Introduce explicit parameter mapping - #148

Closes #71.

Advantages:

• This removes a lot of duplication between models. For example, now all of the models use the same attention implementation, and even the whole transformer block.
• Certain models looked very differently, even though they were for the most part the same, that's because their reference implementations applied the same concepts, but implemented them on their own (naming, structuring and nesting the layers). Now the models are much more consistent, both in terms of layer nesting and naming, which makes it much easier to see the similarities and differences.
• We no longer need to follow an artificial layers nesting, that follows from the class composition in the Python implementations. For example, when building a model with head, we don't need to prefix the base model with something like bert., we just add the head layers.
• We have full control over the parameters, which makes us much less coupled to the original implementation details. For example the original GPT2 implementation uses conv1d layers (in terms of implementation those are essentially transposed dense layers) for attention. The parameter mapping has enough flexibility that we actually slice the conv1d kernels into dense layer kernels and use the same attention implementation as all other models.

Disadvantages:

• A single file doesn't contain all of the model. This already wasn't the case and I don't consider this a major issue. Also, we could still be doing that and use consistent naming/implementation, but again I don't think we should.
• Each model needs explicit mapping to hf/transformers parameters. This also implies we use different parameter names than hf/transformers.