Remove meta device utilities in favor of torchdistx

pyproject.toml

@@ -71,6 +71,5 @@ module = [
     "pytorch_lightning.tuner.batch_size_scaling",
     "pytorch_lightning.utilities.auto_restart",
     "pytorch_lightning.utilities.data",
-    "pytorch_lightning.utilities.meta",
 ]
 ignore_errors = "True"

src/pytorch_lightning/core/mixins/device_dtype_mixin.py

@@ -18,8 +18,6 @@
 from torch.nn import Module
 from typing_extensions import Self
 
-import pytorch_lightning as pl
-
 
 class DeviceDtypeModuleMixin(Module):
     __jit_unused_properties__ = ["device", "dtype"]
@@ -180,10 +178,8 @@ def half(self) -> Self:  # type: ignore[valid-type]
     def __update_properties(
         self, device: Optional[torch.device] = None, dtype: Optional[Union[str, torch.dtype]] = None
     ) -> None:
-        def apply_fn(module: Union["DeviceDtypeModuleMixin", Module]) -> None:
-            # TODO: Find why `isinstance(module, DeviceDtypeModuleMixin)` doesn't
-            # work when using `init_meta_context`.
-            if not isinstance(module, (DeviceDtypeModuleMixin, pl.LightningModule)):
+        def apply_fn(module: Union[DeviceDtypeModuleMixin, Module]) -> None:
+            if not isinstance(module, DeviceDtypeModuleMixin):
                 return
             if device is not None:
                 module._device = device

src/pytorch_lightning/strategies/launchers/multiprocessing.py

@@ -87,6 +87,7 @@ def launch(self, function: Callable, *args: Any, trainer: Optional["pl.Trainer"]
                 a selected set of attributes get restored in the main process after processes join.
             **kwargs: Optional keyword arguments to be passed to the given function.
         """
+        self._check_torchdistx_support()
         # The default cluster environment in Lightning chooses a random free port number
         # This needs to be done in the main process here before starting processes to ensure each rank will connect
         # through the same port
@@ -178,6 +179,16 @@ def _collect_rank_zero_results(self, trainer: "pl.Trainer", results: Any) -> Opt
 
         return _WorkerOutput(best_model_path, weights_path, trainer.state, results, extra)
 
+    def _check_torchdistx_support(self) -> None:
+        if self._start_method == "spawn":
+            from pytorch_lightning.utilities.meta import _is_deferred
+
+            if _is_deferred(self._strategy.lightning_module):
+                raise NotImplementedError(
+                    f"The `{type(self._strategy).__name__}` strategy does not support `torchdistx`'s deferred"
+                    f" initialization."
+                )
+
     def add_to_queue(self, trainer: "pl.Trainer", queue: "_FakeQueue") -> None:
         """Appends the :attr:`trainer.callback_metrics` dictionary to the given queue. To avoid issues with memory
         sharing, we cast the data to numpy.

src/pytorch_lightning/trainer/trainer.py

@@ -70,7 +70,6 @@
     XLAProfiler,
 )
 from pytorch_lightning.strategies import ParallelStrategy, Strategy
-from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
 from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
 from pytorch_lightning.trainer.configuration_validator import verify_loop_configurations
 from pytorch_lightning.trainer.connectors.accelerator_connector import _LITERAL_WARN, AcceleratorConnector
@@ -106,8 +105,7 @@
 from pytorch_lightning.utilities.data import _auto_add_worker_init_fn, has_len_all_ranks
 from pytorch_lightning.utilities.distributed import distributed_available
 from pytorch_lightning.utilities.exceptions import ExitGracefullyException, MisconfigurationException
-from pytorch_lightning.utilities.imports import _fault_tolerant_training
-from pytorch_lightning.utilities.meta import is_on_meta_device, materialize_module
+from pytorch_lightning.utilities.imports import _fault_tolerant_training, _module_available
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.seed import isolate_rng
@@ -1469,20 +1467,14 @@ def _call_setup_hook(self) -> None:
 
     def _call_configure_sharded_model(self) -> None:
         with self.strategy.model_sharded_context():
-            self._handle_meta_model()
-            self._call_lightning_module_hook("configure_sharded_model")
-            self._call_callback_hooks("on_configure_sharded_model")
-
-    def _handle_meta_model(self) -> None:
-        if not is_on_meta_device(self.lightning_module):
-            return
+            # experimental support for torchdistx
+            if _module_available("torchdistx.deferred_init"):
+                from torchdistx.deferred_init import materialize_module
 
