Resolve instantiation problem with init_meta_context

CHANGELOG.md

@@ -131,6 +131,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `CombinedLoader` and `max_size_cycle` didn't receive a `DistributedSampler` ([#10374](https://github.com/PyTorchLightning/pytorch-lightning/issues/10374))
 
 
+- Fixed `isinstance` not working with `init_meta_context`, materialize model not being moved to the device ([#10493](https://github.com/PyTorchLightning/metrics/pull/10493))
+
+
 - Fixed an issue that prevented the Trainer to shutdown workers when execution is interrupted due to failure([#10463](https://github.com/PyTorchLightning/pytorch-lightning/issues/10463))
 
 

pytorch_lightning/core/mixins/device_dtype_mixin.py

@@ -17,6 +17,8 @@
 import torch
 from torch.nn import Module
 
+import pytorch_lightning as pl
+
 
 class DeviceDtypeModuleMixin(Module):
     __jit_unused_properties__ = ["device", "dtype"]
@@ -177,7 +179,9 @@ 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:
-            if not isinstance(module, DeviceDtypeModuleMixin):
+            # TODO: Find why `isinstance(module, DeviceDtypeModuleMixin)` doesn't
+            # work when using `init_meta_context`.
+            if not isinstance(module, (DeviceDtypeModuleMixin, pl.LightningModule)):
                 return
             if device is not None:
                 module._device = device

pytorch_lightning/trainer/trainer.py

@@ -84,7 +84,7 @@
 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 materialize_module
+from pytorch_lightning.utilities.meta import is_meta_device, materialize_module
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import (
@@ -1406,10 +1406,21 @@ def _call_setup_hook(self) -> None:
 
     def _call_configure_sharded_model(self) -> None:
         with self.accelerator.model_sharded_context():
-            materialize_module(self.lightning_module)
+            self._handle_meta_model()
             self.call_hook("configure_sharded_model")
             self.call_hook("on_configure_sharded_model")
 
+    def _handle_meta_model(self) -> None:
+        if not is_meta_device(self.lightning_module):
+            return
+
+        if isinstance(self.training_type_plugin, DDPSpawnPlugin):
+            raise MisconfigurationException("LightningModule on meta device isn't supported with spawn.")
+
+        materialize_module(self.lightning_module)
+        # the trainer reference is lost during materialization
+        self.lightning_module.trainer = proxy(self)
+
     def _call_teardown_hook(self) -> None:
         fn = self.state.fn._setup_fn
 

pytorch_lightning/utilities/meta.py

@@ -18,13 +18,14 @@
 from functools import partial
 from itertools import chain
 from types import ModuleType
-from typing import Callable, Dict, Generator, Iterator, List, Optional, Set, Type
+from typing import Any, Callable, Dict, Generator, Iterator, List, Optional, Set, Type
 
 import torch
 from torch import nn, Tensor
 from torch.nn import Module
 from torch.nn.modules.container import ModuleDict, ModuleList, Sequential
 
+import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_10
@@ -191,7 +192,6 @@ def materialize_module(root_module: nn.Module) -> nn.Module:
 
 # cache subclasses to optimize the search when resetting the meta device later on.
 __STORAGE_META__ = {}
-
 __CREATED_MODULES__ = set()
 
 
@@ -237,45 +237,52 @@ def _set_meta_device() -> None:
 
     for subclass in get_all_subclasses(torch.nn.modules.module.Module):
 
-        if isinstance(subclass, (Sequential, ModuleList, ModuleDict)):
+        if subclass in (Sequential, ModuleList, ModuleDict, pl.LightningModule):
             continue
 
         # if a subclass has already been stored, we should use the cache
         if str(subclass) in __STORAGE_META__:
-            # reset the class import package to its rightfull state.
+            # reset the class import package to its rightful state.
             mods, subclass, meta_class = __STORAGE_META__[subclass]
             for mod in mods:
                 setattr(mod, subclass.__name__, meta_class)
             continue
 
+        class _IsinstanceMetaclass(type(subclass)):
+            def __instancecheck__(self, instance: Any) -> bool:
+                """Overrides the ``isinstance`` check on ``_MaterializerModule`` objects."""
+                return isinstance(instance, self.__bases__[0])
+
         # Create a class subclassing current `subclass` overriding its new method.
         # this will enable use to use `torch.distributed.nn.utils.init_meta` to create a `meta`
         # version of the current subclass module
-        class _MetaClass(subclass):
+        class _MaterializerModule(subclass, metaclass=_IsinstanceMetaclass):
             @classmethod
             @contextmanager
-            def instantiation_context(cls, materialize: bool):
+            def instantiation_context(cls):
                 _unset_meta_device(from_created=True)
                 yield
                 _set_meta_device_populated(from_created=True)
 
