Only allow one value for each plugin type in plugins flag

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -14,7 +14,8 @@
 
 import logging
 import os
-from typing import List, Optional, Union
+from collections import Counter
+from typing import Dict, List, Optional, Union
 
 import torch
 
@@ -304,34 +305,46 @@ def _check_config_and_set_final_flags(
             self._precision_flag = precision
 
         if plugins:
+            plugins_flags_types: Dict[str, int] = Counter()
             for plugin in plugins:
                 if isinstance(plugin, Strategy) or isinstance(plugin, str) and plugin in self._registered_strategies:
                     self._strategy_flag = plugin
                     rank_zero_deprecation(
                         f"Passing {plugin} `strategy` to the `plugins` flag in Trainer has been deprecated"
                         f" in v1.5 and will be removed in v1.7. Use `Trainer(strategy={plugin})` instead."
                     )
+                    plugins_flags_types[Strategy.__name__] += 1
 
                 elif isinstance(plugin, PrecisionPlugin):
                     self._precision_plugin_flag = plugin
-                elif isinstance(plugin, str) and plugin in self._precision_types:
-                    self._precision_flag = plugin
+                    plugins_flags_types[PrecisionPlugin.__name__] += 1
                 elif isinstance(plugin, CheckpointIO):
                     self.checkpoint_io = plugin
+                    plugins_flags_types[CheckpointIO.__name__] += 1
                 elif isinstance(plugin, ClusterEnvironment):
                     self._cluster_environment_flag = plugin
+                    plugins_flags_types[ClusterEnvironment.__name__] += 1
                 elif isinstance(plugin, LayerSync):
                     if sync_batchnorm and not isinstance(plugin, NativeSyncBatchNorm):
                         raise MisconfigurationException(
                             f"You set `Trainer(sync_batchnorm=True)` and provided a `{plugin.__class__.__name__}`"
                             " plugin, but this is not allowed. Choose one or the other."
                         )
                     self._layer_sync = plugin
+                    plugins_flags_types[NativeSyncBatchNorm.__name__] += 1
                 else:
                     raise MisconfigurationException(
-                        f"Found invalid type for plugin {plugin}. Expected a precision plugin or training strategy."
+                        f"Found invalid type for plugin {plugin}. Expected one of: PrecisionPlugin, "
+                        "CheckpointIO, ClusterEnviroment, LayerSync, or Strategy."
                     )
 
+            duplicated_plugin_key = [k for k, v in plugins_flags_types.items() if v > 1]
+            if duplicated_plugin_key:
+                raise MisconfigurationException(
+                    f"Received multiple values for {', '.join(duplicated_plugin_key)} flags in `plugins`."
+                    " Expected one value for each type at most."
+                )
+
         # handle the case when the user passes in a strategy instance which has an accelerator, precision,
         # checkpoint io or cluster env set up
         # TODO: @awaelchli improve the error messages below

tests/accelerators/test_accelerator_connector.py

@@ -26,13 +26,14 @@
 from pytorch_lightning.accelerators.accelerator import Accelerator
 from pytorch_lightning.accelerators.cpu import CPUAccelerator
 from pytorch_lightning.accelerators.gpu import GPUAccelerator
-from pytorch_lightning.plugins import LayerSync, NativeSyncBatchNorm, PrecisionPlugin
+from pytorch_lightning.plugins import DoublePrecisionPlugin, LayerSync, NativeSyncBatchNorm, PrecisionPlugin
 from pytorch_lightning.plugins.environments import (
     KubeflowEnvironment,
     LightningEnvironment,
     SLURMEnvironment,
     TorchElasticEnvironment,
 )
+from pytorch_lightning.plugins.io import TorchCheckpointIO
 from pytorch_lightning.strategies import (
     DataParallelStrategy,
     DDP2Strategy,
@@ -1019,3 +1020,19 @@ def __init__(self, **kwargs):
     assert strategy._layer_sync is None
     Trainer(strategy=strategy, sync_batchnorm=True)
     assert isinstance(strategy._layer_sync, NativeSyncBatchNorm)
+
+
+@pytest.mark.parametrize(
+    ["plugins", "expected"],
+    [
+        ([LightningEnvironment(), SLURMEnvironment()], "ClusterEnvironment"),
+        ([TorchCheckpointIO(), TorchCheckpointIO()], "CheckpointIO"),
+        (
+            [PrecisionPlugin(), DoublePrecisionPlugin(), LightningEnvironment(), SLURMEnvironment()],
+            "PrecisionPlugin, ClusterEnvironment",
+        ),
+    ],
+)
+def test_plugin_only_one_instance_for_one_type(plugins, expected):
+    with pytest.raises(MisconfigurationException, match=f"Received multiple values for {expected}"):
+        Trainer(plugins=plugins)