Make shuffling optional in DistributedSampler (#22479)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22479
In some cases, for example, when we training on CTR data, we would like to start training from old samples and finish on new recent samples.
This diff add the option to disable the shuffling in DistributedSampler to accommodate this use case.
Reviewed By: soumith
Differential Revision: D16100388
fbshipit-source-id: 35566581f5250040b2db5ec408a63037b47a9f5d
