GPT2 model state dictionary Tensor types are not matching with pytorch
Author: snaik2016
Pull request: no

## Environment info
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- `transformers` version: 4.0.1 and 4.4.2
- Platform: windows
- Python version: 3.6
- PyTorch version (GPU?): 1.8
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help
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Models: GPT2

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @Rocketknight1

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

Model hub:

- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:
@patrickvonplaten, @LysandreJik
1.  model = AutoModelForCausalLM.from_pretrained('gpt2', force_download=True)
2. Download size is 548M the size on disk is 535M
2. model.save_pretrained(<some_dir>) the model size comes down to 498M
3. Now a freshly downloaded model not using from_pretrained which has size of 535M is loaded using torch.load
4.  for k, v in model.items():
...     if 'attn.bias' in k:
...         print(v.type())
5. The types are FloatTensor whereas if the same code is run with the model in step 1. I get 
key name transformer.h.0.attn.bias = type = **torch.ByteTensor**
key name transformer.h.0.attn.c_attn.bias = type = torch.FloatTensor

Is this expected?

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## Expected behavior
the model serialization and deserialization shouldn't change the tensor types.

<!-- A clear and concise description of what you would expect to happen. -->


Comments:
Hey @snaik2016,

Note that `transformer.h.0.attn.bias` is actually not the bias weights of the attention layer but a pre-computed causal mask: https://github.com/huggingface/transformers/blob/319d840b46fd3a13e0434de9de69bd74a2f22f43/src/transformers/models/gpt2/modeling_gpt2.py#L130 

=> This means that you don't need to pass this parameter - it'll be generated automatically.

TLDR; this behavior is expected IMO

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.

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