             @classmethod
             def materialize(cls, materialize_fn: Callable):
-                with cls.instantiation_context(materialize=True):
+                with cls.instantiation_context():
                     obj = materialize_fn()
                 return obj
 
             @staticmethod
             def add_subclasses(subclass):
-                """This is used to unrol the instantion tree while creating the modules."""
-                __CREATED_MODULES__.add(subclass)
+                """This is used to unroll the instantiation tree while creating the modules."""
+                # Don't store the LightningModule as skipped from the Meta process.
+                if subclass != pl.LightningModule:
+                    __CREATED_MODULES__.add(subclass)
                 if subclass.__bases__[0] != torch.nn.modules.module.Module:
-                    _MetaClass.add_subclasses(subclass.__bases__[0])
+                    _MaterializerModule.add_subclasses(subclass.__bases__[0])
 
             def __new__(cls, *args, **kwargs):
                 subclass = cls.__bases__[0]
                 cls.add_subclasses(subclass)
-                with cls.instantiation_context(materialize=False):
+                with cls.instantiation_context():
                     obj = init_meta(subclass, *args, **kwargs)
 
                 obj.materialize = partial(cls.materialize, materialize_fn=obj.materialize)
@@ -294,9 +301,8 @@ def search(mod: ModuleType) -> List[ModuleType]:
         # nn.Module class can be imported at different level and they all need to be mocked.
         # Example: torch.nn.Linear is actually torch.nn.modules.linear.Linear
         # Therefore, torch.nn.Linear, torch.nn.modules.Linear, torch.nn.modules.linear.Linear
-        # needs to be replaced by the torch.nn.linear.modules.Linear _MetaClass
-        out = []
-        out.append(search(mod))
+        # needs to be replaced by the torch.nn.linear.modules.Linear _MaterializerModule
+        out = [search(mod)]
         for name in submodules[1:]:
             mod = getattr(mod, name)
             out.append(search(mod))
@@ -305,11 +311,11 @@ def search(mod: ModuleType) -> List[ModuleType]:
         mods = [mod for mod in chain(*out) if mod]
 
         # store the modules search so it doesn't have to be performed again for this class
-        __STORAGE_META__[subclass] = (mods, subclass, _MetaClass)
+        __STORAGE_META__[subclass] = (mods, subclass, _MaterializerModule)
 
         # replace all subclass by its meta form
         for mod in mods:
-            setattr(mod, subclass.__name__, _MetaClass)
+            setattr(mod, subclass.__name__, _MaterializerModule)
 
 
 @contextmanager
@@ -321,3 +327,11 @@ def init_meta_context() -> Generator:
     _set_meta_device()
     yield
     _unset_meta_device()
+
+
+def is_meta_device(module: nn.Module) -> bool:
+    try:
+        param = next(module.parameters())
+        return param.device.type == "meta"
+    except StopIteration:
+        return False

tests/utilities/test_meta.py

@@ -14,7 +14,7 @@
 from torch import nn
 
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities.meta import init_meta_context, materialize_module
+from pytorch_lightning.utilities.meta import init_meta_context, is_meta_device, materialize_module
 from tests.helpers.runif import RunIf
 
 
@@ -31,18 +31,23 @@ def __init__(self, num_layers: int):
         self.layer = nn.Sequential(*[nn.Linear(1, 1) for _ in range(self.hparams.num_layers)])
 
 
-@RunIf(min_torch="1.10.0")
+@RunIf(special=True, min_torch="1.10.0")
 def test_init_meta_context():
 
     with init_meta_context():
         m = nn.Linear(in_features=1, out_features=1)
+        assert isinstance(m, nn.Linear)
         assert m.weight.device.type == "meta"
+        assert is_meta_device(m)
         mlp = MLP(4)
         assert mlp.layer[0].weight.device.type == "meta"
 
         mlp = materialize_module(mlp)
         assert mlp.layer[0].weight.device.type == "cpu"
 
+        assert not is_meta_device(mlp)
+        assert not is_meta_device(nn.Module())
+
         model = BoringModel(4)
         assert model.layer[0].weight.device.type == "meta"
         materialize_module(model)