Attempting to test automatically the `_keys_to_ignore`.
# What does this PR do?

This adds a new part of the `tied_weights` test that aims at detecting automatically
when `_keys_to_ignore` is incorrectly set.

`_keys_to_ignore` aims to ignore weights that are supposed to be tied in the 
final model, meaning it's OK if the parameter is missing from the on-disk weights.
The weights are really empty during the load, but they end up being tied afterwards
so we should ignore them during the load if they are missing.


The test also aims to detect `_keys_to_ignore` that might have been set but 
could be misleading because the parameters are actually NOT tied anymore.

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Fixes # (issue)


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Models:

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 -->
_The documentation is not available anymore as the PR was closed or merged._<|||||>@ydshieh the `splinter` test failing is normal ?
```
FAILED tests/models/splinter/test_modeling_splinter.py::SplinterModelTest::test_save_load_fast_init_from_base - AssertionError: 3069.73388671875 not less than or equal to 0.001 : splinter_qass.query_start_transform.dense.weight not identical
```<|||||>@Narsil 

I am not able to reproduce the `splinter` test failure you mentioned above with current `main` on a GCP GPU VM. Could you provide more information about your environment and how you launched the test?<|||||>IT's this failure : https://app.circleci.com/jobs/github/huggingface/transformers/608396<|||||>@ydshieh This tests now fails in the CI tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py::Wav2Vec2ConformerModelTest::test_save_load_fast_init_from_base

However I can't seem to be able to reproduce locally ? Do you mind trying if it's my setup failing or the CI ?<|||||>Merging.

**fingers crossed** :)<|||||>Hey! 
I am not sure if it is because of this PR but loading NLLB (that is affected by this PR) now gives:
```
│ /home/younes_huggingface_co/debug_issues/code/transformers/src/transformers/modeling_utils.py:24 │
│ 59 in _load_pretrained_model                                                                     │
│                                                                                                  │
│   2456 │   │   │   for key in missing_keys:                                                      │
│   2457 │   │   │   │   if key.startswith(prefix):                                                │
│   2458 │   │   │   │   │   key = ".".join(key.split(".")[1:])                                    │
│ ❱ 2459 │   │   │   │   param = model_state_dict[key]                                             │
│   2460 │   │   │   │   if param.device == torch.device("meta"):                                  │
│   2461 │   │   │   │   │   if not load_in_8bit:                                                  │
│   2462 │   │   │   │   │   │   set_module_tensor_to_device(model, key, "cpu", torch.empty(*para  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
KeyError: 'encoder.embed_positions.weights'
```
Here is the snippet to reproduce the error:
```
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

src_lang = "eng_Latn"
tgt_lang = "spa_Latn"

tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", src_lang=src_lang)
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M",
                                              device_map= "auto")
```
I did not followed entirely this PR but I will dig into that now and see what exactly caused the issue 💪 

cc @Narsil @sgugger 