Fix `test_run_seq2seq_bnb` CI
# What does this PR do?

Fix `test_run_seq2seq_bnb` CI. See [failed job run](https://github.com/huggingface/transformers/actions/runs/3834635537/jobs/6527258225)

It seems the expected reduced GPU memory usage only happens when running the test on multi GPU env.
I simply add `require_torch_multi_gpu` without trying to understand why it fails with single GPU env.

I can try to figure it out, but the probability that @stas00 knows the reason > 1.0, so I would like to see if he has any comment first.

Error
```
tests/extended/test_trainer_ext.py::TestTrainerExt::test_run_seq2seq_bnb
(line 256)  AssertionError: -0.0023021928988980356 not greater than 10 : should use very little peak gpu memory with BNB, compared to without itbut got gpu_peak_mem_orig=509447168 and gpu_peak_mem_bnb=510622720
```
_The documentation is not available anymore as the PR was closed or merged._<|||||>cc @manuelciosici<|||||>hmm, I didn't write this test and don't know why these numbers were set, so perhaps it's easier to ask the one who wrote it?

If it's not possible please let me know I will be able to study it later, as I'm off to the airport now.<|||||>but yes, such tests should do the measurements per number of gpus usually. i.e. 1 gpu - measurement 1, 2 gpus - measurement 2, etc.

the easiest fix is to ensure you run on exactly that many gpus always and then you need only one "truth" to measure against. <|||||>(we can investigate later, not urgent)

@stas00 The first error occurred is that we expect the GPU memory usage will be larger when not using BNB (i.e. `gpu_peak_mem_orig `) than when using BNB (i.e. `gpu_peak_mem_bnb`). The test assert 10% difference. However, on our single GPU runner, 

```bash
gpu_peak_mem_orig=509447168
gpu_peak_mem_bnb=510622720
```
which is quite weird (intuitively).

- We can definitely adjust the values for different environment, but probably it's a good idea to understand what's going on here if possible.
- We left comment in the original PR page, but haven't heard from the PR author. But I cc them in a comment in this PR.

 <|||||>some quick thoughts:
- do we clear cuda cache there between the measurements? and first call `gc.collect()` (then cache clear) 
- using a larger model should make the difference (savings) more distinct

and of course the test might be failing if bnb is broken - recent update? try earlier version?<|||||>I can reproduce the failure, looking<|||||>Fixed here https://github.com/huggingface/transformers/pull/21030
<|||||>Close in favor of #21030 21030