In many functions, e.g. CrossEntropyLoss — PyTorch 1.7.1 documentation and Embedding — PyTorch 1.7.1 documentation
It requires Long tensor as input which has significant larger memory foot-print than a fp16 or fp32 without actually being needed or used. This leads to optimization problems when training larger models.

Are there workarounds in not using Long tensors?
Are there any plans to extend support to other int precision types?
Would there be reasons to not extend support to other int precision types?