Hi,
Even though I don't think I understood everything, I like the idea of solving some of the limitations of `te.compute`. Since the `te.compute` is in a central part of the TVM stack changing it requires a lot of work and understanding. So thank you all for continuing such development. Q1: I was wondering how this fits in the Relay ->Topi->TE->TIR flow. In a more specific case take FuseOps. AFAIK the FuseOps pass creates the multi-stage operator based on the `te.compute`s. Since you mentioned that there would be no notion of a `stage` in the new TensorIR, how would FuseOps work? and more generally how TVM's philosophy of "defining a compute rule and a separate schedule" be changed? [quote="Hzfengsy, post:1, topic:7872"] TE has limited expressiveness since each stage is defined by `Stage = te.compute(lambda expr)` , while TensorIR is a full c+±like IR. We can write any program with TensorIR as you want. Although not all programs can be scheduled, there are still more workloads that can be optimized by TensorIR. [/quote] Q2: What exactly do you mean by "not all program can be scheduled"? maybe an example? Q3: You mentioned "new scheduling primitives", could you maybe give a list? EDIT: Q4: Expected timeline of the steps? --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872/6) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/4c0ef8be45bfe6cfef23f1dfe5e0407dbf9ced10d399080ab68a174431c19b49).
