The motivations of this RFC are extremely similar to those in [pytorch-tvm](https://github.com/pytorch/tvm), however the two implementations are very different and it is worth discussing the tradeoffs.
- torch-tvm is self contained, it doesn't use any special functions or classes in TVM. Instead it modifies torch script to use existing TVM functions. - torch-tvm uses relay to represent subgraphs and then dynamically builds functions rather than using prebuilt libraries as proposed here. I understand that the current implementation is the shortest path to getting tvm functions working in TensorFlow and that a torch-tvm approach would be a much larger undertaking. However, I don't think it will be able to scale well. The use of prebuilt libraries means there will be a lot of back and forth between regular tvm and tensorflow-tvm during development, and it seems like developers would be better off just importing their tf model to relay and doing everything within tvm. Contrast this to the torch-tvm approach where all the tvm magic happens transparently, making it very straight forward for pytorch users. We should also consider where the code belongs. I personally prefer having projects like torch-tvm and tf-tvm being separate from the main tvm repo if possible as it we already are dealing with frontend bloat. All that said, I think something like tf-tvm is a great idea and something we should work towards. I just want to make sure we make the first step carefully. -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/apache/incubator-tvm/issues/4464#issuecomment-562681167
