"total_flos" showing much bigger number than expected
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.14.1
- Platform: Ubuntu 20.04.1
- Python version: 3.8.5
- PyTorch version (GPU?): 1.8.0 (True)
- Tensorflow version (GPU?): n/a
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: DDP

### Who can help

Models:
- TAPAS, LayoutLM, LayoutLMv2, LUKE, ViT, BEiT, DEiT, DETR, CANINE: @NielsRogge

If the model isn't in the list, ping @LysandreJik who will redirect you to the correct contributor.

Library:
- Trainer: @sgugger

Documentation: @sgugger

## Information

Model I am using (Bert, XLNet ...): ViT

The problem arises when using:
* [o ] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [o ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. download transformers with git clone and pip install
2. run example script in transformers/examples/pytorch/image-classification/run_image_classification.py
3. disable pretrained weights loading to enable start pretraining of ViT-L/16
4. pretraining ViT-L/16 on 1 A100 GPU for 2 images (1 epoch only). In the below result, dividing "total_flos" by 2 would result in 273,934,171,766,784 floating point operations, or around 274 teraFLOPs. This is much bigger than the reported 122.9 GFLOPs in   the "Scaling Vision Transformer" paper (https://arxiv.org/pdf/2106.04560.pdf).
```
{
  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 1.0,
  "global_step": 2,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
    {
      "epoch": 1.0,
      "step": 2,
      "total_flos": 547868343533568.0,
      "train_loss": 0.06623733788728714,
      "train_runtime": 2.6909,
      "train_samples_per_second": 0.743,
      "train_steps_per_second": 0.743
    }
  ],
  "max_steps": 2,
  "num_train_epochs": 1,
  "total_flos": 547868343533568.0,
  "trial_name": null,
  "trial_params": null
}
```

## Expected behavior

"total_flos" divided by number of train images would match the reported model's GFLOPs in "Scaling Vision Transformer" paper.
cc @TevenLeScao since he implemented the feature.<|||||>Hey, sorry for the incorrect number - this function was originally written for language models, and might not transfer to vision models, for example. Do you need this number or is it just for checking?<|||||>Hello, I was trying to benchmark my A100 server, so having this FLOS number would be helpful for me to calculate the % of peak GPU FLOPS attained by ViT models.<|||||>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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