LayoutLM Cuda Memory Error
### System Info

torch==1.13.0+cu116
transformers==4.24.0

### Who can help?

@philschmid 

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

I am trying to train LayoutLMv1 following this guide https://www.philschmid.de/fine-tuning-layoutlm

However, when I execute `trainer.train()` I get this error:

```python
RuntimeError                              Traceback (most recent call last)
<command-1206590250403017> in <module>
----> 1 trainer.train()

/databricks/python/lib/python3.8/site-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1499             self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
   1500         )
-> 1501         return inner_training_loop(
   1502             args=args,
   1503             resume_from_checkpoint=resume_from_checkpoint,

/databricks/python/lib/python3.8/site-packages/transformers/trainer.py in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1747                         tr_loss_step = self.training_step(model, inputs)
   1748                 else:
-> 1749                     tr_loss_step = self.training_step(model, inputs)
   1750 
   1751                 if (

/databricks/python/lib/python3.8/site-packages/transformers/trainer.py in training_step(self, model, inputs)
   2506 
   2507         with self.compute_loss_context_manager():
-> 2508             loss = self.compute_loss(model, inputs)
   2509 
   2510         if self.args.n_gpu > 1:

/databricks/python/lib/python3.8/site-packages/transformers/trainer.py in compute_loss(self, model, inputs, return_outputs)
   2538         else:
   2539             labels = None
-> 2540         outputs = model(**inputs)
   2541         # Save past state if it exists
   2542         # TODO: this needs to be fixed and made cleaner later.

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/transformers/models/layoutlm/modeling_layoutlm.py in forward(self, input_ids, bbox, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
   1190         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
   1191 
-> 1192         outputs = self.layoutlm(
   1193             input_ids=input_ids,
   1194             bbox=bbox,

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/transformers/models/layoutlm/modeling_layoutlm.py in forward(self, input_ids, bbox, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)
    825             inputs_embeds=inputs_embeds,
    826         )
--> 827         encoder_outputs = self.encoder(
    828             embedding_output,
    829             extended_attention_mask,

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/transformers/models/layoutlm/modeling_layoutlm.py in forward(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)
    494                 )
    495             else:
--> 496                 layer_outputs = layer_module(
    497                     hidden_states,
    498                     attention_mask,

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/transformers/models/layoutlm/modeling_layoutlm.py in forward(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
    379         # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
    380         self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
--> 381         self_attention_outputs = self.attention(
    382             hidden_states,
    383             attention_mask,

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/transformers/models/layoutlm/modeling_layoutlm.py in forward(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
    306         output_attentions: Optional[bool] = False,
    307     ) -> Tuple[torch.Tensor]:
--> 308         self_outputs = self.self(
    309             hidden_states,
    310             attention_mask,

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/transformers/models/layoutlm/modeling_layoutlm.py in forward(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)
    170         output_attentions: Optional[bool] = False,
    171     ) -> Tuple[torch.Tensor]:
--> 172         mixed_query_layer = self.query(hidden_states)
    173 
    174         # If this is instantiated as a cross-attention module, the keys

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1188         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1189                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190             return forward_call(*input, **kwargs)
   1191         # Do not call functions when jit is used
   1192         full_backward_hooks, non_full_backward_hooks = [], []

/databricks/python/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
    112 
    113     def forward(self, input: Tensor) -> Tensor:
--> 114         return F.linear(input, self.weight, self.bias)
    115 
    116     def extra_repr(self) -> str:

RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)`
```

I can train LayoutLMv3 without any memory issue.

### Expected behavior

I shouldn't get any memory issue as this model is smaller.
It's advised to run the same code on CPU to get a more understandable error message<|||||>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.