Move parameter validation specific to TPU Training plugins

pytorch_lightning/core/decorators.py

@@ -71,12 +71,11 @@ def auto_transfer_args(self, *args, **kwargs):
 
 def parameter_validation(fn: Callable) -> Callable:
     """
-    Decorator for :meth:`~pytorch_lightning.core.LightningModule.to` method.
     Validates that the module parameter lengths match after moving to the device. It is useful
     when tying weights on TPU's.
 
     Args:
-        fn: ``.to`` method
+        fn: ``model_to_device`` method
 
     Note:
         TPU's require weights to be tied/shared after moving the module to the device.
@@ -90,10 +89,10 @@ def parameter_validation(fn: Callable) -> Callable:
 
     @wraps(fn)
     def inner_fn(self, *args, **kwargs):
-        pre_layer_count = len(list(self.parameters()))
+        pre_layer_count = len(list(self.model.parameters()))
         module = fn(self, *args, **kwargs)
-        self.on_post_move_to_device()
-        post_layer_count = len(list(self.parameters()))
+        self.model.on_post_move_to_device()
+        post_layer_count = len(list(self.model.parameters()))
 
         if not pre_layer_count == post_layer_count:
             rank_zero_warn(

pytorch_lightning/plugins/training_type/single_tpu.py

@@ -15,6 +15,7 @@
 
 import torch
 
+from pytorch_lightning.core.decorators import parameter_validation
 from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin
 from pytorch_lightning.utilities import _TPU_AVAILABLE
 from pytorch_lightning.utilities.apply_func import move_data_to_device
@@ -39,6 +40,7 @@ def __init__(self, device: int, debug: bool = False):
     def is_distributed(self) -> bool:
         return False
 
+    @parameter_validation
     def model_to_device(self) -> None:
         self.model.to(self.root_device)
 

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -23,6 +23,7 @@
 from torch.utils.data import DataLoader
 
 import pytorch_lightning as pl
+from pytorch_lightning.core.decorators import parameter_validation
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
 from pytorch_lightning.trainer.connectors.data_connector import _PatchDataLoader
@@ -171,6 +172,7 @@ def new_process(self, process_idx: int, trainer, mp_queue) -> None:
         if self.local_rank == 0:
             time.sleep(2)
 
+    @parameter_validation
     def model_to_device(self) -> None:
         self.model = self.wrapped_model.to(self.root_device)
 

pytorch_lightning/utilities/device_dtype_mixin.py

@@ -17,8 +17,6 @@
 import torch
 from torch.nn import Module
 
-from pytorch_lightning.core.decorators import parameter_validation
-
 
 class DeviceDtypeModuleMixin(Module):
     __jit_unused_properties__ = ['device', 'dtype']
@@ -47,7 +45,6 @@ def device(self) -> Union[str, torch.device]:
 
         return device
 
-    @parameter_validation
     def to(self, *args, **kwargs) -> Module:
         """Moves and/or casts the parameters and buffers.
 
@@ -84,9 +81,6 @@ def to(self, *args, **kwargs) -> Module:
             ...     def __init__(self, weight: torch.Tensor):
             ...         super().__init__()
             ...         self.register_buffer('weight', weight)
-            ...
-            ...     def on_post_move_to_device(self):
-            ...         pass
             >>> _ = torch.manual_seed(0)
             >>> module = ExampleModule(torch.rand(3, 4))
             >>> module.weight #doctest: +ELLIPSIS

tests/accelerators/test_tpu_backend.py

@@ -95,25 +95,21 @@ def test_weight_tying_warning(tmpdir, capsys=None):
         trainer.fit(model)
 
 
-# @RunIf(tpu=True)
-# @pl_multi_process_test
-# def test_if_weights_tied(tmpdir, capsys=None):
-#     """
-#     Test if weights are properly tied on `on_post_move_to_device`.
-#     Ensure no warning for parameter mismatch is thrown.
-#     """
-
-#     # TODO (kaushikb11): Add `parameter_validation` specific to TPU Accelerators
-#     class Model(WeightSharingModule):
+@RunIf(tpu=True)
+@pl_multi_process_test
+def test_if_weights_tied(tmpdir, capsys=None):
+    """
+    Test if weights are properly tied on `on_post_move_to_device`.
+    Ensure no warning for parameter mismatch is thrown.
+    """
 
-#         def on_post_move_to_device(self):
-#             self.layer_3.weight = self.layer_1.weight
+    class Model(WeightSharingModule):
 
-#     model = Model()
-#     trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
+        def on_post_move_to_device(self):
+            self.layer_3.weight = self.layer_1.weight
 
-#     with pytest.warns(UserWarning) as warnings:
-#         trainer.fit(model)
+    model = Model()
+    trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
 
-#     assert not list(filter(lambda x: 'The model layers do not match' in str(x), warnings.list))
-#     assert len(trainer.test(model)) == 1
+    with pytest.warns(UserWarning, match="The model layers do not match"):
+        trainer.fit(model)