DeepSpeed optimizer always creates fp32 master params/gradients/optimizer states.
However, we sometimes want to keep them lower precision given torch.autocast support.
This PR allows lower precision master params/grads/optimizer states with bf16/fp16 enabled.

DeepSpeed currently accepts fp16_master_weights_and_gradients option under fp16 section (not documented) with ZeRO1/2. This PR extends this for bf16 and also ZeRO3.

In bf16 section, we can have new items bf16_master_weights_and_grads and bf16_optimizer_states.
Similary to fp16_master_weights_and_grads, bf16_master_weights_and_grads keeps master parameters in bf16. bf16_optimizer_states keeps optimizer states also in bf16. Here is an example configuration:

        "bf16": {
            "enabled": true,
            "bf16_master_weights_and_grads": true,
            "bf16_optimizer_states": true
        }

Note that bf16_master_weights_and_grads==True and bf16_optimizer_states==False is supported only with cpu offloading. Also, we don't have fp16_optimizer_states as it won't be practical. More details are described in config-json.md

Previously, torch.autocast support (torch_autocast section in config) was not compatible with bf16 fp16 enabled, but we now accept the combination.

This PR also adds some test cases for the configurations as well as the combination with torch.autocast.