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. --- [Visit Topic](https://discuss.tvm.ai/t/google-lasted-work-mlir-primer/1721/15) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/ce284d9a37cb5de47ee3fcab532dd17b2fff248996213b64a072d0bd95980f33). Tianqi Chen, UW, Seattle, WA, 98105, United States http://tracking.discuss.tvm.ai/tracking/unsubscribe?msgid=oFfzY6AgreTn0Y-vgk1jfw2
