Issues related to the input dimensions of the MLP model

[wjj19950828]

@merrymercy hi～

In your open source code, the input dimension of the MLP model is 164, which is aligned with Ansor, but in Appendix C of the Tenset paper, the input dimension is set to 324. Did you do anything?

Looking forward your reply~

[merrymercy]

In Appendix C of the paper, we mention that

Where the first 164 elements are from the orignal Ansor paper, the additional 324 - 164 = 160 elements are from the workload embedding. In our current open source code, we don't use LDP anymore. Instead, we use a simpler approach to get the workload embedding. The related code is https://github.com/tlc-pack/tenset/blob/62f0c20cc6e6b085e0c22bbfa2e241909af19a5d/python/tvm/auto_scheduler/cost_model/xgb_model.py#L79-L87 https://github.com/tlc-pack/tenset/blob/62f0c20cc6e6b085e0c22bbfa2e241909af19a5d/python/tvm/auto_scheduler/cost_model/mlp_model.py#L333

[wjj19950828]

In the paper, the effect of MLP+ranking loss is better than XGB+MSE, but in my experiment, the effect of MLP is not as good as XGB. Do you have any good suggestions for MLP?

[merrymercy]

What's your experiment setting? The results also depend on the dataset and hyperparameters.