Lightning-AI/pytorch-lightning · error · ValueError

Received both `precision={precision_input}` and `plugins={se

Error message

Received both `precision={precision_input}` and `plugins={self._precision_instance}`. Choose one.

What it means

Fabric rejects setting precision through two channels at once. If a Precision plugin is supplied via `plugins` AND a `precision=` string argument is also given, the connector cannot decide which wins and raises this error.

Source

Thrown at src/lightning/fabric/connector.py:253

                    plugins_flags_types[CheckpointIO.__name__] += 1
                elif isinstance(plugin, ClusterEnvironment):
                    self._cluster_environment_flag = plugin
                    plugins_flags_types[ClusterEnvironment.__name__] += 1
                else:
                    raise TypeError(
                        f"Found invalid type for plugin {plugin}. Expected one of: Precision, "
                        "CheckpointIO, ClusterEnvironment."
                    )

            duplicated_plugin_key = [k for k, v in plugins_flags_types.items() if v > 1]
            if duplicated_plugin_key:
                raise ValueError(
                    f"Received multiple values for {', '.join(duplicated_plugin_key)} flags in `plugins`."
                    " Expected one value for each type at most."
                )

            if plugins_flags_types.get(Precision.__name__) and precision_input is not None:
                raise ValueError(
                    f"Received both `precision={precision_input}` and `plugins={self._precision_instance}`. Choose one."
                )

        self._precision_input = "32-true" if precision_input is None else precision_input

        # handle the case when the user passes in a strategy instance which has an accelerator, precision,
        # checkpoint io or cluster env set up
        # TODO: improve the error messages below
        if isinstance(self._strategy_flag, Strategy):
            if self._strategy_flag._accelerator:
                if self._accelerator_flag != "auto":
                    raise ValueError("accelerator set through both strategy class and accelerator flag, choose one")
                self._accelerator_flag = self._strategy_flag._accelerator
            if self._strategy_flag._precision:
                # [RFC] handle precision plugin set up conflict?
                if self._precision_instance:
                    raise ValueError("precision set through both strategy class and plugins, choose one")
                self._precision_instance = self._strategy_flag._precision

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Remove the `precision=` argument and let the Precision plugin define precision
2. Or remove the Precision plugin from `plugins` and keep only `precision="..."`

Example fix

# before
fabric = Fabric(precision="16-mixed", plugins=[MixedPrecision("16-mixed")])

# after
fabric = Fabric(plugins=[MixedPrecision("16-mixed")])

from lightning.fabric.plugins import Precision

has_precision_plugin = any(isinstance(p, Precision) for p in plugins)
if has_precision_plugin and precision is not None:
    raise SystemExit("Set either precision= or a Precision plugin, not both")

When it happens

Trigger: Fabric(precision="16-mixed", plugins=[MixedPrecision("16-mixed")]) or Fabric(precision="bf16-true", plugins=[DeepSpeedPrecision(...)]) — any non-None `precision` combined with a plugins entry that registers under Precision.__name__.

Common situations: Adding a DeepSpeed or MixedPrecision plugin to an existing Fabric(precision="...") call without removing the precision flag; porting Trainer code where both were tolerated; copy-pasting plugin setup on top of an old config.

Related errors

• precision set through both strategy class and plugins, choos
• checkpoint_io set through both strategy class and plugins, c
• cluster_environment set through both strategy class and plug
• Received multiple values for {', '.join(duplicated_plugin_ke
• accelerator set through both strategy class and accelerator

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/662209ede2b930be. Report an issue: GitHub.