[ux] Mix of nn.Sequential and nn.ModuleDict?

Often modules have structure like this (so that state_dict() key names are readable):

class MyModule(nn.Module):
  def __init__(self):
    self.module1 = nn.Linear(1, 4)
    self.act1 = nn.ReLU()
    self.module1 = nn.Linear(4, 8)
    self.act2 = nn.ReLU()

  def forward(self, x):
    x = self.module1(x)
    x = self.act1(x)
    x = self.module2(x)
    x = self.act2(x)
    return x

Such forward method could go away if we could have some mix of nn.Sequential and nn.ModuleDict, i.e. having a forward method for ModuleDict that would rely on the OrderedDict and implement forward as a Sequential, and still enjoy good module names.

Currently it errors out with a unclear error message (it should rather cry “ModuleDict forward is not supported by default”):

>>> torch.nn.ModuleDict(dict(a = torch.nn.Linear(2, 2), b = torch.nn.Linear(2, 4)))(torch.rand(3, 2))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/miniconda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
TypeError: forward() takes 1 positional argument but 2 were given

I think starting Python 3.7 (?) insertion order of dictionaries is preserved, and kwarg order is also preserved since https://www.python.org/dev/peps/pep-0468/

This would also simplify static analysis and graph transformations of such models

Would it work to pass the OrderedDict directly to nn.Sequential as described in the docs?

# Example of using Sequential with OrderedDict
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())

Would nn.Sequential accept a regular dict as well? (given that insertion order is preserved in modern Pythons)

You can find the feature request here.