iree

Obviously you're entitled to your opinion, but I think it's completely wrong. (Note: I don't work for Google, or know anyone on the MLIR team.) Take a look at this recent talk by the people behind IREE[0] (who are not part of the MLIR team): https://drive.google.com/file/d/1os9FaPodPI59uj7JJI3aXnTzkut... In a nutshell, they have made HUGE gains by taking advantage of the MLIR infrastructure for a use case not anticipated by the MLIR authors (i.e. making the TensorFlow-to-hardware path easier to develop). In my opinion, MLIR is going to be much more successful long-term than LLVM is today, and by long-term, I mean "in the next few years." The number of teams consolidating already around MLIR is astounding, and having a multi-dialect SSA-based IR is a freaking game changer for compiler writers. [0] https://github.com/google/iree

I wouldn't be worried for MLIR. MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for example (https://github.com/google/iree ). The TensorFlow lite converter has been replaced by the new MLIR-based one, similarly for the Edge TPU. But more than that, what makes me confident is the traction we are getting outside Google. First, we landed MLIR in LLVM last month: https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80... The LLVM Fortran frontend (f18/flang) which will merge soon in the LLVM monorepo is using MLIR for their own IR. It'll be exiting to develop a non-ML MLIR-based frontend within LLVM! In particular Flang is opening an HPC perspective that could be leveraged by other DSLs later. They are adding an OpenMP dialect to MLIR right now: https://llvm.dis