auto_find_batch_size is supported w deepspeed
Author: winglian
Pull request: yes

# What does this PR do?

Deepspeed seems to be supported with auto_find_batch_size. see https://github.com/huggingface/transformers/commit/6015d0ad6ce946e763fa1052614317ff34825d33

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Comments:
in actual testing, it does cause the train loop to fail


```
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/liger_kernel/transformers/model/llama.py", line 82, in lce_forward 
[rank0]:     outputs = self.model(
[rank0]:               ^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 950, in forward 
[rank0]:     inputs_embeds = self.embed_tokens(input_ids)
[rank0]:                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/modules/sparse.py", line 163, in forward
[rank0]:     return F.embedding(
[rank0]:            ^^^^^^^^^^^^
[rank0]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/functional.py", line 2264, in embedding
[rank0]:     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: RuntimeError: 'weight' must be 2-D
```

Should work with Zero1 and 2, do you have a repr? Also: https://github.com/huggingface/transformers/pull/31731

@muellerzr The error above should no log get be an issue. It's an axolotl issue having to do with packing that's now fixed by another pr. 

superseded by https://github.com/huggingface/transformers/pull/31731