[Bug] `loadPyTorchDump` does not load in models with sequential layers correctly

[ShreyasKhandekar]

The model.loadPyTorchDump() function does not handle nested module within Sequential layers, and is unable to track the layer names correctly to import the files.

Ex: If we have a model from pytorch:

class Dummy(nn.Module):
    def __init__(self):
        super(Dummy, self).__init__()

        self.model = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
        )

and we dump it using chai_dump

dummy = Dummy()
dummy.chai_dump('models/dummy', 'dummy')

The files we get are:

models/dummy
| - model.0.weight.chdata
| - model.0.weight.json
| - specification.json

And the loadPyTorchDump function does not correctly handle these file names since it does not keep track of the "nested" nature of the Conv2d layer since it's inside of the Sequential layer.

A more complicated example that expands on the above:

class DummyTwo(nn.Module):
    def __init__(self, input_model):
        super(DummyTwo, self).__init__()
        self.model = nn.Sequential(
            input_model,
        )

dummy_two = DummyTwo(dummy)
dummy_two.chai_dump('models/dummy_two', 'dummy_two')

Gives us the files:

models/dummy_two
| - model.0.model.0.weight.chdata
| - model.0.model.0.weight.json
| - specification.json

Again, the loadPyTorchDump cannot handle this nested nature of models.

[Iainmon]

I think the loading in feature should be rebuild, along with a discussion about how to engineer the module system to be more like PyTorch. I didn't have enough time to fix this during the internship.

[ShreyasKhandekar]

I have a fix for this that does not re-engineer to module system, but just works with it to support loading nested modules.