-        if isinstance(self.strategy, DDPSpawnStrategy):
-            raise MisconfigurationException("LightningModule on meta device isn't supported with spawn.")
+                materialize_module(self.lightning_module)
 
-        materialize_module(self.lightning_module)
-        # the trainer reference is lost during materialization
-        self.lightning_module.trainer = proxy(self)
+            self._call_lightning_module_hook("configure_sharded_model")
+            self._call_callback_hooks("on_configure_sharded_model")
 
     def _call_teardown_hook(self) -> None:
         fn = self.state.fn._setup_fn

src/pytorch_lightning/utilities/cli.py

@@ -22,7 +22,7 @@
 
 import pytorch_lightning as pl
 import pytorch_lightning.cli as new_cli
-from pytorch_lightning.utilities.meta import get_all_subclasses
+from pytorch_lightning.utilities.meta import _get_all_subclasses
 from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation
 
 _deprecate_registry_message = (
@@ -108,17 +108,17 @@ def _populate_registries(subclasses: bool) -> None:  # Remove in v1.9
     if subclasses:
         rank_zero_deprecation(_deprecate_auto_registry_message)
         # this will register any subclasses from all loaded modules including userland
-        for cls in get_all_subclasses(torch.optim.Optimizer):
+        for cls in _get_all_subclasses(torch.optim.Optimizer):
             OPTIMIZER_REGISTRY(cls, show_deprecation=False)
-        for cls in get_all_subclasses(torch.optim.lr_scheduler._LRScheduler):
+        for cls in _get_all_subclasses(torch.optim.lr_scheduler._LRScheduler):
             LR_SCHEDULER_REGISTRY(cls, show_deprecation=False)
-        for cls in get_all_subclasses(pl.Callback):
+        for cls in _get_all_subclasses(pl.Callback):
             CALLBACK_REGISTRY(cls, show_deprecation=False)
-        for cls in get_all_subclasses(pl.LightningModule):
+        for cls in _get_all_subclasses(pl.LightningModule):
             MODEL_REGISTRY(cls, show_deprecation=False)
-        for cls in get_all_subclasses(pl.LightningDataModule):
+        for cls in _get_all_subclasses(pl.LightningDataModule):
             DATAMODULE_REGISTRY(cls, show_deprecation=False)
-        for cls in get_all_subclasses(pl.loggers.Logger):
+        for cls in _get_all_subclasses(pl.loggers.Logger):
             LOGGER_REGISTRY(cls, show_deprecation=False)
     else:
         # manually register torch's subclasses and our subclasses

src/pytorch_lightning/utilities/data.py

@@ -18,7 +18,7 @@
 from contextlib import contextmanager
 from dataclasses import fields
 from functools import partial
-from typing import Any, Callable, Dict, Generator, Iterable, Mapping, Optional, Set, Tuple, Type, Union
+from typing import Any, Callable, Dict, Generator, Iterable, Mapping, Optional, Tuple, Type, Union
 
 import torch
 from torch import Tensor
@@ -39,6 +39,7 @@
 from pytorch_lightning.utilities.auto_restart import CaptureIterableDataset, CaptureMapDataset, FastForwardSampler
 from pytorch_lightning.utilities.enums import _FaultTolerantMode
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.meta import _get_all_subclasses
 from pytorch_lightning.utilities.rank_zero import rank_zero_warn
 from pytorch_lightning.utilities.seed import pl_worker_init_function
 from pytorch_lightning.utilities.warnings import WarningCache
@@ -493,20 +494,6 @@ def wrapper(obj: Any, *args: Any, **kwargs: Any) -> None:
     return wrapper
 
 
-# https://stackoverflow.com/a/63851681/9201239
-def _get_all_subclasses(cls: Type[Any]) -> Set[Type[Any]]:
-    """Returns a list of all classes that inherit directly or indirectly from the given class."""
-    subclasses = set()
-
-    def recurse(cl: Type[Any]) -> None:
-        for subclass in cl.__subclasses__():
-            subclasses.add(subclass)
-            recurse(subclass)
-
-    recurse(cls)
-    return subclasses
-
-
 @contextmanager
 def _replace_init_method(base_cls: Type, store_explicit_arg: Optional[str] = None) -> Generator[None, None, None]:
     """This context manager is used to add support for re-instantiation of custom (subclasses) of `base_cls`.