Error during evaluation using deepspeed zero stage 2
### System Info

transformers v4.30.0
python 3.8

Training using `deepspeed stage zero 2` hit an error when in evaluation/prediction loop. Both prediction/evaluation initiate [deepspeed with inference=True] (https://github.com/huggingface/transformers/blob/6793f0cfe0006d7cedfb9b6081f55d9d38eae18a/src/transformers/trainer.py#L3045) and hence now can't run inference for anything other than stage 3 (inference not supported for zero 1/2). 

So my question is how to run deepspeed zero 2? My code is [here](https://github.com/explodinggradients/Funtuner/blob/main/funtuner/trainer.py)

Error stack
`Traceback (most recent call last):
  File "funtuner/trainer.py", line 98, in train
    trainer.train()
  File "/nfshome/store03/users/c.scmse/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1645, in train
    return inner_training_loop(
  File "/nfshome/store03/users/c.scmse/venv/lib/python3.8/site-packages/transformers/trainer.py", line 2011, in _inner_training_loop
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/nfshome/store03/users/c.scmse/venv/lib/python3.8/site-packages/transformers/trainer.py", line 2312, in _maybe_log_save_evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
  File "/nfshome/store03/users/c.scmse/venv/lib/python3.8/site-packages/transformers/trainer.py", line 3043, in evaluate
    output = eval_loop(
  File "/nfshome/store03/users/c.scmse/venv/lib/python3.8/site-packages/transformers/trainer.py", line 3769, in prediction_loop
    _, _ = deepspeed_init(self, num_training_steps=0, inference=True)
  File "/nfshome/store03/users/c.scmse/venv/lib/python3.8/site-packages/transformers/deepspeed.py", line 351, in deepspeed_init
    raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config")
ValueError: ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config`



### Who can help?

@pacman100 

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

My code is [here](https://github.com/explodinggradients/Funtuner/blob/main/funtuner/trainer.py)
Run `python3 funtuner/trainer.py`

### Expected behavior

Run evaluation loop without any error using deepspeed stage 1 and 2. 
Hello, could you try the latest release and let us know if that resolves the issues?<|||||>Getting `ModuleNotFoundError: No module named 'funtuner'` when trying to run `python3 funtuner/trainer.py`<|||||>Hi @pacman100 , can you add the PYTHONPATH and try again? 
` export PYTHONPATH="${PYTHONPATH}:/your-path/Funtuner" `
Also checkout the `dev-train` branch.  The issue remains the same with the latest version. I tried that. <|||||>Also, on how many GPUs are you running this? 
<|||||>V 100 16GB - 1. <|||||>with one GPU, there won't be any sharing of the optim states and gradients, therefore it will be same as DDP. So a bit confused there
<|||||>Also, getting various issues when running with 2 GPUs:

main-branch
```
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```

dev-train branch
```
Traceback (most recent call last):
  File "/home/sourab/Funtuner/funtuner/trainer.py", line 28, in train
    os.mkdir(cfg.log_dir)
FileNotFoundError: [Errno 2] No such file or directory: '/scratch/c.scmse/Funtuner-logs'
```

<|||||>The main branch is not updated, please stick to dev-train for now. For fixing this error, please change the `log_dir` to your folder [here](https://github.com/explodinggradients/Funtuner/blob/c4e66209d5ee276a7eb8caf582435f1eaafbf18f/funtuner/config/config.yaml#L4) also you might want to set `log_wandb=False` 
I have run this branch on single and multi GPU settings. Although now I use only single GPU for redpajama-3B model. <|||||>> with one GPU, there won't be any sharing of the optim states and gradients, therefore it will be same as DDP. So a bit confused there

I think in single GPU + Deepspeed zero 2 I can benefit from zero offloading and smart GPU mem management allowing me to fit larger models/batch sizes. <|||||>above PR should resolve the DS issue<|||||>I'll try it out one merged. 