> Hey @cbalint13 thanks for asking! Absolutely! > @junrushao1994
First, thanks a lot for your time ! - I am very happy even just to witness what is going on recently in TVM (on mind blowing pace). > > Was Auto Tensorization removed form this list (was at section [M4b] if I > > recall), what was/is the plan with ? > > The only reason is that I'm trying to organize the roadmap. Auto > tensorization is a huge item and we want to have a separate tracking issue > for it. As you already see, we have been upstreaming auto > tensorization-related PRs, including #9871 #10066. [My > branch](https://github.com/junrushao1994/tvm/tree/meta-schedule) also > contains auto tensorization-related working examples if you want to try them > out now :-) * I see now, thanks for clarification, noticed the recent "blockize - tensorize" PR (quite a large piece, still diving on it). > > > Also regarding of design plan, will/have something in common with > > principles of https://arxiv.org/abs/2101.08458? > > This work is done by my fellow colleagues, and of course we are aware, and we > have a lot in common :-) Their codebase is public > [here](https://github.com/were/unit). The difference here is that we are now > using TensorIR, a more powerful and systematic IR/scheduling system to > support tensorization * Was familiar that code-base for [UNIT](https://github.com/were/unit), it is good to know that such feature will make it into the new TIR. * I am thinking on framework (early [public sketch](https://github.com/cbalint13/OLIMP)) that emits HDL (verilog) blocks reusable and/or as cpu-isa extensions in many possible forms sampled within some combinatorial search-space and auto-tensorisation would be key process in evaluation and metrics here. * It may end sampling some very wierd-looking hardware (including systolic blocks) so auto-tensorizer might need enhancement on some more challenging ends (as i already looked at UNIT). Can't wait to try it, will look into mentioned WiP early branch. Many thanks again ! -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm/issues/8473#issuecomment-1022527520 You are receiving this because you are subscribed to this thread. Message ID: <apache/tvm/issues/8473/1022527...@github.com>
