Export PyTorch model with custom ONNX operators

This document explains the process of exporting PyTorch models with custom ONNX Runtime ops. The aim is to export a PyTorch model with operators that are not supported in ONNX, and extend ONNX Runtime to support these custom ops.


Export Built-In Contrib Ops

“Contrib ops” refers to the set of custom ops that are built in to most ORT packages. Symbolic functions for all contrib ops should be defined in pytorch_export_contrib_ops.py.

To export using those contrib ops, call pytorch_export_contrib_ops.register() before calling torch.onnx.export(). For example:

from onnxruntime.tools import pytorch_export_contrib_ops
import torch


Export a Custom Op

To export a custom op that’s not a contrib op, or that’s not already included in pytorch_export_contrib_ops, one will need to write and register a custom op symbolic function.

We take the Inverse operator as an example:

from torch.onnx import register_custom_op_symbolic

def my_inverse(g, self):
    return g.op("com.microsoft::Inverse", self)

# register_custom_op_symbolic('<namespace>::inverse', my_inverse, <opset_version>)
register_custom_op_symbolic('::inverse', my_inverse, 1)

<namespace> is a part of the torch operator name. For standard torch operators, namespace can be omitted.

com.microsoft should be used as the custom opset domain for ONNX Runtime ops. You can choose the custom opset version during op registration.

For more on writing a symbolic function, see the torch.onnx documentation.

Extend ONNX Runtime with Custom Ops

The next step is to add an op schema and kernel implementation in ONNX Runtime. See custom operators for details.

Test End-to-End: Export and Run

Once the custom op is registered in the exporter and implemented in ONNX Runtime, you should be able to export it and run it with ONNX Runtime.

Below you can find a sample script for exporting and running the inverse operator as part of a model.

The exported model includes a combination of ONNX standard ops and the custom ops.

This test also compares the output of PyTorch model with ONNX Runtime outputs to test both the operator export and implementation.

import io
import numpy
import onnxruntime
import torch

class CustomInverse(torch.nn.Module):
    def forward(self, x):
        return torch.inverse(x) + x

x = torch.randn(3, 3)

# Export model to ONNX
f = io.BytesIO()
torch.onnx.export(CustomInverse(), (x,), f)

model = CustomInverse()
pt_outputs = model(x)

# Run the exported model with ONNX Runtime
ort_sess = onnxruntime.InferenceSession(f.getvalue())
ort_inputs = dict((ort_sess.get_inputs()[i].name, input.cpu().numpy()) for i, input in enumerate((x,)))
ort_outputs = ort_sess.run(None, ort_inputs)

# Validate PyTorch and ONNX Runtime results
numpy.testing.assert_allclose(pt_outputs.cpu().numpy(), ort_outputs[0], rtol=1e-03, atol=1e-05)

By default, the opset version will be set to 1 for custom opsets. If you’d like to export your custom op to a higher opset version, you can specify the custom opset domain and version using the custom_opsets argument when calling the export API. Note that this is different than the opset version associated with default ONNX domain.

torch.onnx.export(CustomInverse(), (x,), f, custom_opsets={"com.microsoft": 5})

Note that you can export a custom op to any version >= the opset version used at registration.