support setattr of arbitrary user provided types in tracing
Labels: triaged, bug, oncall: pt2

### 🐛 Describe the bug

Dynamo already support patching nn.Module attribute outside of forward call (e.g. during model initialization): https://github.com/pytorch/pytorch/pull/91018 . But some use cases (e.g. detectrons's RCNN model) need patch nn.Module attribute in forward method ( fb internal link: https://fburl.com/code/vvekrxl6 ). Dynamo does not support this right now.



### Error logs

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/shunting/cpython/build/install/lib/python3.9/importlib/__init__.py", line 169, in reload
    _bootstrap._exec(spec, module)
  File "<frozen importlib._bootstrap>", line 613, in _exec
  File "<frozen importlib._bootstrap_external>", line 790, in exec_module
  File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
  File "/home/shunting/learn/misc.py", line 20, in <module>
    gm, guards = dynamo.export(MyModule(), *inputs, aten_graph=True, tracing_mode="symbolic")
  File "/home/shunting/pytorch/torch/_dynamo/eval_frame.py", line 616, in export
    result_traced = opt_f(*args, **kwargs)
  File "/home/shunting/pytorch/torch/nn/modules/module.py", line 1482, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/shunting/pytorch/torch/_dynamo/eval_frame.py", line 82, in forward
    return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs)
  File "/home/shunting/pytorch/torch/_dynamo/eval_frame.py", line 211, in _fn
    return fn(*args, **kwargs)
  File "/home/shunting/pytorch/torch/_dynamo/eval_frame.py", line 332, in catch_errors
    return callback(frame, cache_size, hooks)
  File "/home/shunting/pytorch/torch/_dynamo/convert_frame.py", line 103, in _fn
    return fn(*args, **kwargs)
  File "/home/shunting/pytorch/torch/_dynamo/utils.py", line 90, in time_wrapper
    r = func(*args, **kwargs)
  File "/home/shunting/pytorch/torch/_dynamo/convert_frame.py", line 339, in _convert_frame_assert
    return _compile(
  File "/home/shunting/pytorch/torch/_dynamo/convert_frame.py", line 398, in _compile
    out_code = transform_code_object(code, transform)
  File "/home/shunting/pytorch/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
    transformations(instructions, code_options)
  File "/home/shunting/pytorch/torch/_dynamo/convert_frame.py", line 385, in transform
    tracer.run()
  File "/home/shunting/pytorch/torch/_dynamo/symbolic_convert.py", line 1686, in run
    super().run()
  File "/home/shunting/pytorch/torch/_dynamo/symbolic_convert.py", line 537, in run
    and self.step()
  File "/home/shunting/pytorch/torch/_dynamo/symbolic_convert.py", line 500, in step
    getattr(self, inst.opname)(inst)
  File "/home/shunting/pytorch/torch/_dynamo/symbolic_convert.py", line 1048, in STORE_ATTR
    BuiltinVariable(setattr)
  File "/home/shunting/pytorch/torch/_dynamo/variables/builtin.py", line 375, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/shunting/pytorch/torch/_dynamo/variables/base.py", line 230, in call_function
    unimplemented(f"call_function {self} {args} {kwargs}")
  File "/home/shunting/pytorch/torch/_dynamo/exc.py", line 67, in unimplemented
    raise Unsupported(msg)
torch._dynamo.exc.Unsupported: call_function BuiltinVariable(setattr) [UserDefinedClassVariable(), ConstantVariable(str), GetAttrVariable(UserDefinedClassVariable(), run_cos)] {}

from user code:
   File "/home/shunting/learn/misc.py", line 10, in forward
    MyModule.run = MyModule.run_cos

Set torch._dynamo.config.verbose=True for more information


You can suppress this exception and fall back to eager by setting:
    torch._dynamo.config.suppress_errors = True

### Minified repro

```
import torch
from torch import nn
import torch._dynamo as dynamo

class MyModule(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        MyModule.run = MyModule.run_cos
        return self.run(x)

    def run(self, x):
        return torch.sin(x)

    def run_cos(self, x):
        return torch.cos(x)

inputs = [torch.rand(5)]
gm, guards = dynamo.export(MyModule(), *inputs, aten_graph=True, tracing_mode="symbolic")
print(f"Graph is {gm.graph}")
```

cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh