Update to v0.2: a more flexible framework for compressing index of any dense retrieval models

[jingtaozhan]

This is a major code update. The previous code is deprecated. Here are several features:

• Flexible code framework. The previous code has a preprocessing process, which is abandoned now. Tokenization is done during training and inference. JPQ and RepCONC no longer depend on a certain dense retrieval model structure. They are two training instances that accept a dense retrieval model as input. So dense models of different architectures can all be the input of JPQ and RepCONC.
• Support distributed training for RepCONC.
• Support large batch sizes for RepCONC with GradCache
• Already added and will add more examples about transferring dense retrieval models into memory-efficient ones.