Comments (3)
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
tenset/python/tvm/auto_scheduler/cost_model/xgb_model.py
Lines 79 to 87 in 62f0c20
from tenset.
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?
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What's your experiment setting? The results also depend on the dataset and hyperparameters.
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Related Issues (17)
- Get started with cost model experiments HOT 5
- Measurement Data Collection
- How do I get the collected measurement records ? HOT 4
- transfer-tune option tries only one-round for each task HOT 2
- When I collect measurement records meet some error HOT 1
- Meet Error collecting measurement records HOT 1
- Meet error running measure_program.py on TX2 HOT 1
- Get "Only support 64x64 image" error when dumping network infor of dcgan HOT 1
- nsight compute not able to profile the kernels
- train model with big dataset report Segmentation fault (core dumped) HOT 1
- When executing `python measure_programs.py "--target=cuda"`, I get some errors. HOT 3
- run python make_dataset.py get some error HOT 3
- transfer-tune using mlp throwing NoneType error
- About the dependency version of tenset HOT 1
- Is there a way to look at the generated programs? HOT 1
- Dump_network_info for bert_large leads to seg fault
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