- Separate spread scheduling and default hydra scheduling (i.e. SpreadScheduling != HybridScheduling(threshold=0)): they are already separated in the API layer and they have the different end goals so it makes sense to separate their implementations and evolve them independently.
- Simple round robin for spread scheduling: this is just a starting implementation, can be optimized later.
- Prefer not to spill back tasks that are waiting for args since the pull is already in progress.
Expands the `to_torch` method for Datasets with:
* An ability to choose to output a list/dict of feature tensors instead of just one (through setting `feature_columns` to be a list of lists or a dict of lists)
* An ability to choose whether the label should be unsqueezed or not
* An ability to pass `None` as the label (for prediction).
Furthermore, this changes how the `feature_column_dtypes` argument works. Previously, it took a list of dtypes for each feature. However, as the tensor was concatenated in the end, only one dtype mattered (the biggest one). Now, this argument expects a single dtype which will be applied to the features tensor (or a list/dict if `feature_columns` is a list of list/dict of lists).
Unit tests for all cases are included.
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
