Update run_clm_flax.py from single TPU worker to multiple TPU workers
Author: congyingxia
Pull request: yes

Current code only works on 1 TPU worker. If there's multiple TPU workers, the data need to be split into multiple workers first then shard to local devices. The same issue for T5 language modeling with flax: https://github.com/google/flax/discussions/2017

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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_18658). All of your documentation changes will be reflected on that endpoint.

WDYT @sanchit-gandhi?

Hey @congyingxia and sorry for the delay! 

We try to keep these examples as simple as possible. In that spirit, we have limited the example scripts to single host training (v3-8). Unfortunately, scaling up to multi-host training is non-trivial: it requires another driver VM to keep the TPU hosts synced and execute commands across TPUs in parallel. For this reason, we have currently omitted multi-host training/inference, and instead focussed on single host training. If you're interested in running training/inference on a pod, I would suggest looking at the repo https://github.com/huggingface/bloom-jax-inference, which details how inference for LLM's can be scaled up to an arbitrary number of TPU devices with MP + DP.

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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