Dear community,
I'm currently trying to **reduce overall Auto-TVM runtimes** by selectively tuning only the kernels that are actual hotspots in the application. **Hotspot detection** can be performed fairly easily, e.g. by using the **debug runtime** which gives a detailed callgraph profile when executing run(). My question is **how to match** these identified operations to the AutoTVM selected kernels. On the one hand, the profile information looks like [this example](https://docs.tvm.ai/dev/debugger.html) shows. A prioritized list of nodes mostly identified by their LLVM IR name. On the other hand, when selecting the tasks to be tuned ``` kernels =autotvm.task.extract_from_program(ir["main"], target=target, params=params, ops=None)``` gives you a list of [Task](https://docs.tvm.ai/api/python/autotvm.html#tvm.autotvm.task.task.Task) objects, e.g.: ``` Task(func_name=dense_nopack.x86, args=(('TENSOR', (1, 16), 'float32'), ('TENSOR', (64, 16), 'float32'), None, 'float32'), kwargs={}, workload=('dense_nopack.x86', ('TENSOR', (1, 16), 'float32'), ('TENSOR', (64, 16), 'float32'), None, 'float32')) ``` My question refers to how to match such Tasks to their IR counterparts? Any help, ideas, suggestions are much appreciated! Thank you & Best regards --- [Visit Topic](https://discuss.tvm.ai/t/autotvm-selective-tuning-of-hotspots/6083/1) 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/b3ab2b4aee2549639b76b097f07b59596f5d6f8492a15dcdfdb721d70a74f9df).
