[GitHub] [tvm] masahi opened a new pull request, #13357: [MetaSchedule] Fuse loops around shared to global store block in `MultiLevelTilingTensorCore`

[GitBox <gi...@apache.org>]

masahi opened a new pull request, #13357:
URL: https://github.com/apache/tvm/pull/13357

   Currently, vectorization of shared to global store in tensor core auto tensorization is not done properly, since most blocks have the `T.where` predicate which disables vectorization. 
   
   The predicate is introduced after `Split` in cooperative fetch: https://github.com/apache/tvm/blob/main/src/meta_schedule/postproc/rewrite_cooperative_fetch.cc#L159-L162
   As the code says, this split is supposed to be applied to a fused loop. This is the case for cache read blocks, where `AddReadReuse` explicitly fuses loops around cache read blocks. But `AddWriteReuseTensorCore` doesn't fuse loops after cache write. So for cache rewrite blocks, we always try to split a single axis by large factors like [None, 4, 32, 2].


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[GitHub] [tvm] tvm-bot commented on pull request #13357: [MetaSchedule] Fuse loops around shared to global store block in `MultiLevelTilingTensorCore`

[GitBox <gi...@apache.org>]

tvm-bot commented on PR #13357:
URL: https://github.com/apache/tvm/pull/13357#issuecomment-1311067471

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[GitHub] [tvm] masahi merged pull request #13357: [MetaSchedule] Fuse loops around shared to global store block in `MultiLevelTilingTensorCore`

[GitBox <gi...@apache.org>]

masahi merged PR #13357:
URL: https://github.com/apache/tvm/pull/13357


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