higher for dpt architectures?

[Ainaz99]

Hi,

Thanks for the great library!
Does higher support a dpt-based module passed to the higher.innerloop_ctx?
I'm getting the following error:

  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/contextlib.py", line 113, in __enter__
    return next(self.gen)
  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/site-packages/higher/__init__.py", line 85, in innerloop_ctx
    fmodel = monkeypatch(
  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/site-packages/higher/patch.py", line 542, in monkeypatch
    fmodule = make_functional(module, encapsulator=encapsulator)
  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/site-packages/higher/patch.py", line 435, in make_functional
    _, fmodule, MonkeyPatched = _make_functional(module, params_box, 0)
  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/site-packages/higher/patch.py", line 348, in _make_functional
    child_params_offset, fchild, _ = _make_functional(
  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/site-packages/higher/patch.py", line 218, in _make_functional
    class MonkeyPatched(_ModuleType, _MonkeyPatchBase):  # type: ignore
  File "/home/rbachman/miniconda/envs/py38/lib/python3.8/abc.py", line 85, in __new__
    cls = super().__new__(mcls, name, bases, namespace, **kwargs)
TypeError: Cannot create a consistent method resolution
order (MRO) for bases Module, _MonkeyPatchBase

Thank you for your response!

[HamedHemati]

Hi!
I don't know what a DPT architecture is, but if it contains any sort of RNN-based module inside, you may also need to use the context manager below:

with torch.backends.cudnn.flags(enabled=False):
    # Your meta-learning code here...

Have a look at the Known/Possible Issues section on the readme page regarding potential issues:
https://github.com/facebookresearch/higher#knownpossible-issues

[HamedHemati]

@Ainaz99

Here is a follow-up to my previous comment. Today, I also got the same error for a simple MLP model that would inherit from multiple classes :

class MyModel(nn.Module, AnotherClass):
       def __init__(self):
              ...

I re-implemented the model as a class with single inheritance and added the methods from the second base (super) class directly to my new model:

class MyModel(nn.Module):
       def __init__(self):
              ...
      + def methods_from_AnotherClass:
             ...

This solved the issue for me. Let me know if this was also the case for you.

** This problem is probably triggered by the similar inheritances happening inside those different base classes when calling higher.patch.monkeypatch(model,...) .