Hello,
My forward call returns a dictionary, out_dict, as follows:
out_dict = {'main_predict_op': main_differentiable_op,
            'secondary_predict_op': second_differentiable_op}

It seems DDP does not like this and throws me the following warning:
RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`; (2) making sure all `forward` function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).

I have set find_unused_parameters=True. From what I understand, output tensor being encapsulated in a dictionary is not favorable for DDP.
How would I go about this?