Support `call_method` in DDPOptimizer (#121771)
This PR fixes Issue #111279.
While #111279 reported the issue with `MultiheadAttention`, a minimal reproduction would be:
```python
class ToyModel(nn.Module):
def __init__(self,):
super().__init__()
self.linear = nn.Linear(128, 10)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.linear.forward(x) # Error
# return self.linear(x) # OK
```
Dynamo treats `self.linear(x)` as `call_module` while treating `self.linear.forward(x)` as a [`get_attr` and a `call_method`](https://github.com/pytorch/pytorch/blob/main/torch/_dynamo/variables/nn_module.py#L358-L378). However, existing DDPOptimizer assumes, for a `get_attr` node, `getattr(gm, node.target)` gives a tensor with the `requires_grad` attribute. Existing DDPOptimizer also does not support `call_method` nodes.
This PR adds support for `call_method` and check on `get_attr`. It also checks if a module's parameters have been added to a bucket to support multiple method calls from the same module.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121771
Approved by: https://github.com/yf225
