load_in_8bit now respects 'balanced' device maps in multi-gpu environments
# What does this PR do?

Fixes `max_memory` generation for 'auto' 'balanced' and 'balanced_low_0' `device_map`s for models being loaded in 8bit


Fixes # (N/A)
no issues found, but one guy made a [comment](https://github.com/TimDettmers/bitsandbytes/issues/177#issuecomment-1481609654) about it in the bnb issues, and it caused confusion and workarounds elsewhere.
The problem was easily worked around by manually passing a device map or max memory config.

Before this change, the following code would attempt to load the whole model on the first GPU in a two gpu setup, potentially causing OOM errors. After the change, it loads it evenly across GPUs, as intended.
```python
model = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    load_in_8bit=True,
    device_map="auto",
)
```


Additionally, I removed 'pipeline' from my earlier comment in a previous PR, as true Pipeline Parallelism would require some more non-trivial changes to the model.


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_The documentation is not available anymore as the PR was closed or merged._<|||||>I'm not sure if this is caused by your work but I met this problem:

I'm training LLaMA-13B with PEFT, using lora + modules_to_save=['model.embed_tokens', 'lm_head']
And it can run training normally
But the finally model file doesn't contain the lm_head part, only lora + embed_tokens.
And When I use DDP or single card. It can save all the thing normally.<|||||>(I'm using your fork + your modified version alpaca lora code)<|||||>@KohakuBlueleaf that's a bit disingenuous, as you've changed quite a few other things 😉

I think I was able to reproduce what you were talking about on [your repo](https://github.com/KohakuBlueleaf/guanaco-lora) though. Do you mean that when you run export_hf_checkpoint.py, `head_changed` shows `False`?

That's what I am seeing. What's strange, is that the weights *are* in the lora, they're just named "base_model.model.lm_head.0.weight" instead of "base_model.model.lm_head.weight"

If you add `adapters_weights["base_model.model.lm_head.weight"] = adapters_weights["base_model.model.lm_head.0.weight"]` to peft_model.py right after the lora is loaded, but before it is merged with the base_model, then you can get `head_changed: True` 

This might be from my change... as the lm_head is a special case when loading in 8bit, but I'm not sure. I see the same result for *both* single or multi-gpu.  I've run out of time today to investigate though, so I'll have to dig in more later, possibly tonight.<|||||>> @KohakuBlueleaf that's a bit disingenuous, as you've changed quite a few other things 😉
> 
> I think I was able to reproduce what you were talking about on [your repo](https://github.com/KohakuBlueleaf/guanaco-lora) though. Do you mean that when you run export_hf_checkpoint.py, `head_changed` shows `False`?
> 
> That's what I am seeing. What's strange, is that the weights _are_ in the lora, they're just named "base_model.model.lm_head.0.weight" instead of "base_model.model.lm_head.weight"
> 
> If you add `adapters_weights["base_model.model.lm_head.weight"] = adapters_weights["base_model.model.lm_head.0.weight"]` to peft_model.py right after the lora is loaded, but before it is merged with the base_model, then you can get `head_changed: True`
> 
> This might be from my change... as the lm_head is a special case when loading in 8bit, but I'm not sure. I see the same result for _both_ single or multi-gpu. I've run out of time today to investigate though, so I'll have to dig in more later, possibly tonight.

Yeah I also figured it out
But thx for your reply!