[Apache TVM Discuss] [Questions] Optimising pre-trained models with custom layer implementation

[Akshay via Apache TVM Discuss <no...@discuss.tvm.ai>]

Hi, I am completely new to TVM.

1.) One way of optimisation is to convert the whole pre trained model's graph in to an optimised one through TVM.([Like here on TVM tutorial ](https://tvm.apache.org/docs/how_to/compile_models/from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py)

2.) Another way of optimisation is to create your own layer and do custom optimisations ( [Like here](https://tvm.apache.org/docs/how_to/optimize_operators/opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py) ).

Question a.)  In this second layer case, in the tutorial I only see the speed test and not how to load pre trained weights.Which means if i create a custom layer in TVM (for which I already have the pre-trained weights in Pytorch) can I use the pre-trained weights from Pytorch in my custom layer in TVM? Or does that mean that if I create a convolution in TVM then I have to re-train the weights?

Question b.) Can I customise optimisation for some of the layers by hand and auto tune for others in a pre-trained Pytorch model optimisation in 1?

(Forgive me if the questions does not make sense, as I told, I am a complete newbie to TVM.)





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