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From tqchen via TVM Discuss <nore...@discuss.tvm.ai>
Subject [TVM Discuss] [Development] Google lasted work: MLIR Primer
Date Thu, 04 Apr 2019 16:51:03 GMT


Good comments. I would like to separate the answer in two parts, and this is an updated view
after I take look at the MLIR's codebase.

## Interpretation of MLIR's Vision

I think what you answered reflects MLIR's vision. Make the abstract class of IR and derive
dialects. But not necessarily provide specific pass for the dialect, so if X-IR is a dialect
of MLIR,  then there are dialect specific passes that is needed in the pass. 

Polyhedral dialect is a dialect in MLIR. In the current case, the polyhedral IR is part of
the mlir codebase, which gives the view of "native", but non-the-less it is a dialect just
like the other automatic optimization dialect. The fact that it is part of the native code
base does give an opinionated view of what what automatic optimization should be like in MLIR
ecosystem. I think it is still very much an open problem, TVM has done a lot in this direction,
and we can collectively innovate on this area.

## How TVM can work with MLIR

First of all, MLIR won't make TVM obsolete. In the contrary, it can help TVM stack by providing
insights in IR design and possibly some lowering infrastructure.The community will keep improving
our current IR infrastructure toward a better unified TVM-IR infra.  We will try to define
TVM dialects in MLIR to see if it makes sense to allow bi-directional translation between
MLIR and TVM-IR, this way we can take benefit of some of the infra provided by MLIR and make
TVM work together with MLIR's ecosystem.





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Tianqi Chen, UW, Seattle, WA, 98105, United States
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