[modeling_utils] postpone bnb loading until and if it's needed
BNB shouldn't be loaded unless it's actually used - definitely not by used-everywhere `modeling_utils.py`:

The following shouldn't (1) generate all this noise and (2) use up memory and resources w/o an actual need:
```
$ python -c "from transformers import BloomModel"

===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link
================================================================================
CUDA SETUP: CUDA runtime path found: /home/stas/anaconda3/envs/py38-pt112/lib/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 6.1
CUDA SETUP: Detected CUDA version 116
CUDA SETUP: Loading binary /home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cuda116_nocublaslt.so...
```

Specifically, currently only using `from_pretrained(..., load_in_8bit=True)` should load it.

My proposal is probably not the best, but it solves this problem

Probably a cleaner solution is to rewrite `src/transformers/utils/bitsandbytes.py` to delay loading its libraries until and if it is used - not sure. Totally open to other suggestions.

@sgugger, @younesbelkada 
_The documentation is not available anymore as the PR was closed or merged._<|||||>actually the problem was much more severe - before this PR on a machine with no gpu, it lead to this huge crash:

```
python -c "from transformers import AutoModel, AutoTokenizer, AutoConfig; AutoModel.from_pretrained('gpt2'), AutoTokenizer.from_pretrained('gpt2'), AutoConfig.from_pretrained('gpt2');"

Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████| 665/665 [00:00<00:00, 550kB/s]

===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link
================================================================================
CUDA SETUP: CUDA runtime path found: /gpfswork/rech/six/commun/conda/inference/lib/libcudart.so
CUDA SETUP: WARNING! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!
Traceback (most recent call last):
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/utils/import_utils.py", line 1031, in _get_module
    return importlib.import_module("." + module_name, self.__name__)
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
  File "<frozen importlib._bootstrap>", line 991, in _find_and_load
  File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 843, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/models/gpt2/modeling_gpt2.py", line 49, in <module>
    from ...modeling_utils import PreTrainedModel, SequenceSummary
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/modeling_utils.py", line 88, in <module>
    from .utils.bitsandbytes import get_key_to_not_convert, replace_8bit_linear, set_module_8bit_tensor_to_device
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/utils/bitsandbytes.py", line 10, in <module>
    import bitsandbytes as bnb
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/__init__.py", line 6, in <module>
    from .autograd._functions import (
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/autograd/_functions.py", line 4, in <module>
    import bitsandbytes.functional as F
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/functional.py", line 14, in <module>
    from .cextension import COMPILED_WITH_CUDA, lib
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/cextension.py", line 41, in <module>
    lib = CUDALibrary_Singleton.get_instance().lib
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/cextension.py", line 37, in get_instance
    cls._instance.initialize()
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/cextension.py", line 15, in initialize
    binary_name = evaluate_cuda_setup()
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py", line 136, in evaluate_cuda_setup
    cc = get_compute_capability(cuda)
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py", line 109, in get_compute_capability
    ccs = get_compute_capabilities(cuda)
  File "/gpfswork/rech/six/commun/conda/inference/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py", line 87, in get_compute_capabilities
    check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
AttributeError: 'NoneType' object has no attribute 'cuDeviceGetCount'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/models/auto/auto_factory.py", line 462, in from_pretrained
    model_class = _get_model_class(config, cls._model_mapping)
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/models/auto/auto_factory.py", line 359, in _get_model_class
    supported_models = model_mapping[type(config)]
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/models/auto/auto_factory.py", line 583, in __getitem__
    return self._load_attr_from_module(model_type, model_name)
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/models/auto/auto_factory.py", line 597, in _load_attr_from_module
    return getattribute_from_module(self._modules[module_name], attr)
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/models/auto/auto_factory.py", line 553, in getattribute_from_module
    if hasattr(module, attr):
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/utils/import_utils.py", line 1021, in __getattr__
    module = self._get_module(self._class_to_module[name])
  File "/gpfsssd/worksf/projects/rech/six/commun/code/inference/transformers/src/transformers/utils/import_utils.py", line 1033, in _get_module
    raise RuntimeError(
RuntimeError: Failed to import transformers.models.gpt2.modeling_gpt2 because of the following error (look up to see its traceback):
'NoneType' object has no attribute 'cuDeviceGetCount'
```

basically rendering transformers completely broken if bnb was installed and the machine had no visible gpu.

after updating the clone post this PR merge all is back to normal.<|||||>@younesbelkada, I think this functionality of `load_in_8bit=True` requires checking that there is at least one gpu and cleanly assert if there isn't any. i.e this feature can be used only with gpu_count > 0.<|||||>Hi @stas00 ,

Thanks a lot for adding this! I agree with all the points stated on the PR.
Agreed also on your final suggestion, I will add a small PR to cleanly check if a GPU has been correctly detected by Pytorch