Hey @aashaka
Below is the implementation of the DataParallel.forward method. It basically calls replicas[i].forward(inputs[i], ...). So during execution, the self variable in the forward function is the replica. Hence, you can use self._former_parameters to access the field in forward function.

  
      github.com
  
  
    pytorch/pytorch/blob/c3466dabaae9328b207804afb043b7b519f64825/torch/nn/parallel/data_parallel.py#L147-L162 1

def forward(self, *inputs, **kwargs):
    if not self.device_ids:
        return self.module(*inputs, **kwargs)

    for t in chain(self.module.parameters(), self.module.buffers()):
        if t.device != self.src_device_obj:
            raise RuntimeError("module must have its parameters and buffers "
                               "on device {} (device_ids[0]) but found one of "
                               "them on device: {}".format(self.src_device_obj, t.device))

    inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
    if len(self.device_ids) == 1:
        return self.module(*inputs[0], **kwargs[0])
    replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
    outputs = self.parallel_apply(replicas, inputs, kwargs)
    return self.gather(outputs, self.output_device)