diff --git a/mmcv/parallel/distributed.py b/mmcv/parallel/distributed.py
index e339ab8f57..bf34cb5906 100644
--- a/mmcv/parallel/distributed.py
+++ b/mmcv/parallel/distributed.py
@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple
+from typing import Any, List, Tuple
 
 import torch
 from torch.nn.parallel.distributed import (DistributedDataParallel,
@@ -140,3 +140,28 @@ def val_step(self, *inputs, **kwargs):
                     and digit_version(TORCH_VERSION) > digit_version('1.2')):
                 self.require_forward_param_sync = False
         return output
+
+    def _run_ddp_forward(self, *inputs, **kwargs) -> Any:
+        """Processes inputs and runs ``self.module.forward``.
+
+        Pytorch 1.12.0 performs ``self.module.forward`` in ``_run_ddp_forward``
+        and deprecates using ``DistributedDataParallel.to_kwargs`` to
+        process inputs, which leads to inputs cannot be processed by
+        :meth:`MMDistributedDataParallel.to_kwargs` anymore. Therefore,
+        ``MMDistributedDataParallel`` overrides this method to call
+        :meth:`to_kwargs` explicitly.
+
+        See more information in `<https://github.com/open-mmlab/mmsegmentation/issues/1742>`_.  # noqa: E501
+
+        Returns:
+            Any: Forward result of :attr:`module`.
+        """
+        module_to_run = self._replicated_tensor_module if \
+            self._use_replicated_tensor_module else self.module
+
+        if self.device_ids:
+            inputs, kwargs = self.to_kwargs(  # type: ignore
+                inputs, kwargs, self.device_ids[0])
+            return module_to_run(*inputs[0], **kwargs[0])  # type: ignore
+        else:
+            return module_to_run(*inputs, **kwargs)