> I am not sure if tensorize is a good way to suport VNNI: > > 1. VNNI is not true tensorization, though reduction dimension is introduced. > It still operates on 1-D inputs. Due to the design of `tensorization` > interface, you need to provide the declared intrin the shape of tensors > offloaded, but essentially they are 1-D. > 2. Another thing I am worrying about is imperfect tiling. Since `tensorize` > cuts off the whole loop body down, without being aware of the loop body > replaced. Thus, it is hard to extend this to imperfect tiling case.
@were : You are right. I report the current performance of the implementation in this PR in the summary. Not sure how to overcome the limitation of TVM. cc @tqchen @anijain2305 -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/pull/3388#issuecomment-516719085
