Caching node features in GPU

## 🚀 Feature

Hi, this is researcher from SJTU. We hope to contribute DGL with the ability to cache node features in GPU when it's impossible to hold all features.

## Motivation

When using GPU for sampling-based mini-batch training, it's likely that a GPU can not hold all node features. In this case, DGL choose to store all features at CPU. Our recent work finds that, part of node features cached in GPU and cache policy properly chosen, the cost of copying feature from GPU to CPU can be significantly reduced, e.g. caching 20% features leads to 80% cache hit rate.

## Alternatives

The simplest method is to cache features with smallest node id. An efficient yet simple method is to cache features of nodes with highest degree, as recent work PaGraph proposed. We step further by running an offline epoch of sample and then caching the mostly visited nodes.

The degree-based method is easier to contribute, since it only requires knowledge of the graph. Though the sample-based policy achieves greatest performance, it's not easy to design an elegant interface, since it requires knowledge of sample algorithm.

## Pitch

We'd like to know how the community think about the necessity of cache? I've lookup to DGL's PR history and find previous efforts to extend DGL with cache but failed to achieve agreement on the design of semantics #316 . We still hope to take over the job and first contribute the ability of degree-based cache, as it requires less design work but boosts performance significantly. As for the sample-based method, we'd like to borrow the help of community to identify the proper way to design new modules and port our prototype to DGL, if the proposal can be accepted.