Avoid the problem of gradient backpropagation being truncated when fine-tuning CLIP text encoder in diffusers
Author: liming-ai
Pull request: yes

…orting errors

# What does this PR do?

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Fixes # (issue)
In many cases, we may want to fine-tune CLIP Text Encoder. However, naively modify the training code such as [train_t2i_model in diffusers](https://github.com/huggingface/diffusers/blob/d9f71ab3c3cc162226ec1c9945fef1a5faf4c512/examples/text_to_image/train_text_to_image.py#L621) to make `text_encoder.requires_grad_(True)` leads to gradient truncated issue and results in training errors like this:

```
Traceback (most recent call last):
  File "code/diffusers/examples/instruct_pix2pix/train_instruct_pix2pix.py", line 1261, in <module>
    main()
  File "code/diffusers/examples/instruct_pix2pix/train_instruct_pix2pix.py", line 1136, in main
    accelerator.backward(loss)
  File "/usr/local/lib/python3.9/dist-packages/accelerate/accelerator.py", line 2134, in backward
    loss.backward(**kwargs)
  File "/usr/local/lib/python3.9/dist-packages/torch/_tensor.py", line 492, in backward
    torch.autograd.backward(
  File "/usr/local/lib/python3.9/dist-packages/torch/autograd/__init__.py", line 251, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.LongTensor [1, 77]] is at version 3; expected version 2 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
```

To fix this issue, we should avoid the `position_ids` be changed by:
```
position_ids = self.position_ids[:, :seq_length].clone()
```


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Comments:
> Hi @liming-ai, thanks for opening this PR and adding this fix!
> 
> Running `make fix-copies` and then `make fixup` should resolve the failing quality checks

Done, could you please check this PR again?

@liming-ai Could you rebase on main to include any upstream changes? I think this should resolve the failing tokenization tests 

> @liming-ai Could you rebase on main to include any upstream changes? I think this should resolve the failing tokenization tests

Hi @amyeroberts,

The code changes I pushed are the results of executing the command `make fix-copies` and then `make fixup` on the main branch. I have no ideas why there are still errors.

@liming-ai Could you try rebasing on main to include upstream changes? This should resolve the failing tokenization tests 

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.