Using Bloom with int8 generate unreadable outputs
Author: SAI990323
Pull request: no

### System Info

- `transformers` version: 4.26.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.27
- Python version: 3.9.16
- Huggingface_hub version: 0.12.1
- PyTorch version (GPU?): 1.12.0+cu113 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

When I use the int8 type of bloom to generate outputs on 8*Tesla V100(32GB), I find all of the tokens generated by the model are "unk". Are their any ideas to help me solve this problem?
This phenomenon doesn't appear in the bloom-7b1 model.



### Who can help?

@sgugger @muellerzr 

### Information

- [X] The official example scripts
- [ ] 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

My code is here.
`from transformers import AutoModelForCausalLM, AutoTokenizer`
`checkpoint = "model_path"`
`max_memory_mapping = {0: "25GB", 1: "25GB", 2: "25GB", 3: "25GB", 4: "25GB", 5: "25GB", 6: "25GB", 7: "25GB"}`
`tokenizer = AutoTokenizer.from_pretrained(checkpoint)`
`model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", max_memory=max_memory_mapping, load_in_8bit=True)`
`inputs = tokenizer.encode('''Hello ''', return_tensors="pt").to("cuda")`
`outputs = model.generate(inputs, max_new_tokens=10)`
`print(tokenizer.decode(outputs[0]))`



And the output is "Hello unk  unk unk unk unk unk  unk  unk  unk  unk "

### Expected behavior

I expect the model outputs some meaningful results, such as "Hello, I am a young woman of 28 years old who has just arrived in New Braunfels for"  from the API in the [https://huggingface.co/bigscience/bloom?text=Hello](url) or "Hello  I am a newbie in python and I am" -- use the "bloom-7b1' model (int8) inference on a single Tesla V100

Comments:
cc @younesbelkada 

the V100 series were not supported by `bitsandbytes` but now they should be compatible since the `0.37.0` relase. What is your `bitsandbytes` version? Can you try to update `bitsandbytes` ? `pip install --upgrade bitsandbytes`

> the V100 series were not supported by `bitsandbytes` but now they should be compatible since the `0.37.0` relase. What is your `bitsandbytes` version? Can you try to update `bitsandbytes` ? `pip install --upgrade bitsandbytes`

I have used their latest version 0.37.0, and the int8 type of "bloom-7b1" seems work well on a single Tesla V100, albeit it have repetitions at the end of the outputs.

@SAI990323 
Are you still facing the issue? Can you try an approach that is similar to: https://github.com/huggingface/transformers/issues/21987#issuecomment-1458231709 and let us know if this works?
Also make sure to use `bitsandbytes==0.37.1`

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