Lightning-AI/pytorch-lightning · error · ValueError

Could not export to ONNX since neither `input_sample` nor `m

Error message

Could not export to ONNX since neither `input_sample` nor `model.example_input_array` attribute is set.

What it means

ONNX export needs a concrete input tensor to trace/script the model. If `input_sample` is not passed and the LightningModule has no `self.example_input_array` attribute set, Lightning raises ValueError because there is nothing to run the model with.

Source

Thrown at src/lightning/pytorch/core/module.py:1493

            model = SimpleModel()
            input_sample = torch.randn(1, 64)
            model.to_onnx("export.onnx", input_sample, export_params=True)

        """
        if not _ONNX_AVAILABLE:
            raise ModuleNotFoundError(f"`{type(self).__name__}.to_onnx()` requires `onnx` to be installed.")

        if kwargs.get("dynamo", False) and not _ONNXSCRIPT_AVAILABLE:
            raise ModuleNotFoundError(
                f"`{type(self).__name__}.to_onnx(dynamo=True)` requires `onnxscript` to be installed."
            )

        mode = self.training

        if input_sample is None:
            if self.example_input_array is None:
                raise ValueError(
                    "Could not export to ONNX since neither `input_sample` nor"
                    " `model.example_input_array` attribute is set."
                )
            input_sample = self.example_input_array

        input_sample = self._on_before_batch_transfer(input_sample)
        input_sample = self._apply_batch_transfer_handler(input_sample)

        file_path = str(file_path) if isinstance(file_path, Path) else file_path
        # PyTorch (2.5) declares file_path to be str | PathLike[Any] | None, but
        #               BytesIO does work, too.
        ret = torch.onnx.export(self, input_sample, file_path, **kwargs)  # type: ignore
        self.train(mode)
        return ret

    @torch.no_grad()
    def to_torchscript(
        self,

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Pass an input sample explicitly: model.to_onnx("f.onnx", torch.randn(1, *input_shape))
2. Or set self.example_input_array = torch.randn(1, *input_shape) in the model's __init__

Example fix

# before
model.to_onnx("f.onnx")
# after
model.to_onnx("f.onnx", torch.randn(1, 28, 28))
# or in __init__: self.example_input_array = torch.randn(1, 28, 28)

sample = input_sample if input_sample is not None else getattr(model, "example_input_array", None)
if sample is None:
    raise ValueError("Provide input_sample or set model.example_input_array")
model.to_onnx(path, sample)

When it happens

Trigger: Calling `model.to_onnx("f.onnx")` with no second argument on a model whose `__init__` never assigned `self.example_input_array`.

Common situations: Reusing a plain nn.Module-style LightningModule that omits example_input_array, or refactorings that removed the attribute.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

• `{type(self).__name__}.to_onnx()` requires `onnx` to be inst
• `{type(self).__name__}.to_onnx(dynamo=True)` requires `onnxs
• Choosing method=`trace` requires either `example_inputs` or
• `{type(self).__name__}.to_tensorrt` requires `torch_tensorrt
• Could not export to TensorRT since neither `input_sample` no

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/af2e42ca8daf3fc2. Report an issue: GitHub.