diff --git a/CHANGELOG.md b/CHANGELOG.md
index b85f3c76cad46..29585a0e82ac8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -201,7 +201,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - LightningLite:
     * Added `PrecisionPlugin.forward_context`, making it the default implementation for all `{train,val,test,predict}_step_context()` methods ([#9988](https://github.com/PyTorchLightning/pytorch-lightning/pull/9988))
-    * Added `DDPSpawnPlugin.spawn()` for spawning new processes of a given function ([#10018](https://github.com/PyTorchLightning/pytorch-lightning/pull/10018))
+    * Added `DDPSpawnPlugin.spawn()` for spawning new processes of a given function ([#10018](https://github.com/PyTorchLightning/pytorch-lightning/pull/10018), [#10022](https://github.com/PyTorchLightning/pytorch-lightning/pull/10022))
     * Added `TrainingTypePlugin.{_setup_model, _setup_optimizer}` methods ([#9994](https://github.com/PyTorchLightning/pytorch-lightning/pull/9994))
 
 ### Changed
@@ -500,6 +500,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Remove deprecated `distributed_backend` from `Trainer` ([#10017](https://github.com/PyTorchLightning/pytorch-lightning/pull/10017))
 
 
+- Removed `process_idx` from the `{DDPSpawnPlugin,TPUSpawnPlugin}.new_process` methods ([#10022](https://github.com/PyTorchLightning/pytorch-lightning/pull/10022))
+
+
 ### Fixed
 
 
diff --git a/pytorch_lightning/plugins/training_type/tpu_spawn.py b/pytorch_lightning/plugins/training_type/tpu_spawn.py
index 55e62aade809d..e71d1c64f8d65 100644
--- a/pytorch_lightning/plugins/training_type/tpu_spawn.py
+++ b/pytorch_lightning/plugins/training_type/tpu_spawn.py
@@ -15,7 +15,8 @@
 import os
 import re
 import time
-from typing import Any, Dict, List, Optional, Union
+from multiprocessing.queues import SimpleQueue
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 import torch.multiprocessing as mp
@@ -148,17 +149,9 @@ def init_ddp_connection(self, global_rank: int, world_size: int) -> None:
     def set_world_ranks(self, process_idx: int = 0) -> None:
         pass
 
-    def new_process(self, process_idx: int, trainer, mp_queue) -> None:
+    def new_process(self, trainer: "pl.Trainer", mp_queue: SimpleQueue) -> None:
         self.mp_queue = mp_queue
 
-        reset_seed()
-
-        self.tpu_local_core_rank = xm.get_local_ordinal()
-        self.tpu_global_core_rank = xm.get_ordinal()
-
-        # set warning rank
-        rank_zero_only.rank = self.global_rank
-
         if self.tpu_global_core_rank != 0 and trainer.progress_bar_callback is not None:
             trainer.progress_bar_callback.disable()
 
@@ -261,26 +254,31 @@ def _close_logger(self, trainer) -> None:
         if trainer.logger is not None:
             trainer.logger.finalize("success")
 
-    def get_mp_spawn_kwargs(self, trainer: "pl.Trainer") -> dict:
+    def get_mp_spawn_kwargs(self, trainer: Optional["pl.Trainer"] = None) -> Dict[str, Any]:
         return {
-            "args": (trainer, self.mp_queue),
             "nprocs": len(self.parallel_devices),
             "start_method": self.start_method,
         }
 
+    def spawn(self, function: Callable, *args: Any, **kwargs: Any) -> None:
+        xmp.spawn(self._wrapped_function, args=(function, args, kwargs), **self.get_mp_spawn_kwargs())
+
+    def _worker_setup(self, process_idx: int):
+        reset_seed()
+        self.tpu_local_core_rank = xm.get_local_ordinal()
+        self.tpu_global_core_rank = xm.get_ordinal()
+        rank_zero_only.rank = self.global_rank
+
     def start_training(self, trainer: "pl.Trainer") -> None:
         # todo: precision pluging is call in accelerator setup and should be moved
         if "XLA_USE_BF16" in os.environ:
             del os.environ["XLA_USE_BF16"]
         self._close_logger(trainer)
-        xmp.spawn(self.new_process, **self.get_mp_spawn_kwargs(trainer))
+        return super().start_training(trainer)
 
     def start_evaluating(self, trainer: "pl.Trainer") -> None:
         self._close_logger(trainer)
-        xmp.spawn(self.new_process, **self.get_mp_spawn_kwargs(trainer))
-
-    def start_predicting(self, trainer: "pl.Trainer") -> None:
-        xmp.spawn(self.new_process, **self.get_mp_spawn_kwargs(trainer))
+        return super().start_evaluating(trainer)
 
     def training_step(self, *args, **kwargs):
         return self.model(*args, **kwargs)