[BUG] Zero3: Gather the params for inference(huggingface_language_model.generate) in the end of 1 epoch and re-partition it for next epoch training

Describe the bug
Hi, I use zero-3 for MLLM training. After one-epoch training stage, I want to evaluate this model(using model.generate()). However, params of the model are located on multi-gpu, lacking of gather.

If not gathering params, during evaluation(generation), error will be raised because the forward process like:

image_embeds += self.pos_embed

RuntimeError: The size of tensor a (1152) must match the size of tensor b (0) at non-singleton dimension 2

How can I gather params on every gpu for paralized evaluation(inference/generation), liking using deepspeed.zero.GatheredParameters? And after evaluation, how can I shard the model parameters again for next training epoch?

Thanks for your reply!

To Reproduce
Steps to reproduce the behavior:

1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error

Expected behavior
A clear and concise description of what you expected to happen.

ds_report output
Please run ds_report to give us details about your setup.

Screenshots
If applicable, add screenshots to help explain your problem.

System info (please complete the following information):

• OS: [e.g. Ubuntu 18.04]
• GPU count and types [e.g. two machines with x8 A100s each]
• Interconnects (if applicable) [e.g., two machines connected with 100 Gbps IB]
• Python version
• Any other relevant info about your setup

Launcher context
Are you launching your experiment with the deepspeed launcher, MPI, or something else?

Docker context
Are you using a specific docker image that you can share?

Additional context
Add any other context about the problem here.

Start by tracing DeepSpeed ZeRO-3 parameter handling around model.generate() and the deepspeed.zero.GatheredParameters usage mentioned in the report. Reproduce the multi-GPU evaluation failure if possible, then establish a supported gather-before-generation and re-shard-after-evaluation workflow for the next training epoch.