Thanks @sjoerdmeijer , sorry for getting back to this late. If LLVM also encodes the SVE vector as a special type(and not tying the n) it could be a precedence that we can learn from. I do want to point out that the same approach won't work when it comes to scalable matrix instruction, so it would be good to think about a loop pattern based approach from that angle.
If we conclude that we really want to introduce the scalable vector into data type and so on. A remaining thing we need to resolve is the encoding. DataType and DLDataType are part of DLPack standard, used as standard ABI convention. So ideally we do not want to change the type signature of DataType DLDataType. What we might be able to is to introduce a magic lane number (say `DataType::kScalableVectorLaneMark = -1`) that indicate a scalable vector type, and also use similar approaches for other language construct as well. This would alleviate the problems from that side. This being said. It would still be good to get @MeeraN7 @giuseros @sjoerdmeijer 's take on the possibility of using a normalized loop pattern vs embedding into the vector. Thank you for all the discussions so far! -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/18#issuecomment-930488098
