Hi all,
In my effort to accelerate AArch64 through tensorization, I incurred into an issue. Basically, I am padding my input tensor, to let `tensorize` work (I need rows to be multiple of 4 and cols to be multiple of 16). However, bound inference removes padding (since it is not used) and, when I tile the computation, `tir.likely` statements appear. This results in `tensorize` producing the error: ``` TVMError: Tensorize failed, split condition tir.likely(((...)) relies on var defined inside tensorize scope ``` One solution is to add a (sufficiently complex) zero multiplication by a padding element, to trick the bound inference (see for example [here](https://github.com/apache/incubator-tvm/blob/34647ed8defcc52e4d5a173feb577caf35df7a82/python/tvm/topi/bifrost/conv2d.py#L346-L350)). However, this is very hacky and it is not supposed to last (as the bound inference gets smarter, it might detect that the added element is zero). The question is: should we try to come up with a "good" solution for this? One idea might be to let tensorize accept `@tir.likely` statements and replace them with a "variable size" tensorization which will be provided by the developer. For instance, we might add a `_intrin_func_variable` private function that gets called only when a *variable tensorization* (i.e., a tensorization over `@tir.likely`) is needed. I have also read through [this post](https://discuss.tvm.ai/t/loop-partitioning-and-tensorization-work-on-different-ir-levels/876), but it doesn't seem to arrive to a concrete solution. Any ideas? @anijain2305, @FrozenGene, @matt-arm, @tqchen , @ramana-arm --- [Visit Topic](https://discuss.tvm.ai/t/loop-partitioning-padding-and-tensorization/7753/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/cbe5f21debb1eca5c6009968242d48ca5cced3335b41b4b2351bc5e2db4fbf3c).
