configure_model with deepspeed_stage3 goes wrong

Nobody has claimed this yet.

### Bug description

I tried to train huggingface transformers model with deepspeed_stage3,
but when I load model with checkpoint like the code below, error occurs.

I think checkpoint and model template are totally fine, because it worked okay if I configure model in __init__.
(And it might goes wrong only with multi-GPU env?)

### What version are you seeing the problem on?

v2.1

### How to reproduce the bug

```python
def configure_model(self):
self.model = SOME_MODEL.from_pretrained(some_path, config=some_config)
```

### Error messages and logs

```
RuntimeError: Error(s) in loading state_dict for GPTJForCausalLM:
size mismatch for transformer.wte.weight: copying a param with shape torch.Size([32768, 3072]) from checkpoint, the shape in current model is torch.Size([0]).
size mismatch for transformer.h.0.ln_1.weight: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([0]).
...
```

### Environment

Current environment

```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):
```

### More info

_No response_

cc @borda

Start with the configure_model path and the SOME_MODEL.from_pretrained call, then reproduce the checkpoint load with deepspeed_stage3 in a multi-GPU environment and compare it with configuration in __init__. Done means the checkpoint loads without zero-sized parameter mismatches; the issue names no repository files or tests to run.

Written by the indexing model from the issue text.