load_in_4bit seems dosen't work as experted which actually increase GPU memory usage when using zero3 via accelerate
### System Info

transformers==4.31.0
deepspeed==0.9.2
peft==0.4.0
bitsandbytes==0.39.0
torch==1.13.0
CUDA Version 11.8
GPUS 8x A100 80gb

### Who can help?

@pacman100

### Information

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

### Tasks

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

### Reproduction

I compared the from_pretrained method w/o load_in_4bit passed in and I found out that after passing load_in_4bit in from_pretrained method I can't load model with same hardware and same accelerate config.

accelerate config:
ds_zero3.yaml
```
compute_environment: LOCAL_MACHINE
deepspeed_config:
  gradient_accumulation_steps: 1
  gradient_clipping: 1.0
  offload_optimizer_device: 'none'
  offload_param_device: 'cpu'
  zero3_init_flag: true
  zero3_save_16bit_model: true
  zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```

load in normal way:
test.py
```import torch
from accelerate import Accelerator

from transformers import (
    AutoModelForCausalLM,
)

def main():
    accelerator = Accelerator()
    model_name_or_path = "local path of sambanovasystems/BLOOMChat-176B-v1"

    model = AutoModelForCausalLM.from_pretrained(
        model_name_or_path,
        trust_remote_code=True)

if __name__ == "__main__":
    main()
```
run command:
```
accelerate launch --config_file ds_zero3.yaml test.py
```
it works just fine, 
GPU memory usage: 22GB x 8 
peak CPU memory usage: ~500GB

then, I try to use load_in_4bit=True
test_4bit.py
```import torch
from accelerate import Accelerator

from transformers import (
    AutoModelForCausalLM,
)

def main():
    accelerator = Accelerator()
    model_name_or_path = "local path of sambanovasystems/BLOOMChat-176B-v1"

    model = AutoModelForCausalLM.from_pretrained(
        model_name_or_path,
        trust_remote_code=True,
        load_in_4bit=True)

if __name__ == "__main__":
    main()
```
run command:
```
accelerate launch --config_file ds_zero3.yaml test_4bit.py
```
OOM error, it seems nether zero3 nor parameters offlaod working as experted. peak cpu usage ~500GB in this case.

also tried 
```
quantization_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)
```
OOM

### Expected behavior

I am trying to finetune bloomchat176b using qlora and qlora should using less hardware resource. Thanks for help
Hello, QLoRA and DeepSpeed can't be used together. DeepSpeed doesn't work with quantized parameters.<|||||>> Hello, QLoRA and DeepSpeed can't be used together. DeepSpeed doesn't work with quantized parameters.

is that means I can't do zero optimization while using qlora, at least for now?  ddp is the only parallelism method compatible with qlora?<|||||>Hello, yes<|||||>cc @younesbelkada for adding more context just in case<|||||>Hi there!
Indeed 4bit + 8bit is not supported with DS, let's maybe add that check on accelerate.preprare (I can work on that)<|||||>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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