int8 with device_map doesn't work well in generation
**Versions**
`transformers==4.26.0`
`accelerate==0.17.1`

I was trying to do inference with the `.generate()` method using a 7B OPT model. When using fp16 in an 8-gpu(80G A100) node, I figure out that fitting each whole model into a GPU with independent process achieves the best speed. But when trying to use int8, I can't use `auto` in device_map(it will shard model into different gpus, which I don't want) and I'll have to design a device_map like this:
`{
  "model.decoder.embed_tokens": 1, 
  "lm_head": 1,
  "model.decoder.embed_positions": 1,
  "model.decoder.final_layer_norm": 1,
  "model.decoder.layers.0": 1,
  "model.decoder.layers.1": 1,
  "model.decoder.layers.2": 1,
  "model.decoder.layers.3": 1,
  "model.decoder.layers.4": 1,
  "model.decoder.layers.5": 1,
   ...
}`
I found the speed is rather slow compared to fp16 in generation. 
How can I correctly use int8 in a multi-process way(one independent process for a GPU) in an 8-card node?
cc @younesbelkada Also if you want your whole model on one GPU, you shouldn't use the `device_map` argument but just place your model on that GPU.<|||||>@fnshwi you can run the following: 
```python
import torch

...
device_map = {"":torch.cuda.current_device()}
```

and use this device map instead of `"auto"`. the `"":device` means that you want to fit your entire model on that device
Related and similar issue: https://github.com/huggingface/transformers/issues/21736

Also if you run multiple processes, you can use a trick that [we use in `trl`](https://github.com/lvwerra/trl/blob/main/trl/models/modeling_base.py#L246-L258):

```python
from accelerate import Accelerator

current_device = Accelerator().process_index
device_map = {"":current_device}
```<|||||>@younesbelkada Thanks for your help, the device_map works well. But I still have the inference speed issue, it's much slower than fp16, the code is:
```
device_map = {"": torch.cuda.current_device()}
model = OPTForCausalLM.from_pretrained(ckpt_dir, load_in_8bit=True, device_map=device_map)
input_id = torch.tensor(input_id, device=device)
gen = model.generate(input_ids,
                     min_length=1,
                     max_length=512,
                     do_sample=True,
                     temperature=1.0,
                     num_return_sequences=topk)
```
I manually send input_id to device, is that correct?<|||||>Thanks for the feedback, yes you need to manually send the input ids to the correct device
How much slower is the int8 model compared to fp16?<|||||>@younesbelkada generating 130 cases in fp16 costs ~20mins, but int8 costs more than 1h  
<|||||>Hi i'm having the same problem, as i'm having to convert the input to device every time it makes int8 way more slow than regular fp16<|||||>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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