ReflectionPad3D fails at runtime

[metascroy]

🐞Describing the bug

torch.ops.aten.pad.default fails at runtime when in reflect mode, but ideally this would happen ahead of time.

Stack Trace

/opt/miniconda3/envs/op-et/lib/python3.10/site-packages/coremltools/models/model.py:560: RuntimeWarning: You will not be able to run predict() on this Core ML model. Underlying exception message was: Error compiling model: "Failed to parse the model specification. Error: Unable to parse ML Program: in operation pad_cast_fp16: Padding for more than two dimensions only supports `constant` mode".
  _warnings.warn(
Traceback (most recent call last):
  File "/Users/scroy/Desktop/executorch/test.py", line 160, in <module>
    out = mlmodel.predict(predict_inputs)
  File "/opt/miniconda3/envs/op-et/lib/python3.10/site-packages/coremltools/models/model.py", line 804, in predict
    raise self._framework_error
  File "/opt/miniconda3/envs/op-et/lib/python3.10/site-packages/coremltools/models/model.py", line 549, in _get_proxy_and_spec
    _MLModelProxy(
RuntimeError: Error compiling model: "Failed to parse the model specification. Error: Unable to parse ML Program: in operation pad_cast_fp16: Padding for more than two dimensions only supports `constant` mode".

To Reproduce

import torch

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.pad = torch.nn.ReflectionPad3d(padding=2)
        
    def forward(self, x):
        return self.pad(x)

model = Model()
inputs = (
    torch.randn(1, 6, 6, 6, 6),
)

eager_outputs = model(*inputs)
#print(f"Eager: {eager_outputs.shape} {eager_outputs}")

ep = torch.export.export(model.eval(), inputs)
print(ep)


import coremltools as ct
import numpy as np
ep = ep.run_decompositions({})

eager_outputs = model(*inputs)

mlmodel = ct.convert(ep)

coreml_inputs = mlmodel.get_spec().description.input
coreml_outputs = mlmodel.get_spec().description.output
predict_inputs = {str(ct_in.name): pt_in.detach().cpu().numpy().astype(np.int32) for ct_in, pt_in in zip(coreml_inputs, inputs)}
out = mlmodel.predict(predict_inputs)

print("Eager", eager_outputs)
print("CoremL", out)

System environment (please complete the following information):

• coremltools version: 8.3
• OS (e.g. MacOS version or Linux type): macOS15