[bug/feat] Support parameters_to_ignore in DDP (#7239)

* update

* update

* update

* update on comments

* update

CHANGELOG.md

@@ -367,6 +367,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed metrics not being properly logged with `precision=16` and `manual_optimization` ([#7228](https://github.com/PyTorchLightning/pytorch-lightning/pull/7228))
 
 
+- Fixed `parameters_to_ignore` not properly set to DDPWrapper ([#7239](https://github.com/PyTorchLightning/pytorch-lightning/pull/7239))
+
+
 ## [1.2.7] - 2021-04-06
 
 ### Fixed

pytorch_lightning/overrides/base.py

@@ -36,6 +36,9 @@ class _LightningModuleWrapperBase(DeviceDtypeModuleMixin, torch.nn.Module):
         super().__init__()
         self.module = pl_module
 
+        # set the parameters_to_ignore from LightningModule.
+        self._ddp_params_and_buffers_to_ignore = getattr(pl_module, "_ddp_params_and_buffers_to_ignore", [])
+
     def forward(self, *inputs, **kwargs):
         trainer = self.module.trainer
 

tests/accelerators/test_ddp.py

@@ -18,8 +18,11 @@ from unittest.mock import patch
 
 import pytest
 import torch
+from torch.nn.parallel.distributed import DistributedDataParallel
 
+import pytorch_lightning as pl
 from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import Callback
 from tests.accelerators import ddp_model
 from tests.helpers.boring_model import BoringModel
 from tests.helpers.runif import RunIf
@@ -117,3 +120,35 @@ def test_ddp_torch_dist_is_available_in_setup(mock_set_device, mock_is_available
     )
     with pytest.raises(SystemExit):
         trainer.fit(model)
+
+
+@RunIf(min_gpus=2, min_torch="1.8.1", special=True)
+def test_ddp_wrapper(tmpdir):
+    """
+    Test parameters to ignore are carried over for DDP.
+    """
+
+    class WeirdModule(torch.nn.Module):
+
+        def _save_to_state_dict(self, destination, prefix, keep_vars):
+            return {"something": "something"}
+
+    class CustomModel(BoringModel):
+
+        def __init__(self):
+            super().__init__()
+            self.weird_module = WeirdModule()
+
+            # should be skip.
+            self._ddp_params_and_buffers_to_ignore = ('something')
+
+    class CustomCallback(Callback):
+
+        def on_train_start(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None:
+            assert isinstance(trainer.training_type_plugin.model, DistributedDataParallel)
+            assert trainer.training_type_plugin.model.parameters_to_ignore == ('something')
+            assert trainer.training_type_plugin.model.module._ddp_params_and_buffers_to_ignore == ('something')
+
+    model = CustomModel()
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="ddp", gpus=2, callbacks=CustomCallback())
+    trainer.fit(model)