can predict_with_generate (do_eval) work with sharded_ddp fairscale in 4.6.1+?
In 4.5.0, sharded_ddp won't work with  predict_with_generate in seq2seq or clm model training during the eval step. Wonder whether it can work in the latest version. 
Can I ask whether we have any progress on the model prediction (text generation) during the training with fairscale? Thanks/ cc @stas00 <|||||>most likely you'd want to ask @sgugger as I he did the fairscale integration. 

you can ask me about the deepspeed integration if you try that instead.<|||||>`sharded_ddp` does not work for evaluation, as is mentioned in the documentation. I have mentioned that on the fairscale repository but did not get any update for the authors (same for the blocking aprt of Zero offload via fairscale) so I suggest you use DeepSpeed instead, where we have a much better support from the team at Microsoft.<|||||>and if fairscale solves the problem on their side and the work resumes in this direction, the key to making generate work might have to include the enabling `synced_gpus` here for fairscale (for zero3-like fairscale features that is):

https://github.com/huggingface/transformers/blob/fb65f65ea6175036f0cc8318145853e9c833f914/src/transformers/trainer_seq2seq.py#L164<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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