Thanks for taking a look @tqchen! Since scheduling will be completed with TensorIR, it will provide the building blocks for being plugged into an IRModule=>IRModule transformation pass. For our current use-case, it's important to be able to fallback to previous optimizations in the form of TE schedules / TOPI where coverage of the TensorIR schedules doesn't exist.
>From the [proposed >strategy](https://discuss.tvm.apache.org/t/discuss-tvm-core-strategy-for-operator-scheduling-and-tuning/16352), > I understand it's important to ensure the schedule can operate on a generic >compute definition of the operation. In the case of matmul-style operations, >we'd want to apply "array packing" to the input, which is currently expressed >via the compute definition. Is it possible to express this through TIR >scheduling alone? -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/107#issuecomment-1944331637 You are receiving this because you are subscribed to this thread. Message ID: <apache/tvm-rfcs/pull/107/c1944331...@github.com>
