add type promotion support for sparse tensors (#30429)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30429
also fix a bug in uncoalesced division
General approach here is that we:
* compute the common dtype based on input tensors
* error if the output tensor is specified and the common type can't be cast back to the output type (e.g. for inplace ops)
* convert input tensor (values) to the common dtype
* perform the op as normal (computing at the common dtype instead of the result type).
* convert/copy the result values back to that of the result tensor (for in-place ops).
For uncoalesced division we need to coalesce, because an integral tensor with values=[1,1] at the same index divided by 2 would give 1/2 + 1/2 =0 instead of 2/2=1.
Test Plan: Imported from OSS
Differential Revision: D19143223
Pulled By: nairbv
fbshipit-source-id: 480fa334c0b2b3df046818f2342cfd4e2d9d892a
