Got it! Thank you very much ~~
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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
Hi all,
I defined a toy computation and scheduled it in TVM. I am having some
difficulty in understanding how the lowered code that TVM produces corresponds
to the schedule. I have reproduced both the Python and the lowered IR below.
Python code:
```
import tvm
from tvm import te
I think the relay's convention is to convert multiple parameters into a tuple.
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Thank you for the response! I try it using the cpu backend and target with
"llvm".
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Since tvm is a compiler infrastructure, though the convolution is defined using
a Python API, it is simply defining the computation. When the operation runs,
this computation is compiled to a backend, e.g. LLVM, OpenCL, CUDA. So there
isn't an overhead in inference time by using Python
Are you running this on GPU or CPU? The performance degradation is expected on
GPU as we need the heterogenous runtime support to avoid redundant memory copy
between CPU and GPU. @zhiics is currently working on this.
Besides, @jroesch is working on the memory planning for dynamic shape cases
Did you use the latest TVM master version? In latest version, we move to use
[Relay Op Strategy](https://docs.tvm.ai/dev/relay_op_strategy.html) to choose
which implementation to compile for each op. You need to add your
implementation in the strategy in order to be used during the
