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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2019/11/22 22:30:17 UTC
[GitHub] [incubator-tvm] zxy844288792 opened a new pull request #4404:
[AutoTVM] select model with the most tuned schedules
zxy844288792 opened a new pull request #4404: [AutoTVM] select model with the most tuned schedules
URL: https://github.com/apache/incubator-tvm/pull/4404
Recently I noticed that the performance for gpu drops significantly for conv2d with untuned workload. I did some investigation and some results are shown below:
test script: https://gist.github.com/zxy844288792/af05f12895ceaa2ba754ff22116b341d
After Commit [Bump up CUDA log version in tophub.py] 888a3c35cf53f6af6cb9bcca4ff3e917ea6fafca
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
fused_nn_conv2d fused_nn_conv2d 15119.7 100.0 (1, 32, 512, 512) 2 1
Total_time - 15119.7 - - - -
Before Commit [Bump up CUDA log version in tophub.py] 888a3c35cf53f6af6cb9bcca4ff3e917ea6fafca
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
fused_nn_conv2d fused_nn_conv2d 68.82 100.0 (1, 32, 512, 512) 2 1
The performance drops 200x than before. The commit [Bump up CUDA log version in tophub.py] only modifies the tuned schedule file by adding one more line of schedules for conv2d_transpose_nchw. In fact the root cause of this issue is that for a untuned workload, we are trying to find the most similar tuned one from the file to mimic its parameters. And the formal logic was trying to use the "model" of the last line of tuned schedule file as a reference. In this case, as we added one more line to the schedule file with "model" = v100, so it is trying to use all tuned schedules for v100 to mimic the parameters. However, we only have one tuned schedule for v100 so it does not help here.
The change I made is instead of using the last line as the reference, we fisrt will count the number of tuned schedules and if we need to mimic the parameters, it will choose the model with the most tuned schedules.
After applying the above logic, the performance goes back to normal level as shown below:
Node Name Ops Time(us) Time(%) Shape Inputs Outputs
--------- --- -------- ------- ----- ------ -------
fused_nn_conv2d fused_nn_conv2d 68.675 100.0 (1, 32, 512, 512) 2 1
@merrymercy @eqy
I want to hear some feedbacks if the change here is valid.
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