TransformerEnginePrecision _convert_layers(module) fails for FSDP zero2/zero3

Bug description

TransformerEnginePrecision.convert_module function seems to not work for the the FSDP-wrapped model.

What version are you seeing the problem on?

master

How to reproduce the bug

model = FSDP(
    model,
    sharding_strategy=sharding_strategy,
    auto_wrap_policy=custom_wrap_policy,
    device_id=local_rank,
    use_orig_params=True,
    device_mesh=mesh,
)
te_precision = TransformerEnginePrecision(weights_dtype=torch.bfloat16, replace_layers=True)
self.model = te_precision.convert_module(self.model)

Error messages and logs

[rank1]:     self.model = te_precision.convert_module(self.model)
[rank1]:     _convert_layers(module)
[rank1]:   File "/usr/local/lib/python3.10/dist-packages/lightning/fabric/plugins/precision/transformer_engine.py", line 165, in _convert_layers
[rank1]:     replacement.weight.data = child.weight.data.clone()
[rank1]: RuntimeError: Attempted to call `variable.set_data(tensor)`, but `variable` and `tensor` have incompatible tensor type.

More info

I actually see it for pytorch-lightning==2.3.0

cc @lantiga

Start with lightning/fabric/plugins/precision/transformer_engine.py, especially _convert_layers and TransformerEnginePrecision.convert_module. Reproduce the failure using an FSDP-wrapped model with zero2 or zero3 sharding and the shown precision settings. Done means conversion completes without the incompatible tensor type error for the reported FSDP configurations.