlugin.master_params` was deprecated in v1.5"):
         PrecisionPlugin().master_params(Mock(spec=Optimizer))

tests/lite/test_lite.py

@@ -164,6 +164,34 @@ def test_setup_dataloaders_return_type():
     assert lite_dataloader1.dataset is dataset1
 
 
+def test_setup_dataloaders_with_custom_type():
+    """Test that Lite intercepts arguments passed to custom subclasses of torch.utils.DataLoader and sets them as
+    attributes."""
+
+    class DataLoaderSubclass1(DataLoader):
+        def __init__(self, attribute1, *args, **kwargs):
+            # intentionally not setting this attribute, calling super with different args
+            # self.attribute1 = attribute1
+            super().__init__(*args, **kwargs)
+
+    class DataLoaderSubclass2(DataLoaderSubclass1):
+        def __init__(self, attribute1, attribute2, *args, **kwargs):
+            # intentionally not setting this attribute, calling super with different args
+            # self.attribute2 = attribute2
+            super().__init__(attribute1, *args, **kwargs)
+
+    class LiteWithCustomDataLoader(LightningLite):
+        def run(self):
+            dataloader = DataLoaderSubclass2("attribute1", "attribute2", dataset=range(4), batch_size=2)
+            assert dataloader.attribute1 == "attribute1"
+            assert dataloader.attribute2 == "attribute2"
+            lite_dataloader = self.setup_dataloaders(dataloader)
+            assert lite_dataloader.attribute1 == "attribute1"
+            assert lite_dataloader.attribute2 == "attribute2"
+
+    LiteWithCustomDataLoader().run()
+
+
 def test_setup_custom_dataloaders():
     """Test that the setup_dataloaders method returns the dataloaders wrapped as LiteDataLoader."""
     lite = EmptyLite()