NaN in GPT NeoX model (generation)
### System Info

```shell
- `transformers` version: 4.20.0.dev0
- Platform: Linux-4.15.0-140-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.7.0
- PyTorch version (GPU?): 1.11.0+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>
```


### Who can help?

@LysandreJik (NeoX)

### 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

Script to run:
```
import torch
from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM

weights_path = "EleutherAI/gpt-neox-20b"
config = AutoConfig.from_pretrained(weights_path)
with init_empty_weights():
    model = AutoModelForCausalLM.from_config(config)

tokenizer = AutoTokenizer.from_pretrained(weights_path)

device_map = infer_auto_device_map(model, no_split_module_classes=["GPTNeoXLayer"])
load_checkpoint_and_dispatch(
    model,
    weights_path,
    device_map=device_map,
    offload_folder=None,
    offload_state_dict=True
)

prompt = 'Huggingface is'
input_tokenized = tokenizer(prompt, return_tensors="pt")
output = model.generate(input_tokenized["input_ids"].to(0), do_sample=True)
output_text = tokenizer.decode(output[0].tolist())
```

Script is crashing with the traceback:
```
Traceback (most recent call last):
  File "run.py", line 24, in <module>
    output = model.generate(input_tokenized["input_ids"].to(0), do_sample=True)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/generation_utils.py", line 1316, in generate
    return self.sample(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/generation_utils.py", line 1934, in sample
    outputs = self(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/accelerate/hooks.py", line 148, in new_forward
    output = old_forward(*args, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 596, in forward
    outputs = self.gpt_neox(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 488, in forward
    outputs = layer(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/accelerate/hooks.py", line 148, in new_forward
    output = old_forward(*args, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 296, in forward
    attention_layer_outputs = self.attention(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 148, in forward
    attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 208, in _attn
    raise RuntimeError()
RuntimeError
```
The problem was also mentioned here: 
https://github.com/huggingface/transformers/issues/15642#issuecomment-1133067212
https://github.com/huggingface/transformers/issues/15642#issuecomment-1133828254
The problem seems to be that `torch.einsum` returns `inf` (fp16 overflow) which leads to `nan` when calculating the softmax.

### Expected behavior

```shell
Code should run.
```

cc @zphang do you have an idea of what might be happening there?

Also happening here: https://github.com/huggingface/accelerate/issues/404<|||||>Also cc @sgugger as it's leveraging the auto map.<|||||>I don't think it comes from the auto map, some weights are Nan and so an error is raised [here](https://github.com/huggingface/transformers/blob/5af38953bb05fe722c2ec5c345f54c2712ce4573/src/transformers/models/gpt_neox/modeling_gpt_neox.py#L208). (I did say that `RuntimeError` with no messages were not ideal @zphang ;-) )<|||||>Seems to be a float16 overflow problem after applying `torch.einsum`, fixable with 

```
  attn_scores = torch.einsum("bik,bjk->bij", query, key) / self.norm_factor
+ finfo = torch.finfo(attn_scores.dtype)
+ attn_scores = attn_scores.clamp(finfo.min, finfo.max)
``` 
Although after fixing this NaN problem, the generation is still not working correctly.
<|||||>Thanks @zomux, this works. I tried something similar (casting to FP32 and back) which gave the same error:
```
Traceback (most recent call last):
  File "tt.py", line 24, in <module>
    output = model.generate(input_tokenized["input_ids"].to(0), do_sample=True)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/generation_utils.py", line 1317, in generate
    return self.sample(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/generation_utils.py", line 1937, in sample
    outputs = self(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1112, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/accelerate/hooks.py", line 150, in new_forward
    output = old_forward(*args, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 602, in forward
    outputs = self.gpt_neox(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1112, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 493, in forward
    outputs = layer(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1112, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/accelerate/hooks.py", line 150, in new_forward
    output = old_forward(*args, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 299, in forward
    attention_layer_outputs = self.attention(
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1112, in _call_impl
    return forward_call(*input, **kwargs)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 150, in forward
    attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
  File "/secondary/thies/anaconda3/envs/py38/lib/python3.8/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 218, in _attn
    attn_output = torch.matmul(attn_weights, value)
RuntimeError: Expected size for first two dimensions of batch2 tensor to be: [64, 5] but got: [64, 1]
```

I figured out that when setting use_cache=False generation runs without errors.

Change:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#L642
```
- return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
+ return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past, "use_cache": False}
```

<|||||>> I don't think it comes from the auto map, some weights are Nan and so an error is raised [here](https://github.com/huggingface/transformers/blob/5af38953bb05fe722c2ec5c345f54c2712ce4573/src/transformers/models/gpt_neox/modeling_gpt_neox.py#L208). (I did say that `RuntimeError` with no messages were not ideal @zphang ;-) )

Ah, I thought I'd removed all the RuntimeErrors, I've submitted a PR here: https://github.com/huggingface/transformers/pull/17563

As for the NaN-ing, I've not figured that out either. I found (in very ad-hoc testing) that the einsum appears to be more stable than [the original approach](https://github.com/zphang/minimal-gpt-neox-20b/blob/1d485409c0c108d1c03831cb2498040a769e8460/minimal20b/model.py#L226-L232), but it looks like it hasn't fully solved the issue.<|||||>Hello @zphang,
the problem is that when the scaling factor is applied, the overflow has already happened. 
Therefore I think the `self.norm_factor` should go into the matrix multiply (scale first and do the matrix multiply second):
```
- attn_scores = torch.einsum("bik,bjk->bij", query, key) / self.norm_factor
+ attn_scores = torch.einsum("bik,bjk->bij", query / self.norm_factor, key)
```
It seems to work fine: 

```
Huggingface is a fast-growing Chinese company that is the leader in deep learning and natural language processing.

The company raised $75million from Tencent and Baidu.

Deep Voice is a Chinese startup that enables users to control various home appliances using simple commands issued with their voice alone.
```<|||||>@zphang Is there a clear release version where the issue is resolved?
I got `RuntimeError: probability tensor contains either `inf`, `nan` or element < 0` while deploying the gpt-neox model with half.
I can deploy it completely in full preciosion.<|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.