sync AveragedModel buffers when use_buffers=False #84054
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Hi sorry--since it's approved, feel free to comment with |
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/84054
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Just set up the job so I didn't forget. Sorry about that @RangiLyu and thanks for the PR |
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Hey @RangiLyu. |
Fixes pytorch#84053 As described in the issue, the AveragedModel will deep copy the model during initialization, which means that the buffers in the averaged model cannot be updated together with the model. One solution is to make the buffers equal to the source model every time when calling `update_parameters`. Pull Request resolved: pytorch#84054 Approved by: https://github.com/samdow
Fixes pytorch#84053 As described in the issue, the AveragedModel will deep copy the model during initialization, which means that the buffers in the averaged model cannot be updated together with the model. One solution is to make the buffers equal to the source model every time when calling `update_parameters`. Pull Request resolved: pytorch#84054 Approved by: https://github.com/samdow
Fixes pytorch#84053 As described in the issue, the AveragedModel will deep copy the model during initialization, which means that the buffers in the averaged model cannot be updated together with the model. One solution is to make the buffers equal to the source model every time when calling `update_parameters`. Pull Request resolved: pytorch#84054 Approved by: https://github.com/samdow
Fixes #84053
As described in the issue, the AveragedModel will deep copy the model during initialization, which means that the buffers in the averaged model cannot be updated together with the model.
One solution is to make the buffers equal to the source model every time when calling
update_parameters.