The implementation of grad clipping is not correct when gradient accumulation is enabled
torch.nn.utils.clip_grad_norm_ should be applied before optimizer.step() not after each backward



## 🐛 Bug

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

Language I am using the model on (English, Chinese....):

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

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

## To Reproduce

Steps to reproduce the behavior:

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## Expected behavior

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## Environment

* OS:
* Python version:
* PyTorch version:
* PyTorch Transformers version (or branch):
* Using GPU ?
* Distributed of parallel setup ?
* Any other relevant information:

## Additional context

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This should be fixed in https://github.com/huggingface/transformers/pull/1580<|||||>yeah @yangyiben please let me know if that merge fixes it<|||||>#1580 is now merged