torch.fill_ can not apply after `add` function

[fukatani]

🐞Describing the bug

• torch.fill_ can not apply after add function

Maybe related to #1914 and we need more general solution.

Stack Trace

Model is not in eval mode. Consider calling '.eval()' on your model prior to conversion
Traceback (most recent call last):
  File "/Users/ryosukefukatani/work/coremltools/onth9.py", line 26, in <module>
    convert_to="neuralnetwork",
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/_converters_entry.py", line 542, in convert
    main_pipeline=pass_pipeline,
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 188, in mil_convert
    return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 217, in _mil_convert
    **kwargs
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
    prog = frontend_converter(model, **kwargs)
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 108, in __call__
    return load(*args, **kwargs)
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/frontend/torch/load.py", line 61, in load
    specification_version,
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/frontend/torch/converter.py", line 335, in __init__
    p(self.graph)
  File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/frontend/torch/torchir_passes.py", line 151, in generate_tensor_assignment_ops
    raise ValueError("No matching select or slice.")
ValueError: No matching select or slice.

To Reproduce

import torch
import coremltools as ct
import numpy as np


class Net(torch.nn.Module):
    def forward(self, x):
        y = torch.empty(x.shape).to(torch.int32) + 1
        y.fill_(0.0)
        return y


x = torch.rand(2, 3)
traced_fn = torch.jit.trace(Net(), x)
ct_model = ct.convert(
    traced_fn,
    inputs=[
        ct.TensorType(
            shape=(
                ct.RangeDim(),
                ct.RangeDim(),
            )
        ),
    ],
    source="pytorch",
    convert_to="neuralnetwork",
)

out = traced_fn(x)
out_dict = ct_model.predict(
    {
        'x': x.detach().numpy().astype(np.float32),
    }
)
np.testing.assert_allclose(out, list(out_dict.values())[0], rtol=0.001, atol=0.001)

System environment (please complete the following information):

• coremltools version: latest master

[TobyRoseman]

We probably need a more general solution of #1917.