Add tie_weights() to LM heads and set bias in set_output_embeddings()

[hackyon]

What does this PR do?

This fixes a bug from the wrong bias in prediction heads in some situations. The predictions.bias needs to be tied to predictions.decoder.bias inside tie_weights().

Repro Steps:

1. Sync to HEAD in main
2. Add the following test case to test_modeling_bert.py and run it:

    def test_save_load_bert_prediction_head(self):
        with tempfile.TemporaryDirectory() as tmpdirname:
            model_to_save = BertForMaskedLM.from_pretrained("bert-base-uncased")

            model_to_save.save_pretrained(tmpdirname)

            model = BertForMaskedLM.from_pretrained(
                tmpdirname,
                low_cpu_mem_usage=True,
            )
            model.to(torch_device)

3. Error is thrown:

FAILED tests/models/bert/test_modeling_bert.py::BertModelTest::test_save_load_bert_prediction_head - NotImplementedError: Cannot copy out of meta tensor; no data!

    def convert(t):
        if convert_to_format is not None and t.dim() in (4, 5):
            return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,
                        non_blocking, memory_format=convert_to_format)
>       return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
E       NotImplementedError: Cannot copy out of meta tensor; no data!

../venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1158: NotImplementedError

The issue was uncovered in #28802.

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• Did you write any new necessary tests?

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@ArthurZucker @amyeroberts

[amyeroberts]

Thanks for digging into this and fixing for all these models!

Just two things I think we need to do before merging:

• Run the slow model tests for these models to confirm this change is backwards compatible.
• Apply the test to all models.

[amyeroberts]

This should be applied to all the models. Could you move the test to ModelTesterMixin?

[hackyon]

Done.

I also noticed that there were other non-slow tests in ModelTesterMixin that loaded/saved pretrained models.

I had a hunch that the saving/loading should actually be pretty fast, and so ran the tests without the @slow tag. The before/after runs on CircleCI showed similar timings for tests_torch, so my 2 cents is that we don't need to mark this test as @slow.

Before run (tests_torch ran in 6m58s):
https://app.circleci.com/pipelines/github/huggingface/transformers/84140/workflows/0998b8ba-9336-49e5-9680-2ddd86443669

After run (tests_torch ran in 6m48s):
https://app.circleci.com/pipelines/github/huggingface/transformers/84451/workflows/f5efa9af-c079-4303-8611-574f8a3bf7bd

[hackyon]

This came from an existing comment:

transformers/src/transformers/models/deta/modeling_deta.py

Line 1780 in 354775b

# We can't initialize the model on meta device as some weights are modified during the initialization

It seems like the DetaForObjectDetection model has logic to manipulate the params during init, and so it simply doesn't support initializing on a meta device.

With that said, should we add some kind of flag in PreTrainedModel that would throw an error when trying to initial on meta device for models that do not support it? So far, it seems like DetaModel is the only one, so maybe not worth the effort to do so.

cc @SunMarc who added this: #27089

[amyeroberts]

Let's keep as-is for the moment, until we have more affected models. DETR and DETR-like models are special but I think they're the only case.

[amyeroberts]

Great work - thanks for adding this!

[amyeroberts]

Let's keep as-is for the moment, until we have more affected models. DETR and DETR-like models are special but I think they're the only case.

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