Refactor initialization tests to check init from from_pretrained

[amyeroberts]

What does this PR do?

Adds a new test which checks that all model weights are correctly initialized when creating a model using from_pretrained. This was done by:

• Adding a new test_initialization_from_pretrained test
• Abstraction out the common logic between test_initialization_from_pretrained and test_initialization into _test_models_weight_initialization

Motivation

A weight init bug in CLIP was highlighted in #30374.

When a model is created via a config, the layer weights are initialized with their default torch initialization i.e. with parameter.reset_parameters().

When a model is created with a checkpoint using from_pretrained, the model weights are not initialized, and are instead torch.empty arrays. This is to speed up model creation: there's no need to initialize if we're going to use the checkpoint's weight.

However, if the weight isn't in the checkpoint and doesn't have init behaviour specified in the model's _init_weight method, then the weight will remain empty.

This can be tricky to catch if only part of the layer's weights are initialized. For example, in CLIP, the bias initialization is specified but not the weight. This means the Linear module will be marked as initialized even if this is partly True.

The initialization bug wasn't caught in our test suite, despite test_initialization, because this test creates the model from the config, not from a checkpoint.

As this is difficult to correctly handle within the code itself, I propose adding a test which will at least catch when weights aren't properly covered in the from_pretrained scenario

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• Did you write any new necessary tests?

[amyeroberts]

Suggestion for reviewer to look at the changes in test_modeling_common.py as this will provide more context for the changes in the other model's tests :)

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