Lightning-AI/pytorch-lightning · error · ValueError

`model` must either be an instance of OptimizedModule or Lig

Error message

`model` must either be an instance of OptimizedModule or LightningModule

What it means

to_uncompiled only accepts torch._dynamo.OptimizedModule or LightningModule instances; any other type (plain nn.Module, numpy object, etc.) raises ValueError.

Source

Thrown at src/lightning/pytorch/utilities/compile.py:91

    Note: this method will in-place modify the ``LightningModule`` that is passed in.

    """
    if isinstance(model, OptimizedModule):
        original = model._orig_mod
        if not isinstance(original, pl.LightningModule):
            raise TypeError(
                f"Unexpected error, the wrapped model should be a LightningModule, found {type(model).__name__}"
            )

    elif isinstance(model, pl.LightningModule):
        if model._compiler_ctx is None:
            raise ValueError(
                "`model` is required to be a compiled LightningModule. Found a non-compiled LightningModule instead."
            )
        original = model

    else:
        raise ValueError("`model` must either be an instance of OptimizedModule or LightningModule")

    ctx = original._compiler_ctx
    if ctx is not None:
        original.forward = ctx["original_forward"]  # type: ignore[method-assign]
        original.training_step = ctx["original_training_step"]  # type: ignore[method-assign]
        original.validation_step = ctx["original_validation_step"]  # type: ignore[method-assign]
        original.test_step = ctx["original_test_step"]  # type: ignore[method-assign]
        original.predict_step = ctx["original_predict_step"]  # type: ignore[method-assign]
        original._compiler_ctx = None

    return original


def _maybe_unwrap_optimized(model: object) -> "pl.LightningModule":
    if isinstance(model, OptimizedModule):
        return from_compiled(model)
    if isinstance(model, pl.LightningModule):
        return model

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Pass the LightningModule you gave the Trainer
2. If you have an OptimizedModule from torch.compile, pass that instead
3. Add an isinstance check before calling

Example fix

# before
unwrapped = _module_to_compiled.to_uncompiled(my_sequential)
# after
unwrapped = _module_to_compiled.to_uncompiled(model)  # a LightningModule or OptimizedModule

def is_uncompilable(m) -> bool:
    from torch._dynamo import OptimizedModule
    import lightning.pytorch as pl
    return isinstance(m, (OptimizedModule, pl.LightningModule))

When it happens

Trigger: Passing an nn.Sequential, a raw tensor-wrapping object, or arbitrary class to _module_to_compiled.to_uncompiled.

Common situations: Sending helper modules or strategies' wrapped models that are neither type.

Related errors

• `model` is required to be a `OptimizedModule`. Found a `{typ
• `model` is expected to be a compiled LightningModule. Found
• `model` must be a `LightningModule` or `torch._dynamo.Optimi
• `devices` selected with `CPUAccelerator` should be an int >
• Blocking backward sync is only possible if the module passed

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/e2cadeef51a1e580. Report an issue: GitHub.